forked from Archive/PX4-Autopilot
EKF: Remove un-used code
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@ -97,32 +97,6 @@ void EKFGSF_yaw::update(const imuSample& imu_sample,
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// all weights have collapsed due to excessive innovation variances so reset filters
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initialiseEKFGSF();
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}
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// Enforce a minimum weighting value. This was added during initial development but has not been needed
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// subsequently, so this block of code and the corresponding _weight_min can be removed if we get
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// through testing without any weighting function issues.
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if (_weight_min > FLT_EPSILON) {
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float correction_sum = 0.0f; // amount the sum of weights has been increased by application of the limit
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bool change_mask[N_MODELS_EKFGSF] = {}; // true when the weighting for that model has been increased
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float unmodified_weights_sum = 0.0f; // sum of unmodified weights
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for (uint8_t model_index = 0; model_index < N_MODELS_EKFGSF; model_index ++) {
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if (_model_weights(model_index) < _weight_min) {
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correction_sum += _weight_min - _model_weights(model_index);
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_model_weights(model_index) = _weight_min;
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change_mask[model_index] = true;
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} else {
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unmodified_weights_sum += _model_weights(model_index);
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}
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}
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// rescale the unmodified weights to make the total sum unity
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const float scale_factor = (unmodified_weights_sum - correction_sum - _weight_min) / (unmodified_weights_sum - _weight_min);
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for (uint8_t model_index = 0; model_index < N_MODELS_EKFGSF; model_index ++) {
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if (!change_mask[model_index]) {
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_model_weights(model_index) = _weight_min + scale_factor * (_model_weights(model_index) - _weight_min);
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}
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}
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}
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}
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}
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} else if (_ekf_gsf_vel_fuse_started && !_run_ekf_gsf) {
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@ -58,7 +58,6 @@ private:
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const float _accel_noise{2.0f}; // horizontal accel noise used for covariance prediction (m/sec**2)
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const float _tilt_gain{0.2f}; // gain from tilt error to gyro correction for complementary filter (1/sec)
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const float _gyro_bias_gain{0.04f}; // gain applied to integral of gyro correction for complementary filter (1/sec)
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const float _weight_min{0.0f}; // minimum value of an individual model weighting
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// Declarations used by the bank of N_MODELS_EKFGSF AHRS complementary filters
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