px4-firmware/msg/YawEstimatorStatus.msg

uint64 timestamp		# time since system start (microseconds)
uint64 timestamp_sample         # the timestamp of the raw data (microseconds)

float32 yaw_composite	# composite yaw from GSF (rad)
float32 yaw_variance	# composite yaw variance from GSF (rad^2)
bool yaw_composite_valid

float32[5] yaw		# yaw estimate for each model in the filter bank (rad)
float32[5] innov_vn	# North velocity innovation for each model in the filter bank (m/s)
float32[5] innov_ve	# East velocity innovation for each model in the filter bank (m/s)
float32[5] weight	# weighting for each model in the filter bank
estimator messages add explicit timestamp_sample - timestamp is uORB publication metadata - this allows us to see what the system saw at publication time plus the latency in estimation 2020-09-01 15:38:41 -03:00			`uint64 timestamp # time since system start (microseconds)`
			`uint64 timestamp_sample # the timestamp of the raw data (microseconds)`

Update ecl to add ability to recover from bad magnetic yaw * msg: Add EKF-GSF yaw estimator logging data * ecl: update to version with EKF-GSF yaw estimator * ekf2: Add param control and logging for EKF-GSF yaw estimator * logger: Add logging for EKF-GSF yaw esimtator 2020-03-15 13:57:25 -03:00			`float32 yaw_composite # composite yaw from GSF (rad)`
			`float32 yaw_variance # composite yaw variance from GSF (rad^2)`
ekf2: yaw estimator add yaw_composite_valid boolean 2022-03-25 12:30:32 -03:00			`bool yaw_composite_valid`
estimator messages add explicit timestamp_sample - timestamp is uORB publication metadata - this allows us to see what the system saw at publication time plus the latency in estimation 2020-09-01 15:38:41 -03:00
Update ecl to add ability to recover from bad magnetic yaw * msg: Add EKF-GSF yaw estimator logging data * ecl: update to version with EKF-GSF yaw estimator * ekf2: Add param control and logging for EKF-GSF yaw estimator * logger: Add logging for EKF-GSF yaw esimtator 2020-03-15 13:57:25 -03:00			`float32[5] yaw # yaw estimate for each model in the filter bank (rad)`
			`float32[5] innov_vn # North velocity innovation for each model in the filter bank (m/s)`
			`float32[5] innov_ve # East velocity innovation for each model in the filter bank (m/s)`
			`float32[5] weight # weighting for each model in the filter bank`