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ardupilot/libraries/AP_GyroFFT/AP_GyroFFT.cpp

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/*
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Code by Andy Piper with help from betaflight
*/
#include "AP_GyroFFT.h"
#if HAL_GYROFFT_ENABLED
#include <GCS_MAVLink/GCS.h>
#include <AP_Logger/AP_Logger.h>
#include <Filter/HarmonicNotchFilter.h>
#include <AP_BoardConfig/AP_BoardConfig.h>
#include <AP_Arming/AP_Arming.h>
#include <AP_Vehicle/AP_Vehicle.h>
#if APM_BUILD_COPTER_OR_HELI || APM_BUILD_TYPE(APM_BUILD_ArduPlane)
#include <AP_Motors/AP_Motors.h>
#endif
#include <stdio.h>
extern const AP_HAL::HAL& hal;
#ifndef FFT_DEFAULT_WINDOW_SIZE
// the H7 can cope with a longer length and these boards generally have BMI088 which needs a longer length
#if defined(STM32H7)
#define FFT_DEFAULT_WINDOW_SIZE 64
#else
#define FFT_DEFAULT_WINDOW_SIZE 32
#endif
#endif
#ifndef FFT_DEFAULT_WINDOW_OVERLAP
#if defined(STM32H7)
#define FFT_DEFAULT_WINDOW_OVERLAP 0.75f
#else
#define FFT_DEFAULT_WINDOW_OVERLAP 0.5f
#endif
#endif
#define FFT_THR_REF_DEFAULT 0.35f // the estimated throttle reference, 0 ~ 1
#define FFT_SNR_DEFAULT 25.0f // a higher SNR is safer and this works quite well on a Pixracer
#define FFT_SNR_PFILT_DEFAULT 10.0f // post-filter there is much less noise so default should be lower
#define FFT_STACK_SIZE 1024
#define FFT_MIN_SAMPLES_PER_FRAME 16
#define FFT_HARMONIC_FIT_DEFAULT 10
#define FFT_HARMONIC_FIT_FILTER_HZ 15.0f
#define FFT_HARMONIC_FIT_MULT 50.0f
#define FFT_HARMONIC_FIT_TRACK_ROLL 4
#define FFT_HARMONIC_FIT_TRACK_PITCH 5
// table of user settable parameters
const AP_Param::GroupInfo AP_GyroFFT::var_info[] = {
// @Param: ENABLE
// @DisplayName: Enable
// @Description: Enable Gyro FFT analyser
// @Values: 0:Disabled,1:Enabled
// @User: Advanced
// @RebootRequired: True
AP_GROUPINFO_FLAGS("ENABLE", 1, AP_GyroFFT, _enable, 0, AP_PARAM_FLAG_ENABLE),
// @Param: MINHZ
// @DisplayName: Minimum Frequency
// @Description: Lower bound of FFT frequency detection in Hz. On larger vehicles the minimum motor frequency is likely to be significantly lower than for smaller vehicles.
// @Range: 20 400
// @Units: Hz
// @User: Advanced
AP_GROUPINFO("MINHZ", 2, AP_GyroFFT, _fft_min_hz, 50),
// @Param: MAXHZ
// @DisplayName: Maximum Frequency
// @Description: Upper bound of FFT frequency detection in Hz. On smaller vehicles the maximum motor frequency is likely to be significantly higher than for larger vehicles.
// @Range: 20 495
// @Units: Hz
// @User: Advanced
AP_GROUPINFO("MAXHZ", 3, AP_GyroFFT, _fft_max_hz, 450),
// @Param: SAMPLE_MODE
// @DisplayName: Sample Mode
// @Description: Sampling mode (and therefore rate). 0: Gyro rate sampling, 1: Fast loop rate sampling, 2: Fast loop rate / 2 sampling, 3: Fast loop rate / 3 sampling. Takes effect on reboot.
// @Range: 0 4
// @User: Advanced
// @RebootRequired: True
AP_GROUPINFO("SAMPLE_MODE", 4, AP_GyroFFT, _sample_mode, 0),
// @Param: WINDOW_SIZE
// @DisplayName: FFT window size
// @Description: Size of window to be used in FFT calculations. Takes effect on reboot. Must be a power of 2 and between 32 and 512. Larger windows give greater frequency resolution but poorer time resolution, consume more CPU time and may not be appropriate for all vehicles. Time and frequency resolution are given by the sample-rate / window-size. Windows of 256 are only really recommended for F7 class boards, windows of 512 or more H7 class.
// @Range: 32 1024
// @User: Advanced
// @RebootRequired: True
AP_GROUPINFO("WINDOW_SIZE", 5, AP_GyroFFT, _window_size, FFT_DEFAULT_WINDOW_SIZE),
// @Param: WINDOW_OLAP
// @DisplayName: FFT window overlap
// @Description: Percentage of window to be overlapped before another frame is process. Takes effect on reboot. A good default is 50% overlap. Higher overlap results in more processed frames but not necessarily more temporal resolution. Lower overlap results in lost information at the frame edges.
// @Range: 0 0.9
// @User: Advanced
// @RebootRequired: True
AP_GROUPINFO("WINDOW_OLAP", 6, AP_GyroFFT, _window_overlap, FFT_DEFAULT_WINDOW_OVERLAP),
// @Param: FREQ_HOVER
// @DisplayName: FFT learned hover frequency
// @Description: The learned hover noise frequency
// @Range: 0 250
// @User: Advanced
AP_GROUPINFO("FREQ_HOVER", 7, AP_GyroFFT, _freq_hover_hz, 80.0f),
// @Param: THR_REF
// @DisplayName: FFT learned thrust reference
// @Description: FFT learned thrust reference for the hover frequency and FFT minimum frequency.
// @Range: 0.01 0.9
// @User: Advanced
AP_GROUPINFO("THR_REF", 8, AP_GyroFFT, _throttle_ref, FFT_THR_REF_DEFAULT),
// @Param: SNR_REF
// @DisplayName: FFT SNR reference threshold
// @Description: FFT SNR reference threshold in dB at which a signal is determined to be present.
// @Range: 0.0 100.0
// @User: Advanced
AP_GROUPINFO("SNR_REF", 9, AP_GyroFFT, _snr_threshold_db, FFT_SNR_DEFAULT),
// @Param: ATT_REF
// @DisplayName: FFT attenuation for bandwidth calculation
// @Description: FFT attenuation level in dB for bandwidth calculation and peak detection. The bandwidth is calculated by comparing peak power output with the attenuated version. The default of 15 has shown to be a good compromise in both simulations and real flight.
// @Range: 0 100
// @User: Advanced
AP_GROUPINFO("ATT_REF", 10, AP_GyroFFT, _attenuation_power_db, 15),
// @Param: BW_HOVER
// @DisplayName: FFT learned bandwidth at hover
// @Description: FFT learned bandwidth at hover for the attenuation frequencies.
// @Range: 0 200
// @User: Advanced
AP_GROUPINFO("BW_HOVER", 11, AP_GyroFFT, _bandwidth_hover_hz, 20),
// @Param: HMNC_FIT
// @DisplayName: FFT harmonic fit frequency threshold
// @Description: FFT harmonic fit frequency threshold percentage at which a signal of the appropriate frequency is determined to be the harmonic of another. Signals that have a harmonic relationship that varies at most by this percentage are considered harmonics of each other for the purpose of selecting the harmonic notch frequency. If a match is found then the lower frequency harmonic is always used as the basis for the dynamic harmonic notch. A value of zero completely disables harmonic matching.
// @Range: 0 100
// @User: Advanced
// @RebootRequired: True
AP_GROUPINFO("HMNC_FIT", 12, AP_GyroFFT, _harmonic_fit, FFT_HARMONIC_FIT_DEFAULT),
// @Param: HMNC_PEAK
// @DisplayName: FFT harmonic peak target
// @Description: The FFT harmonic peak target that should be returned by FTN1.PkAvg. The resulting value will be used by the harmonic notch if configured to track the FFT frequency. By default the appropriate peak is auto-detected based on the harmonic fit between peaks and the energy-weighted average frequency on roll on pitch is used. Setting this to 1 will always target the highest energy peak. Setting this to 2 will target the highest energy peak that is lower in frequency than the highest energy peak. Setting this to 3 will target the highest energy peak that is higher in frequency than the highest energy peak. Setting this to 4 will target the highest energy peak on the roll axis only and only the roll frequency will be used (some vehicles have a much more pronounced peak on roll). Setting this to 5 will target the highest energy peak on the pitch axis only and only the pitch frequency will be used (some vehicles have a much more pronounced peak on roll).
// @Values: 0:Auto,1:Center Frequency,2:Lower-Shoulder Frequency,3:Upper-Shoulder Frequency,4:Roll-Axis,5:Pitch-Axis
// @User: Advanced
AP_GROUPINFO("HMNC_PEAK", 13, AP_GyroFFT, _harmonic_peak, 0),
// @Param: NUM_FRAMES
// @DisplayName: FFT output frames to retain and average
// @Description: Number of output frequency frames to retain and average in order to calculate final frequencies. Averaging output frames can drastically reduce noise and jitter at the cost of latency as long as the input is stable. The default is to perform no averaging. For rapidly changing frequencies (e.g. smaller aircraft) fewer frames should be averaged.
// @Range: 0 8
// @User: Advanced
// @RebootRequired: True
AP_GROUPINFO("NUM_FRAMES", 14, AP_GyroFFT, _num_frames, 0),
// @Param: OPTIONS
// @DisplayName: FFT options
// @Description: FFT configuration options. Values: 1:Apply the FFT *after* the filter bank,2:Check noise at the motor frequencies using ESC data as a reference
// @Bitmask: 0:Enable post-filter FFT,1:Check motor noise
// @User: Advanced
// @RebootRequired: True
AP_GROUPINFO("OPTIONS", 15, AP_GyroFFT, _options, 0),
AP_GROUPEND
};
// The FFT splits the frequency domain into an number of bins
// A sampling frequency of 1000 and max frequency (Nyquist) of 500 at a window size of 32 gives 16 frequency bins each 31.25Hz wide
// The first bin is used to store the DC and Nyquist values combined.
// Eg [DC/Nyquist], [16,47), [47,78), [78,109) etc
// For a loop rate of 800Hz, 16 bins each 25Hz wide
// Eg X[0]=[DC/Nyquist], X[1]=[12,37), X[2]=[37,62), X[3]=[62,87), X[4]=[87,112)
// So middle frequency is X[n] * 25 and the range is X[n] * 25 - 12 < f < X[n] * 25 + 12
const extern AP_HAL::HAL& hal;
AP_GyroFFT::AP_GyroFFT()
{
_thread_state._noise_needs_calibration = 0x07; // all axes need calibration
AP_Param::setup_object_defaults(this, var_info);
if (_singleton != nullptr) {
AP_HAL::panic("AP_GyroFFT must be singleton");
}
_singleton = this;
}
// initialize the FFT parameters and engine
void AP_GyroFFT::init(uint16_t loop_rate_hz)
{
// if FFT analysis is not enabled we don't want to allocate any of the associated resources
if (!_enable) {
return;
}
_ins = &AP::ins();
// sanity check
if (_ins->get_raw_gyro_rate_hz() == 0) {
AP_HAL::panic("AP_GyroFFT must be initialized after AP_InertialSensor");
}
// check that we support the window size requested and it is a power of 2
_window_size.set(1 << lrintf(log2f(_window_size.get())));
#if defined(STM32H7) || CONFIG_HAL_BOARD == HAL_BOARD_LINUX || CONFIG_HAL_BOARD == HAL_BOARD_SITL
_window_size.set(constrain_int16(_window_size, 32, 512));
#else
_window_size.set(constrain_int16(_window_size, 32, 256));
#endif
// number of samples needed before a new frame can be processed
_window_overlap.set(constrain_float(_window_overlap, 0.0f, 0.9f));
_samples_per_frame = (1.0f - _window_overlap) * _window_size;
// if we allow too small a number of samples per frame the output rate gets very high
// this is particularly a problem on IMUs with higher sample rates (e.g. BMI088)
// 16 gives a maximum output rate of 2Khz / 16 = 125Hz per axis or 375Hz in aggregate
_samples_per_frame = MAX(FFT_MIN_SAMPLES_PER_FRAME, 1 << lrintf(log2f(_samples_per_frame)));
if (_num_frames > 0) {
_num_frames.set(constrain_int16(_num_frames, 2, AP_HAL::DSP::MAX_SLIDING_WINDOW_SIZE));
}
// check that we have enough memory for the window size requested
// INS: XYZ_AXIS_COUNT * INS_MAX_INSTANCES * _window_size, DSP: 3 * _window_size, FFT: XYZ_AXIS_COUNT + 3 * _window_size
const uint32_t allocation_count = (XYZ_AXIS_COUNT * INS_MAX_INSTANCES + 3 + XYZ_AXIS_COUNT + 3 + _num_frames) * sizeof(float);
if (allocation_count * FFT_DEFAULT_WINDOW_SIZE > hal.util->available_memory() / 2) {
GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "AP_GyroFFT: disabled, required %u bytes", (unsigned int)allocation_count * FFT_DEFAULT_WINDOW_SIZE);
return;
} else if (allocation_count * _window_size > hal.util->available_memory() / 2) {
GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "AP_GyroFFT: req alloc %u bytes (free=%u)", (unsigned int)allocation_count * _window_size, (unsigned int)hal.util->available_memory());
_window_size.set(FFT_DEFAULT_WINDOW_SIZE);
}
// save any changes that were made
_window_size.save();
// determine the FFT sample rate based on the gyro rate, loop rate and configuration
if (_sample_mode == 0) {
_fft_sampling_rate_hz = _ins->get_raw_gyro_rate_hz();
} else {
_fft_sampling_rate_hz = loop_rate_hz / _sample_mode;
for (uint8_t axis = 0; axis < XYZ_AXIS_COUNT; axis++) {
if (!_downsampled_gyro_data[axis].set_size(_window_size + _samples_per_frame)) {
GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "Failed to allocate window for AP_GyroFFT");
return;
}
}
}
_current_sample_mode = _sample_mode;
_ref_energy = new Vector3f[_window_size];
if (_ref_energy == nullptr) {
GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "Failed to allocate window for AP_GyroFFT");
return;
}
// make the gyro window match the window size plus a buffer to cope with the backend
// getting too far ahead.
if (!_ins->set_gyro_window_size(_window_size + _samples_per_frame)) {
return;
}
#if AP_INERTIALSENSOR_HARMONICNOTCH_ENABLED
// check for harmonics across all harmonic notch filters
// note that we only allow one harmonic notch filter linked to the FFT code
uint32_t harmonics = 0;
uint8_t num_notches = 0;
for (auto &notch : _ins->harmonic_notches) {
if (notch.params.enabled()) {
harmonics |= notch.params.harmonics();
num_notches = MAX(num_notches, notch.num_dynamic_notches);
}
}
if (harmonics == 0) {
// this allows use of FFT to find peaks with all notch filters disabled
harmonics = 3;
}
// count the number of active harmonics or dynamic notchs
_tracked_peaks = constrain_int16(MAX(__builtin_popcount(harmonics),
num_notches), 1, FrequencyPeak::MAX_TRACKED_PEAKS);
// calculate harmonic multiplier. this assumes the harmonics configured on the
// harmonic notch reflect the multiples of the fundamental harmonic that should be tracked
if (_harmonic_fit > 0) {
uint8_t first_harmonic = 0;
for (uint8_t i = 0; i < HNF_MAX_HARMONICS; i++) {
if (harmonics & (1<<i)) {
if (first_harmonic == 0) {
first_harmonic = i + 1;
} else {
_harmonic_multiplier = float(i + 1) / first_harmonic;
break;
}
}
}
// if no harmonic specified then select a simple 2x multiple
if (is_zero(_harmonic_multiplier)) {
_harmonic_multiplier = 2.0f;
}
}
#endif // AP_INERTIALSENSOR_HARMONICNOTCH_ENABLED
// initialise the HAL DSP subsystem
_state = hal.dsp->fft_init(_window_size, _fft_sampling_rate_hz, _num_frames);
if (_state == nullptr) {
GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "Failed to initialize DSP engine");
return;
}
// per-axis frame time
_frame_time_ms = _samples_per_frame * 1000 / _fft_sampling_rate_hz;
// The update rate for the output, defaults are 1Khz / (1 - 0.5) * 32 == 62hz
const float output_rate = static_cast<float>(_fft_sampling_rate_hz) / static_cast<float>(_samples_per_frame);
// establish suitable defaults for the detected values
for (uint8_t axis = 0; axis < XYZ_AXIS_COUNT; axis++) {
_thread_state._center_freq_hz[axis] = _fft_min_hz;
for (uint8_t peak = 0; peak < FrequencyPeak::MAX_TRACKED_PEAKS; peak++) {
_thread_state._center_freq_hz_filtered[axis][peak] = _fft_min_hz;
}
// number of cycles to average over, two complete windows to be sure
_noise_calibration_cycles[axis] = (_window_size / _samples_per_frame) * 2;
// harmonic frequency fit should change relatively slowly
_harmonic_fit_filter[axis].set_cutoff_frequency(output_rate, MIN(output_rate * 0.48f, FFT_HARMONIC_FIT_FILTER_HZ));
}
// configure a filter for frequency, bandwidth and energy for each of the three tracked noise peaks
// filter more aggressively post-filter since the noise is harder to detect
const float scale_factor = using_post_filter_samples() ? 0.1f : 1.0f;
for (uint8_t peak = 0; peak < FrequencyPeak::MAX_TRACKED_PEAKS; peak++) {
// calculate low-pass filter characteristics based on window size and overlap
_center_freq_filter[peak].set_cutoff_frequency(output_rate, output_rate * 0.48f * scale_factor);
// the bin energy jumps around a lot so requires more filtering
_center_freq_energy_filter[peak].set_cutoff_frequency(output_rate, output_rate * 0.25f * scale_factor);
// smooth the bandwidth output more aggressively
_center_bandwidth_filter[peak].set_cutoff_frequency(output_rate, output_rate * 0.25f * scale_factor);
}
// turn down the SNR threshold if examining post-filter
if (using_post_filter_samples()) {
_snr_threshold_db.set_default(FFT_SNR_PFILT_DEFAULT);
}
// the number of cycles required to have a proper noise reference
_noise_cycles = (_window_size / _samples_per_frame) * XYZ_AXIS_COUNT;
// finally we are done
_initialized = true;
update_parameters(true);
// start running FFTs
if (start_update_thread()) {
set_analysis_enabled(true);
}
}
// sample the gyros either by using a gyro window sampled at the gyro rate or making individual samples
// called from fast_loop thread - this function does not take out a semaphore to avoid waiting on the FFT thread
void AP_GyroFFT::sample_gyros()
{
if (!analysis_enabled()) {
return;
}
// update counters for gyro window
if (_current_sample_mode > 0) {
// for loop rate sampling accumulate and average gyro samples
_oversampled_gyro_accum += _ins->get_gyro_for_fft();
_oversampled_gyro_count++;
if ((_oversampled_gyro_count % _current_sample_mode) == 0) {
// calculate mean value of accumulated samples
Vector3f sample = _oversampled_gyro_accum / _current_sample_mode;
// fast sampling means that the raw gyro values have already been averaged over 8 samples
_downsampled_gyro_data[0].push(sample.x);
_downsampled_gyro_data[1].push(sample.y);
_downsampled_gyro_data[2].push(sample.z);
_oversampled_gyro_accum.zero();
_oversampled_gyro_count = 0;
}
}
}
// update the state as as required
// called from main thread at 400Hz - anything that requires atomic access to IMU data needs to be done here
void AP_GyroFFT::update()
{
if (!analysis_enabled()) {
return;
}
WITH_SEMAPHORE(_sem);
_config._analysis_enabled = _analysis_enabled;
_global_state = _thread_state;
// calculate health based on being 5 frames behind, SITL needs longer
#if CONFIG_HAL_BOARD == HAL_BOARD_SITL
const uint32_t output_delay = _frame_time_ms * FFT_MAX_MISSED_UPDATES * 2;
#else
const uint32_t output_delay = _frame_time_ms * FFT_MAX_MISSED_UPDATES;
#endif
uint32_t now = AP_HAL::millis();
_rpy_health.x = (now - _global_state._health_ms.x <= output_delay);
_rpy_health.y = (now - _global_state._health_ms.y <= output_delay);
_rpy_health.z = (now - _global_state._health_ms.z <= output_delay);
_health = _global_state._health;
if (!_rpy_health.x) {
_health.x = 0;
}
if (!_rpy_health.y) {
_health.y = 0;
}
if (!_rpy_health.z) {
_health.z = 0;
}
}
// analyse gyro data using FFT, returns number of samples still held
// called from FFT thread
uint16_t AP_GyroFFT::run_cycle()
{
if (!analysis_enabled()) {
return 0;
}
if (!_sem.take(HAL_SEMAPHORE_BLOCK_FOREVER)) {
return 0;
}
// do we have enough samples for another pass?
if (!start_analysis()) {
uint16_t new_sample_count = get_available_samples(_update_axis);
_sem.give();
return new_sample_count;
}
// take a copy of the config inside the semaphore
EngineConfig config = _config;
_sem.give();
uint32_t now = AP_HAL::micros();
// get the appropriate gyro buffer
FloatBuffer& gyro_buffer = (_sample_mode == 0 ?_ins->get_raw_gyro_window(_update_axis) : _downsampled_gyro_data[_update_axis]);
// if we have many more samples than the window size then we are struggling to
// stay ahead of the gyro loop so drop samples so that this cycle will use all available samples
if (gyro_buffer.available() > uint32_t(_state->_window_size + uint16_t(_samples_per_frame >> 1))) { // half the frame size is a heuristic
gyro_buffer.advance(gyro_buffer.available() - _state->_window_size);
}
// let's go!
hal.dsp->fft_start(_state, gyro_buffer, _samples_per_frame);
// calculate FFT and update filters outside the semaphore
uint16_t bin_max = hal.dsp->fft_analyse(_state, config._fft_start_bin, config._fft_end_bin, config._attenuation_cutoff);
// something has been detected, update the peak frequency and associated metrics
update_ref_energy(bin_max);
calculate_noise(false, config);
// record how we are doing
_thread_state._last_output_us[_update_axis] = AP_HAL::micros();
_output_cycle_micros = _thread_state._last_output_us[_update_axis] - now;
#if AP_SIM_ENABLED && HAL_LOGGING_ENABLED
// extra logging when running simulations
AP::logger().WriteStreaming(
"FTN3",
"TimeUS,Id,Pk1,Pk2,Pk3,Bw1,Bw2,Bw3,En1,En2,En3",
"s#zzzzzz---",
"F----------",
"QBfffffffff",
AP_HAL::micros64(),
_update_axis,
_state->_peak_data[0]._freq_hz,
_state->_peak_data[1]._freq_hz,
_state->_peak_data[2]._freq_hz,
_state->_peak_data[0]._noise_width_hz,
_state->_peak_data[1]._noise_width_hz,
_state->_peak_data[2]._noise_width_hz,
_state->_freq_bins[_state->_peak_data[0]._bin],
_state->_freq_bins[_state->_peak_data[1]._bin],
_state->_freq_bins[_state->_peak_data[2]._bin]);
#endif
// move onto the next axis
_update_axis = (_update_axis + 1) % XYZ_AXIS_COUNT;
// ready to receive another frame, because lock contention is so expensive we don't lock
// around this flag but rather rely on the semaphore at the beginning of the loop to
// ensure eventual visibility to the main loop
_thread_state._analysis_started = false;
// samples remaining in the next axis
return get_available_samples(_update_axis);
}
// whether analysis can be run again or not
// called from FFT thread with the semaphore held
bool AP_GyroFFT::start_analysis() {
if (_thread_state._analysis_started) {
return false;
}
// don't run any more gyro cycles once noise is calibrated and the self-test is running
if (!_thread_state._noise_needs_calibration && !_calibrated) {
return false;
}
if (get_available_samples(_update_axis) >= _state->_window_size) {
_thread_state._analysis_started = true;
return true;
}
return false;
}
// update calculated values of dynamic parameters - runs at 1Hz
void AP_GyroFFT::update_parameters(bool force)
{
// lock contention is very costly, so don't allow configuration updates while flying
if ((!_initialized || AP::arming().is_armed()) && !force) {
return;
}
WITH_SEMAPHORE(_sem);
// don't allow MAXHZ to go to Nyquist
_fft_max_hz.set(MIN(_fft_max_hz, _fft_sampling_rate_hz * 0.48));
_config._snr_threshold_db = _snr_threshold_db;
_config._fft_min_hz = _fft_min_hz;
_config._fft_max_hz = _fft_max_hz;
// determine the start FFT bin for all frequency detection
_config._fft_start_bin = MAX(floorf(_fft_min_hz.get() / _state->_bin_resolution), 1);
// determine the endt FFT bin for all frequency detection
_config._fft_end_bin = MIN(ceilf(_fft_max_hz.get() / _state->_bin_resolution), _state->_bin_count);
// actual attenuation from the db value
_config._attenuation_cutoff = powf(10.0f, -_attenuation_power_db * 0.1f);
}
// thread for processing gyro data via FFT
void AP_GyroFFT::update_thread(void)
{
while (true) {
uint16_t remaining_samples = run_cycle();
// this is to stop us burning CPU while waiting for samples, the reduction by _samples_per_frame is a heuristic to prevent waiting too long
// and missing frames (easy to see in SITL because the noise will keep calibrating)
// we always delay by at least 1us to give logging a chance to run at the same priority
uint32_t delay = constrain_int32((int16_t)_state->_window_size - (int16_t)remaining_samples, 0, _samples_per_frame)
* 1e6 / _fft_sampling_rate_hz;
#if CONFIG_HAL_BOARD == HAL_BOARD_SITL
// in SITL the gyros do not run in a different thread
if (delay > 0) {
hal.scheduler->delay_microseconds(delay);
}
#else
hal.scheduler->delay_microseconds(MAX(delay, 1U));
#endif
}
}
// start the update thread
bool AP_GyroFFT::start_update_thread(void)
{
WITH_SEMAPHORE(_sem);
if (_thread_created) {
return true;
}
if (!hal.scheduler->thread_create(FUNCTOR_BIND_MEMBER(&AP_GyroFFT::update_thread, void), "apm_fft", FFT_STACK_SIZE, AP_HAL::Scheduler::PRIORITY_IO, 0)) {
AP_HAL::panic("Failed to start AP_GyroFFT update thread");
return false;
}
_thread_created = true;
return true;
}
// self-test the FFT analyser - can only be done while samples are not being taken
// called from main thread
bool AP_GyroFFT::pre_arm_check(char *failure_msg, const uint8_t failure_msg_len)
{
if (!analysis_enabled()) {
return true;
}
// already calibrated
if (_calibrated) {
return true;
}
// analysis is started in the main thread, don't trample on in-flight analysis
if (_global_state._analysis_started) {
hal.util->snprintf(failure_msg, failure_msg_len, "FFT still analyzing");
return false;
}
// still calibrating noise so not ready
if (_global_state._noise_needs_calibration) {
hal.util->snprintf(failure_msg, failure_msg_len, "FFT calibrating noise");
return false;
}
// make sure the frequency maximum is below Nyquist
if (_fft_max_hz > _fft_sampling_rate_hz * 0.5f) {
hal.util->snprintf(failure_msg, failure_msg_len, "FFT config MAXHZ %dHz > %dHz", _fft_max_hz.get(), _fft_sampling_rate_hz / 2);
return false;
}
// check for sane frequency resolution - for 1k backends with length 32 this will be 32Hz
if (_state->_bin_resolution > 50.0f) {
GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "FFT: resolution is %.1fHz, increase length", _state->_bin_resolution);
return true; // a low resolution is not fatal
}
#if 0 // these calculations do not result in a long enough expected delay
// in order to test all frequencies we need to endure a long pause for higher windows lengths
// see timings AP_HAL_ChibiOS/DSP.cpp for per step timings on different hardware
#if defined(STM32H7)
const uint32_t cycle_time = (16 * (1 << (_window_size / 32)) + 5) * (_config._fft_end_bin - _config._fft_start_bin + 1) * 2 / 1000; // H7
#else
const uint32_t cycle_time = (29 * (1 << (_window_size / 32)) + 5) * (_config._fft_end_bin - _config._fft_start_bin + 1) * 2 / 1000; // F4
#endif
#endif
EXPECT_DELAY_MS(2000); // tested on an H7 at 1024 window
float max_divergence = self_test_bin_frequencies();
// for longer FFT lengths the resolution gets below 1Hz
if (max_divergence > MAX(_state->_bin_resolution * 0.5f, 1)) {
hal.util->snprintf(failure_msg, failure_msg_len, "FFT self-test failed, max error %fHz", max_divergence);
}
_calibrated = max_divergence <= MAX(_state->_bin_resolution * 0.5f, 1);
if (_calibrated) {
// provide the user with some useful information about what they have configured
GCS_SEND_TEXT(MAV_SEVERITY_INFO, "FFT: calibrated %.1fKHz/%.1fHz/%.1fHz", _fft_sampling_rate_hz * 0.001f,
_state->_bin_resolution * 0.5, 1000.0f * XYZ_AXIS_COUNT / _frame_time_ms);
}
return _calibrated;
}
// we may have disabled the FFT arming check, in which case make sure the engine can still run
bool AP_GyroFFT::prepare_for_arming()
{
_calibrated = true;
return true;
}
// update the hover frequency input filter. should be called at 100hz when in a stable hover
// called from main thread
void AP_GyroFFT::update_freq_hover(float dt, float throttle_out)
{
if (!analysis_enabled()) {
return;
}
// throttle averaging for average fft calculation
if (is_zero(_avg_throttle_out)) {
_avg_throttle_out = throttle_out;
} else {
_avg_throttle_out = constrain_float(_avg_throttle_out + (dt / (10.0f + dt)) * (throttle_out - _avg_throttle_out), 0.01f, 0.9f);
}
// we have chosen to constrain the hover frequency to be within the range reachable by the third order expo polynomial.
_freq_hover_hz.set(constrain_float(_freq_hover_hz + (dt / (10.0f + dt)) * (get_weighted_noise_center_freq_hz() - _freq_hover_hz), _fft_min_hz, _fft_max_hz));
_bandwidth_hover_hz.set(constrain_float(_bandwidth_hover_hz + (dt / (10.0f + dt)) * (get_weighted_noise_center_bandwidth_hz() - _bandwidth_hover_hz), 0, _fft_max_hz * 0.5f));
_throttle_ref.set(constrain_float(_throttle_ref + (dt / (10.0f + dt)) * (throttle_out * sq((float)_fft_min_hz.get() / _freq_hover_hz.get()) - _throttle_ref), 0.01f, 0.9f));
}
// save parameters as part of disarming
// called from main thread
void AP_GyroFFT::save_params_on_disarm()
{
if (!analysis_enabled()) {
return;
}
_freq_hover_hz.save();
_bandwidth_hover_hz.save();
_throttle_ref.save();
}
// notch tuning
void AP_GyroFFT::start_notch_tune()
{
if (!analysis_enabled()) {
return;
}
if (!hal.dsp->fft_start_average(_state)) {
GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "FFT: Unable to start FFT averaging");
}
// throttle averaging for average fft calculation
_avg_throttle_out = 0.0f;
#if APM_BUILD_COPTER_OR_HELI || APM_BUILD_TYPE(APM_BUILD_ArduPlane)
AP_Motors* motors = AP::motors();
if (motors != nullptr) {
_avg_throttle_out = motors->get_throttle_hover();
}
#endif
}
// calculate the frequency to be used for the harmonic notch
float AP_GyroFFT::calculate_notch_frequency(float* freqs, uint16_t numpeaks, float harmonic_fit, uint8_t& harmonics)
{
float harmonic = freqs[0];
harmonics = 1;
for (uint8_t i = 1; i < numpeaks; i++) {
if (freqs[i] < harmonic) {
for (uint8_t x = 2; x <=HNF_MAX_HARMONICS; x++) {
if (is_harmonic_of(harmonic, freqs[i], x, harmonic_fit)) {
harmonic = freqs[i];
}
}
}
}
// select the harmonics that were matched
for (uint8_t i = 0; i < numpeaks; i++) {
for (uint8_t x = 1; x <=HNF_MAX_HARMONICS; x++) {
if (is_harmonic_of(freqs[i], harmonic, x, harmonic_fit)) {
harmonics |= 1<<(x - 1);
}
}
}
return harmonic;
}
void AP_GyroFFT::stop_notch_tune()
{
if (!analysis_enabled()) {
return;
}
float freqs[FrequencyPeak::MAX_TRACKED_PEAKS] {};
uint16_t numpeaks = hal.dsp->fft_stop_average(_state, _config._fft_start_bin, _config._fft_end_bin, freqs);
if (numpeaks == 0) {
return;
}
uint8_t harmonics;
float harmonic = calculate_notch_frequency(freqs, numpeaks, _harmonic_fit, harmonics);
GCS_SEND_TEXT(MAV_SEVERITY_INFO, "FFT: Found peaks at %.1f/%.1f/%.1fHz", freqs[0], freqs[1], freqs[2]);
GCS_SEND_TEXT(MAV_SEVERITY_NOTICE, "FFT: Selected %.1fHz\n", harmonic);
// if we don't have a throttle value then all bets are off
if (is_zero(_avg_throttle_out) || is_zero(harmonic)) {
GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "FFT: Unable to calculate notch: need stable hover");
AP_Notify::events.autotune_failed = true;
return;
}
#if AP_INERTIALSENSOR_HARMONICNOTCH_ENABLED
if (!_ins->setup_throttle_gyro_harmonic_notch(harmonic, (float)_fft_min_hz.get(), _avg_throttle_out, harmonics)) {
GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "FFT: Unable to set throttle notch with %.1fHz/%.2f",
harmonic, _avg_throttle_out);
AP_Notify::events.autotune_failed = true;
// save results to FFT slots
_throttle_ref.set(_avg_throttle_out);
_freq_hover_hz.set(harmonic);
} else {
GCS_SEND_TEXT(MAV_SEVERITY_NOTICE, "FFT: Notch frequency %.1fHz and ref %.2f selected", harmonic, _avg_throttle_out);
AP_Notify::events.autotune_complete = true;
}
#endif // AP_INERTIALSENSOR_HARMONICNOTCH_ENABLED
}
// return the noise peak that is being tracked
// called from main thread
AP_GyroFFT::FrequencyPeak AP_GyroFFT::get_tracked_noise_peak() const
{
// if the user has specified a specific axis to track then use that
if (_harmonic_peak > FrequencyPeak::MAX_TRACKED_PEAKS) {
switch (_harmonic_peak) {
case FFT_HARMONIC_FIT_TRACK_ROLL:
if (_global_state._harmonic_fit.x < _harmonic_fit) {
return FrequencyPeak(_global_state._tracked_peak.x);
}
break;
case FFT_HARMONIC_FIT_TRACK_PITCH:
if (_global_state._harmonic_fit.y < _harmonic_fit) {
return FrequencyPeak(_global_state._tracked_peak.y);
}
break;
default:
break;
}
return FrequencyPeak::CENTER;
}
// if the user has specified a specific peak to track then use that
if (_harmonic_peak > 0) {
return FrequencyPeak(constrain_int16(_harmonic_peak - 1, FrequencyPeak::CENTER, FrequencyPeak::UPPER_SHOULDER));
}
// required fit of 10% is fairly conservative when testing in SITL, testing shows that it's safer to
// require both tracked axes to fit - biasing towards the highest energy peak
if (_global_state._harmonic_fit.x < _harmonic_fit && _global_state._harmonic_fit.y < _harmonic_fit) {
return FrequencyPeak(_global_state._tracked_peak.x);
}
return FrequencyPeak::CENTER;
}
// weighted center frequency
float AP_GyroFFT::get_weighted_freq_hz(FrequencyPeak peak) const
{
const Vector3f& energy = get_center_freq_energy(peak);
const Vector3f& freq = get_noise_center_freq_hz(peak);
if (!energy.is_nan() && !is_zero(energy.x) && !is_zero(energy.y)) {
return (freq.x * energy.x + freq.y * energy.y) / (energy.x + energy.y);
} else {
return (freq.x + freq.y) * 0.5f;
}
}
// return an average center frequency weighted by bin energy
// called from main thread
float AP_GyroFFT::get_weighted_noise_center_freq_hz() const
{
if (!analysis_enabled()) {
return _fft_min_hz;
}
if (_health.is_zero()) {
#if APM_BUILD_COPTER_OR_HELI || APM_BUILD_TYPE(APM_BUILD_ArduPlane)
// if we are post-filter sampling then throttle estimate will be useless
if (using_post_filter_samples()) {
return 0.0f;
}
AP_Motors* motors = AP::motors();
if (motors != nullptr && !is_zero(_throttle_ref)) {
// FFT is not healthy, fallback to FFT's throttle-based estimate
return constrain_float(_fft_min_hz * MAX(1.0f, sqrtf(motors->get_throttle_out() / _throttle_ref)), _fft_min_hz, _fft_max_hz);
}
#endif
}
const FrequencyPeak peak = get_tracked_noise_peak();
// pitch was good or required, roll was not, use pitch only
if (!_health.x || _harmonic_peak == FFT_HARMONIC_FIT_TRACK_PITCH) {
return get_noise_center_freq_hz(peak).y; // Y-axis
}
// roll was good or required, pitch was not, use roll only
if (!_health.y || _harmonic_peak == FFT_HARMONIC_FIT_TRACK_ROLL) {
return get_noise_center_freq_hz(peak).x; // X-axis
}
return get_weighted_freq_hz(peak);
}
// return all the center frequencies weighted by bin energy
// called from main thread
uint8_t AP_GyroFFT::get_weighted_noise_center_frequencies_hz(uint8_t num_freqs, float* freqs) const
{
if (!analysis_enabled()) {
freqs[0] = _fft_min_hz;
return 1;
}
if (_health.is_zero()) {
#if APM_BUILD_COPTER_OR_HELI || APM_BUILD_TYPE(APM_BUILD_ArduPlane)
// if we are post-filter sampling then throttle estimate will be useless
if (using_post_filter_samples()) {
return 0;
}
AP_Motors* motors = AP::motors();
if (motors != nullptr) {
// FFT is not healthy, fallback to FFT's throttle-based estimate
freqs[0] = constrain_float(_fft_min_hz * MAX(1.0f, sqrtf(motors->get_throttle_out() / _throttle_ref)), _fft_min_hz, _fft_max_hz);
return 1;
}
#endif
}
// pitch was good or required, roll was not, use pitch only
if (!_health.x || _harmonic_peak == FFT_HARMONIC_FIT_TRACK_PITCH) {
const uint8_t tracked_peaks = MIN(_health.y, num_freqs);
for (uint8_t i = 0; i < tracked_peaks; i++) {
freqs[i] = get_noise_center_freq_hz(FrequencyPeak(i)).y; // Y-axis
}
return tracked_peaks;
}
// roll was good or required, pitch was not, use roll only
if (!_health.y || _harmonic_peak == FFT_HARMONIC_FIT_TRACK_ROLL) {
const uint8_t tracked_peaks = MIN(_health.x, num_freqs);
for (uint8_t i = 0; i < tracked_peaks; i++) {
freqs[i] = get_noise_center_freq_hz(FrequencyPeak(i)).x; // X-axis
}
return tracked_peaks;
}
const uint8_t tracked_peaks = MIN(MAX(_health.x, _health.y), num_freqs);
for (uint8_t i = 0; i < tracked_peaks; i++) {
freqs[i] = get_weighted_freq_hz(FrequencyPeak(i));
}
return tracked_peaks;
}
// return noise energy at the requested frequency
float AP_GyroFFT::has_noise_at_frequency_hz(float freq) const
{
if (!analysis_enabled()) {
return 0.0f;
}
float max_energy = 0.0f;
// check each axis of each peak to see if it contains the pass frequency
for (uint8_t i = 0; i < _tracked_peaks; i++) {
const Vector3f& noise = get_noise_center_freq_hz(FrequencyPeak(i));
const Vector3f& snr = get_noise_signal_to_noise_db(FrequencyPeak(i));
for (uint8_t j = 0; j < XYZ_AXIS_COUNT; j++) {
if (!_rpy_health[j]) {
continue;
}
// only check one bin either side of the frequency
if ((noise[j] - _state->_bin_resolution) < freq && (noise[j] + _state->_bin_resolution) > freq) {
max_energy = MAX(snr[j], max_energy);
}
}
}
return max_energy;
}
float AP_GyroFFT::calculate_weighted_freq_hz(const Vector3f& energy, const Vector3f& freq) const
{
// there is generally a lot of high-energy, slightly lower frequency noise on yaw, however this
// appears to be a second-order effect as only targetting pitch and roll (x & y) produces much cleaner output all round
if (!energy.is_nan() && !is_zero(energy.x) && !is_zero(energy.y)) {
return (freq.x * energy.x + freq.y * energy.y)
/ (energy.x + energy.y);
}
else {
return (freq.x + freq.y) * 0.5f;
}
}
// @LoggerMessage: FTN1
// @Description: FFT Filter Tuning
// @Field: TimeUS: microseconds since system startup
// @Field: PkAvg: peak noise frequency as an energy-weighted average of roll and pitch peak frequencies
// @Field: BwAvg: bandwidth of weighted peak frequency where edges are determined by FFT_ATT_REF
// @Field: SnX: signal-to-noise ratio on the roll axis
// @Field: SnY: signal-to-noise ratio on the pitch axis
// @Field: SnZ: signal-to-noise ratio on the yaw axis
// @Field: FtX: harmonic fit on roll of the highest noise peak to the second highest noise peak
// @Field: FtY: harmonic fit on pitch of the highest noise peak to the second highest noise peak
// @Field: FtZ: harmonic fit on yaw of the highest noise peak to the second highest noise peak
// @Field: FHX: FFT health, X-axis
// @Field: FHY: FFT health, Y-axis
// @Field: FHZ: FFT health, Z-axis
// @Field: Tc: FFT cycle time
#if HAL_LOGGING_ENABLED
// log gyro fft messages
void AP_GyroFFT::write_log_messages()
{
if (!analysis_enabled()) {
return;
}
AP::logger().WriteStreaming(
"FTN1",
"TimeUS,PkAvg,BwAvg,SnX,SnY,SnZ,FtX,FtY,FtZ,FHX,FHY,FHZ,Tc",
"szz---%%%---s",
"F-----------F",
"QffffffffBBBI",
AP_HAL::micros64(),
get_weighted_noise_center_freq_hz(),
get_weighted_noise_center_bandwidth_hz(),
get_noise_signal_to_noise_db().x,
get_noise_signal_to_noise_db().y,
get_noise_signal_to_noise_db().z,
get_raw_noise_harmonic_fit().x,
get_raw_noise_harmonic_fit().y,
get_raw_noise_harmonic_fit().z,
_health.x, _health.y, _health.z, _output_cycle_micros);
log_noise_peak(0, FrequencyPeak::CENTER);
if (_tracked_peaks> 1) {
log_noise_peak(1, FrequencyPeak::LOWER_SHOULDER);
log_noise_peak(2, FrequencyPeak::UPPER_SHOULDER);
}
#if DEBUG_FFT
const uint32_t now = AP_HAL::millis();
// output at 1hz
if ((now - _last_output_ms) > 1000) {
// doing this from the update thread overflows the stack
WITH_SEMAPHORE(_sem);
GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "FFT: f:%.1f, fr:%.1f, b:%u, fd:%.1f",
_debug_state._center_freq_hz_filtered[FrequencyPeak::CENTER][_update_axis], _debug_state._center_freq_hz[_update_axis], _debug_max_bin, _debug_max_bin_freq);
GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "FFT: bw:%.1f, e:%.1f, r:%.1f, snr:%.1f",
_debug_state._center_bandwidth_hz_filtered[FrequencyPeak::CENTER][_update_axis], _debug_max_freq_bin, _ref_energy[_debug_max_bin][_update_axis], _debug_snr);
_last_output_ms = now;
}
#endif
}
// @LoggerMessage: FTN2
// @Description: FFT Noise Frequency Peak
// @Field: TimeUS: microseconds since system startup
// @Field: Id: peak id where 0 is the centre peak, 1 is the lower shoulder and 2 is the upper shoulder
// @Field: PkX: noise frequency of the peak on roll
// @Field: PkY: noise frequency of the peak on pitch
// @Field: PkZ: noise frequency of the peak on yaw
// @Field: BwX: bandwidth of the peak frequency on roll where edges are determined by FFT_ATT_REF
// @Field: BwY: bandwidth of the peak frequency on pitch where edges are determined by FFT_ATT_REF
// @Field: BwZ: bandwidth of the peak frequency on yaw where edges are determined by FFT_ATT_REF
// @Field: SnX: signal-to-noise ratio on the roll axis
// @Field: SnY: signal-to-noise ratio on the pitch axis
// @Field: SnZ: signal-to-noise ratio on the yaw axis
// @Field: EnX: power spectral density bin energy of the peak on roll
// @Field: EnY: power spectral density bin energy of the peak on roll
// @Field: EnZ: power spectral density bin energy of the peak on roll
// write a single log message
void AP_GyroFFT::log_noise_peak(uint8_t id, FrequencyPeak peak) const
{
AP::logger().WriteStreaming("FTN2", "TimeUS,Id,PkX,PkY,PkZ,BwX,BwY,BwZ,SnX,SnY,SnZ,EnX,EnY,EnZ", "s#zzzzzz------", "F-------------", "QBffffffffffff",
AP_HAL::micros64(),
id,
get_noise_center_freq_hz(peak).x,
get_noise_center_freq_hz(peak).y,
get_noise_center_freq_hz(peak).z,
get_noise_center_bandwidth_hz(peak).x,
get_noise_center_bandwidth_hz(peak).y,
get_noise_center_bandwidth_hz(peak).z,
get_noise_signal_to_noise_db(peak).x,
get_noise_signal_to_noise_db(peak).y,
get_noise_signal_to_noise_db(peak).z,
get_center_freq_energy(peak).x,
get_center_freq_energy(peak).y,
get_center_freq_energy(peak).z);
}
#endif
// return an average noise bandwidth weighted by bin energy
// called from main thread
float AP_GyroFFT::get_weighted_noise_center_bandwidth_hz() const
{
if (!analysis_enabled()) {
return 0.0f;
}
const FrequencyPeak peak = get_tracked_noise_peak();
return calculate_weighted_freq_hz(get_center_freq_energy(peak), get_noise_center_bandwidth_hz(peak));
}
// calculate noise frequencies from FFT data provided by the HAL subsystem
// called from FFT thread
void AP_GyroFFT::calculate_noise(bool calibrating, const EngineConfig& config)
{
// calculate the SNR and center frequency energy
float weighted_center_freq_hz = 0.0f;
uint8_t num_peaks = calculate_tracking_peaks(weighted_center_freq_hz, calibrating, config);
_thread_state._center_freq_bin[_update_axis] = _state->_peak_data[_thread_state._center_peak[_update_axis]]._bin;
_thread_state._center_freq_hz[_update_axis] = weighted_center_freq_hz;
// record the last time we had a good signal on this axis
if (num_peaks > 0) {
_thread_state._health_ms[_update_axis] = AP_HAL::millis();
} else {
_thread_state._health_ms[_update_axis] = 0;
}
_thread_state._health[_update_axis] = num_peaks;
FrequencyPeak tracked_peak = FrequencyPeak::CENTER;
// record the tracked peak for harmonic fit, but only if we have more than one noise peak
// this checks filtered energies and so can allow energies to be closer together
if (num_peaks > 1 && _tracked_peaks > 1 && !is_zero(get_tl_noise_center_freq_hz(FrequencyPeak::CENTER, _update_axis))) {
if (get_tl_noise_center_freq_hz(FrequencyPeak::CENTER, _update_axis) > get_tl_noise_center_freq_hz(FrequencyPeak::LOWER_SHOULDER, _update_axis)
// ignore the fit if there is too big a discrepancy between the energies
&& get_tl_center_freq_energy(FrequencyPeak::CENTER, _update_axis) < get_tl_center_freq_energy(FrequencyPeak::LOWER_SHOULDER, _update_axis) * FFT_HARMONIC_FIT_MULT) {
tracked_peak = FrequencyPeak::LOWER_SHOULDER;
} else if (num_peaks > 2 && get_tl_noise_center_freq_hz(FrequencyPeak::CENTER, _update_axis) > get_tl_noise_center_freq_hz(FrequencyPeak::UPPER_SHOULDER, _update_axis)
// ignore the fit if there is too big a discrepancy between the energies
&& get_tl_center_freq_energy(FrequencyPeak::CENTER, _update_axis) < get_tl_center_freq_energy(FrequencyPeak::UPPER_SHOULDER, _update_axis) * FFT_HARMONIC_FIT_MULT) {
tracked_peak = FrequencyPeak::UPPER_SHOULDER;
}
}
_thread_state._tracked_peak[_update_axis] = tracked_peak;
// if targetting more than one harmonic then make sure we get the fundamental
// on larger copters the second harmonic often has more energy
// if the highest peak is above the second highest then check for harmonic fit
// comparisons are made using filter, normalised data
if (_thread_state._tracked_peak[_update_axis] != FrequencyPeak::CENTER) {
// calculate the fit and filter at 10hz
const float harmonic_fit = 100.0f * fabsf(get_tl_noise_center_freq_hz(FrequencyPeak::CENTER, _update_axis)
- get_tl_noise_center_freq_hz(tracked_peak, _update_axis) * _harmonic_multiplier)
/ get_tl_noise_center_freq_hz(FrequencyPeak::CENTER, _update_axis);
// calculate the fit and filter at 10hz
if (isfinite(harmonic_fit)) {
_thread_state._harmonic_fit[_update_axis] = _harmonic_fit_filter[_update_axis].apply(harmonic_fit);
}
} else {
_thread_state._harmonic_fit[_update_axis] = 100.0f;
}
#if DEBUG_FFT
WITH_SEMAPHORE(_sem);
_debug_state = _thread_state;
_debug_max_freq_bin = _state->get_freq_bin(_state->_peak_data[FrequencyPeak::CENTER]._bin);
_debug_max_bin_freq = _state->_peak_data[FrequencyPeak::CENTER]._freq_hz;
_debug_snr = snr;
_debug_max_bin = _state->_peak_data[FrequencyPeak::CENTER]._bin;
#endif
}
// calculate noise peaks based on the frequencies closest to the recent historical average, switching peaks around as necessary
uint8_t AP_GyroFFT::calculate_tracking_peaks(float& weighted_center_freq_hz, bool calibrating, const EngineConfig& config)
{
uint8_t num_peaks = 0;
FrequencyData freqs(*this, config);
// the noise peaks are returned by the HAL in decreasing order of magnitude, however each peak can temporarily
// switch places with another depending on a whole host of hardware and software factors
// thus we must be able to temporarily reassign the peaks so that the filtered values track
// a continuous frequency
DistanceMatrix distance_matrix;
find_distance_matrix(distance_matrix, freqs, config);
FrequencyPeak center = find_closest_peak(FrequencyPeak::CENTER, distance_matrix);
FrequencyPeak lower = find_closest_peak(FrequencyPeak::LOWER_SHOULDER, distance_matrix, 1 << center);
FrequencyPeak upper = find_closest_peak(FrequencyPeak::UPPER_SHOULDER, distance_matrix, 1 << center | 1 << lower);
// if we have had the maximum number of swapped cycles, force a full calculation
if (calibrating || _distorted_cycles[_update_axis] == 0) {
num_peaks = calculate_tracking_peaks(weighted_center_freq_hz, freqs, config);
#if DEBUG_FFT
printf("Skipped update, order would have been is %d/%.1f(%.1f) %d/%.1f(%.1f) %d/%.1f(%.1f) n = %d\n",
center, _state->_peak_data[center]._freq_hz, get_tl_noise_center_freq_hz(FrequencyPeak::CENTER, _update_axis),
lower, _state->_peak_data[lower]._freq_hz, get_tl_noise_center_freq_hz(FrequencyPeak::LOWER_SHOULDER, _update_axis),
upper, _state->_peak_data[upper]._freq_hz, get_tl_noise_center_freq_hz(FrequencyPeak::UPPER_SHOULDER, _update_axis), num_peaks);
#endif
return num_peaks;
}
// another peak is closer to what is currently considered the center frequency
if (center != FrequencyPeak::CENTER || lower != FrequencyPeak::LOWER_SHOULDER || upper != FrequencyPeak::UPPER_SHOULDER) {
if (lower != FrequencyPeak::NONE && calculate_filtered_noise(FrequencyPeak::LOWER_SHOULDER, lower, freqs, config)) {
num_peaks++;
} else {
lower = FrequencyPeak::NONE;
}
if (upper != FrequencyPeak::NONE && calculate_filtered_noise(FrequencyPeak::UPPER_SHOULDER, upper, freqs, config)) {
num_peaks++;
} else {
upper = FrequencyPeak::NONE;
}
if (center != FrequencyPeak::NONE && calculate_filtered_noise(FrequencyPeak::CENTER, center, freqs, config)) {
num_peaks++;
} else {
center = FrequencyPeak::NONE;
}
weighted_center_freq_hz = freqs.get_weighted_frequency(center);
_thread_state._center_peak[_update_axis] = center;
update_snr_values(freqs);
// if two adjacent peaks have simply swapped, we will allow this to continue indefinitely
// as there is no loss of fidelity
if (!((center == FrequencyPeak::LOWER_SHOULDER && lower == FrequencyPeak::CENTER)
|| (center == FrequencyPeak::UPPER_SHOULDER && upper == FrequencyPeak::CENTER))) {
_distorted_cycles[_update_axis]--;
}
return num_peaks;
}
num_peaks = calculate_tracking_peaks(weighted_center_freq_hz, freqs, config);
return num_peaks;
}
// calculate the noise and whether valid for each peak
uint8_t AP_GyroFFT::calculate_tracking_peaks(float& weighted_center_freq_hz, const FrequencyData& freqs, const EngineConfig& config)
{
uint8_t num_peaks = 0;
if (calculate_filtered_noise(FrequencyPeak::LOWER_SHOULDER, FrequencyPeak::LOWER_SHOULDER, freqs, config)) {
num_peaks++;
}
if (calculate_filtered_noise(FrequencyPeak::UPPER_SHOULDER, FrequencyPeak::UPPER_SHOULDER, freqs, config)) {
num_peaks++;
}
if (calculate_filtered_noise(FrequencyPeak::CENTER, FrequencyPeak::CENTER, freqs, config)) {
num_peaks++;
}
// record the number of cycles where something was tracked
_distorted_cycles[_update_axis] = constrain_int16(_distorted_cycles[_update_axis] + 1, 0, FFT_MAX_MISSED_UPDATES);
weighted_center_freq_hz = freqs.get_weighted_frequency(FrequencyPeak::CENTER);
_thread_state._center_peak[_update_axis] = FrequencyPeak::CENTER;
update_snr_values(freqs);
return num_peaks;
}
// calculate noise frequencies from FFT data provided by the HAL subsystem
// target_peak is the filtered record we want to apply the new fft data to, source peak is where the fft data is coming from
// called from FFT thread
bool AP_GyroFFT::calculate_filtered_noise(FrequencyPeak target_peak, FrequencyPeak source_peak, const FrequencyData& freqs, const EngineConfig& config)
{
if (source_peak > FrequencyPeak::MAX_TRACKED_PEAKS) {
// if we failed to find a signal, carry on using the previous readings
if (_missed_cycles[_update_axis][target_peak]++ < FFT_MAX_MISSED_UPDATES) {
return true; // the peak is synthetic
}
update_tl_center_freq_energy(target_peak, _update_axis, 0.0f);
update_tl_noise_center_bandwidth_hz(target_peak, _update_axis, _bandwidth_hover_hz);
update_tl_noise_center_freq_hz(target_peak, _update_axis, config._fft_min_hz);
return false;
}
AP_HAL::DSP::FrequencyPeakData* peak_data = &_state->_peak_data[source_peak];
const uint16_t nb = peak_data->_bin;
if (freqs.is_valid(FrequencyPeak(source_peak))) {
// total peak energy requires an integration, as an approximation use amplitude * noise width * 5/6
update_tl_center_freq_energy(target_peak, _update_axis, _state->get_freq_bin(nb) * peak_data->_noise_width_hz * 0.8333f);
update_tl_noise_center_bandwidth_hz(target_peak, _update_axis, peak_data->_noise_width_hz);
update_tl_noise_center_freq_hz(target_peak, _update_axis, freqs.get_weighted_frequency(FrequencyPeak(source_peak)));
_missed_cycles[_update_axis][target_peak] = 0;
return true;
}
// if we failed to find a signal, carry on using the previous readings
if (_missed_cycles[_update_axis][target_peak]++ < FFT_MAX_MISSED_UPDATES) {
return true; // the peak is synthetic
}
// we failed to find a signal for more than FFT_MAX_MISSED_UPDATES cycles
update_tl_center_freq_energy(target_peak, _update_axis, _state->get_freq_bin(nb) * peak_data->_noise_width_hz * 0.8333f); // use the actual energy detected rather than 0
update_tl_noise_center_bandwidth_hz(target_peak, _update_axis, _bandwidth_hover_hz);
update_tl_noise_center_freq_hz(target_peak, _update_axis, config._fft_min_hz);
return false;
}
void AP_GyroFFT::update_snr_values(const FrequencyData& freqs)
{
_thread_state._center_freq_snr[FrequencyPeak::CENTER][_update_axis] = freqs.get_signal_to_noise(FrequencyPeak::CENTER);
_thread_state._center_freq_snr[FrequencyPeak::LOWER_SHOULDER][_update_axis] = freqs.get_signal_to_noise(FrequencyPeak::LOWER_SHOULDER);
_thread_state._center_freq_snr[FrequencyPeak::UPPER_SHOULDER][_update_axis] = freqs.get_signal_to_noise(FrequencyPeak::UPPER_SHOULDER);
}
// filter values through a median sliding window followed by low pass filter
// this eliminates temporary spikes in the detected frequency that are either pure noise
// or a different peak that will erroneously bias the peak we are tracking
float AP_GyroFFT::MedianLowPassFilter3dFloat::apply(uint8_t axis, float sample)
{
_median_filter[axis].apply(sample);
const float a = _median_filter[axis].get_sample(0);
const float b = _median_filter[axis].get_sample(1);
const float c = _median_filter[axis].get_sample(2);
float median = MAX(MIN(a, b), MIN(MAX(a, b), c));
return _lowpass_filter[axis].apply(median);
}
// initialize a FrequencyData structure with peak frequency information for use in the swapping algorithm
AP_GyroFFT::FrequencyData::FrequencyData(const AP_GyroFFT& gyrofft, const EngineConfig& config)
{
for (uint8_t i = 0; i < FrequencyPeak::MAX_TRACKED_PEAKS; i++) {
valid[i] = gyrofft.get_weighted_frequency(FrequencyPeak(i), frequency[i], snr[i], config);
}
}
// calculate noise frequencies from FFT data provided by the HAL subsystem
bool AP_GyroFFT::get_weighted_frequency(FrequencyPeak peak, float& weighted_peak_freq_hz, float& snr, const EngineConfig& config) const
{
AP_HAL::DSP::FrequencyPeakData* peak_data = &_state->_peak_data[peak];
const uint16_t bin = peak_data->_bin;
// calculate the SNR and center frequency energy
const float max_energy = MAX(1.0f, _state->get_freq_bin(bin));
const float ref_energy = MAX(1.0f, _ref_energy[bin][_update_axis]);
snr = 10.f * (log10f(max_energy) - log10f(ref_energy));
// if the bin energy is above the noise threshold then we have a signal
if (!_thread_state._noise_needs_calibration && isfinite(_state->get_freq_bin(bin)) && snr > config._snr_threshold_db) {
weighted_peak_freq_hz = constrain_float(peak_data->_freq_hz, (float)config._fft_min_hz, (float)config._fft_max_hz);
return true;
}
weighted_peak_freq_hz = (float)config._fft_min_hz;
return false;
}
// calculate a matrix of distances between the current filtered estimates and instantaneous values from the current cycle
void AP_GyroFFT::find_distance_matrix(DistanceMatrix& distance_matrix, const FrequencyData& freqs, const EngineConfig& config) const
{
float curr_freqs[FrequencyPeak::MAX_TRACKED_PEAKS];
// get the current frequency estimate for all peaks
for (uint8_t i = 0; i < FrequencyPeak::MAX_TRACKED_PEAKS; i++) {
curr_freqs[i] = get_tl_noise_center_freq_hz(FrequencyPeak(i), _update_axis);
}
// calculate the matrix
for (uint8_t i = 0; i < FrequencyPeak::MAX_TRACKED_PEAKS; i++) {
for (uint8_t j = 0; j < FrequencyPeak::MAX_TRACKED_PEAKS; j++) {
distance_matrix[i][j] = fabsf((freqs.is_valid(FrequencyPeak(i)) ?
freqs.get_weighted_frequency(FrequencyPeak(i)) : FLT_MAX) - curr_freqs[j]);
}
}
}
// return the instantaneous peak that is closest to the target estimate peak
AP_GyroFFT::FrequencyPeak AP_GyroFFT::find_closest_peak(const FrequencyPeak target, const DistanceMatrix& distance_matrix, uint8_t ignore) const
{
// find the closest peak to target
uint8_t closest = target;
for (uint8_t i = 0; i < FrequencyPeak::MAX_TRACKED_PEAKS; i++) {
if (distance_matrix[i][target] < distance_matrix[closest][target] && (1 << i & ~ignore)) {
closest = i;
}
}
// didn't find anything
if (!(1<<closest & ~ignore)) {
return FrequencyPeak::NONE;
}
return FrequencyPeak(closest);
}
// calculate noise baseline from FFT data provided by the HAL subsystem
// called from FFT thread
void AP_GyroFFT::update_ref_energy(uint16_t max_bin)
{
if (!_thread_state._noise_needs_calibration) {
return;
}
// according to https://www.tcd.ie/Physics/research/groups/magnetism/files/lectures/py5021/MagneticSensors3.pdf sensor noise is not necessarily gaussian
// determine a PS noise reference at each of the possible center frequencies
if (_noise_cycles == 0 && _noise_calibration_cycles[_update_axis] > 0) {
for (uint16_t i = 1; i < _state->_bin_count; i++) {
_ref_energy[i][_update_axis] += _state->get_freq_bin(i);
}
if (--_noise_calibration_cycles[_update_axis] == 0) {
for (uint16_t i = 1; i < _state->_bin_count; i++) {
const float cycles = (static_cast<float>(_window_size) / static_cast<float>(_samples_per_frame)) * 2;
// overall random noise is reduced by sqrt(N) when averaging periodigrams so adjust for that
_ref_energy[i][_update_axis] = (_ref_energy[i][_update_axis] / cycles) * sqrtf(cycles);
}
WITH_SEMAPHORE(_sem);
_thread_state._noise_needs_calibration &= ~(1 << _update_axis);
}
}
else if (_noise_cycles > 0) {
_noise_cycles--;
}
}
// perform FFT analysis on the range of frequencies supported by the analyser
// called from main thread
float AP_GyroFFT::self_test_bin_frequencies()
{
if (_state->_window_size * sizeof(float) > hal.util->available_memory() / 2) {
GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "FFT: unable to run self-test, required %u bytes", (unsigned int)(_state->_window_size * sizeof(float)));
return 0.0f;
}
FloatBuffer test_window(_state->_window_size);
// in the unlikely event we can't allocate a test window, skip the checks
if (test_window.get_size() == 0) {
return 0.0f;
}
float max_divergence = 0;
for (uint16_t bin = _config._fft_start_bin; bin <= _config._fft_end_bin; bin++) {
// the algorithm will only ever return values in this range
float frequency = constrain_float(bin * _state->_bin_resolution, _fft_min_hz, _fft_max_hz);
max_divergence = MAX(max_divergence, self_test(frequency, test_window)); // test bin centers
frequency = constrain_float(bin * _state->_bin_resolution - _state->_bin_resolution / 4, _fft_min_hz, _fft_max_hz);
max_divergence = MAX(max_divergence, self_test(frequency, test_window)); // test bin off-centers
}
return max_divergence;
}
// perform FFT analysis of a single sine wave at the selected frequency
// called from main thread
float AP_GyroFFT::self_test(float frequency, FloatBuffer& test_window)
{
test_window.clear();
for(uint16_t i = 0; i < _state->_window_size; i++) {
if (!test_window.push(sinf(2.0f * M_PI * frequency * i / _fft_sampling_rate_hz) * ToRad(20) * 2000)) {
AP_HAL::panic("Could not create FFT test window");
}
}
_update_axis = 0;
// if using averaging we need to process _num_frames in order to not bias the result
for (uint8_t i = 1; i < _num_frames; i++) {
hal.dsp->fft_start(_state, test_window, 0);
hal.dsp->fft_analyse(_state, _config._fft_start_bin, _config._fft_end_bin, _config._attenuation_cutoff);
}
// final cycle is the one we want
hal.dsp->fft_start(_state, test_window, 0);
uint16_t max_bin = hal.dsp->fft_analyse(_state, _config._fft_start_bin, _config._fft_end_bin, _config._attenuation_cutoff);
if (max_bin == 0) {
GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "FFT: self-test failed, failed to find frequency %.1f", frequency);
}
calculate_noise(true, _config);
float max_divergence = 0;
// make sure the selected frequencies are in the right bin
max_divergence = MAX(max_divergence, fabsf(frequency - _thread_state._center_freq_hz[0]));
if (_thread_state._center_freq_hz[0] < (frequency - MAX(_state->_bin_resolution * 0.5f, 1)) || _thread_state._center_freq_hz[0] > (frequency + MAX(_state->_bin_resolution * 0.5f, 1))) {
GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "FFT: self-test failed: wanted %.1f, had %.1f", frequency, _thread_state._center_freq_hz[0]);
}
#if DEBUG_FFT
else {
GCS_SEND_TEXT(MAV_SEVERITY_INFO, "FFT: self-test succeeded: wanted %.1f, had %.1f", frequency, _thread_state._center_freq_hz[0]);
}
#endif
return max_divergence;
}
// singleton instance
AP_GyroFFT *AP_GyroFFT::_singleton;
namespace AP {
AP_GyroFFT *fft()
{
return AP_GyroFFT::get_singleton();
}
}
#endif // HAL_GYROFFT_ENABLED
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