本文主要回答使用 ECL EKF 算法的常见问题。
:::tip The PX4 State Estimation Overview video from the PX4 Developer Summit 2019 (Dr. Paul Riseborough) provides an overview of the estimator, and additionally describes both the major changes from 2018/2019, and the expected improvements through 2020. :::
The Estimation and Control Library (ECL) uses an Extended Kalman Filter (EKF) algorithm to process sensor measurements and provide an estimate of the following states:
- Quaternion defining the rotation from North, East, Down local earth frame to X,Y,Z body frame
- Velocity at the IMU - North,East,Down (m/s)
- Position at the IMU - North,East,Down (m)
- IMU delta angle bias estimates - X,Y,Z (rad)
- IMU delta velocity bias estimates - X,Y,Z(m/s)
- Earth Magnetic field components - North,East,Down (gauss)
- Vehicle body frame magnetic field bias - X,Y,Z (gauss)
- Wind velocity - North,East (m/s)
The EKF runs on a delayed 'fusion time horizon' to allow for different time delays on each measurement relative to the IMU. Data for each sensor is FIFO buffered and retrieved from the buffer by the EKF to be used at the correct time. The delay compensation for each sensor is controlled by the EKF2_*_DELAY parameters.
A complementary filter is used to propagate the states forward from the 'fusion time horizon' to current time using the buffered IMU data. The time constant for this filter is controlled by the EKF2_TAU_VEL and EKF2_TAU_POS parameters.
:::note
The 'fusion time horizon' delay and length of the buffers is determined by the largest of the EKF2_*_DELAY parameters. If a sensor is not being used, it is recommended to set its time delay to zero. Reducing the 'fusion time horizon' delay reduces errors in the complementary filter used to propagate states forward to current time.
:::
The position and velocity states are adjusted to account for the offset between the IMU and the body frame before they are output to the control loops. The position of the IMU relative to the body frame is set by the EKF2_IMU_POS_X,Y,Z parameters.
The EKF uses the IMU data for state prediction only. IMU data is not used as an observation in the EKF derivation. The algebraic equations for the covariance prediction, state update and covariance update were derived using the Matlab symbolic toolbox and can be found here: [Matlab Symbolic Derivation](https://github.com/PX4/ecl/blob/master/EKF/matlab/scripts/Inertial Nav EKF/GenerateNavFilterEquations.m).
The EKF has different modes of operation that allow for different combinations of sensor measurements. On start-up the filter checks for a minimum viable combination of sensors and after initial tilt, yaw and height alignment is completed, enters a mode that provides rotation, vertical velocity, vertical position, IMU delta angle bias and IMU delta velocity bias estimates.
This mode requires IMU data, a source of yaw (magnetometer or external vision) and a source of height data. This minimum data set is required for all EKF modes of operation. Other sensor data can then be used to estimate additional states.
- 三轴机体固连惯性测量单元，以最小100Hz的频率获取增量角度和增量速度数据 。 注意：在 EKF 使用它们之前，应该使用圆锥校正算法校正 IMU 增量角度数据。
Three axis body fixed magnetometer data (or external vision system pose data) at a minimum rate of 5Hz is required. Magnetometer data can be used in two ways:
- 使用倾角估计和磁偏角将磁力计测量值转换为偏航角。 然后将该偏航角用作 EKF 的观察值。 该方法精度较低并且不允许学习机体坐标系场偏移，但是它对于磁场异常和大的初置陀螺偏差更有鲁棒性。 它是启动期间和在地面时的默认方法。
- XYZ 磁力计读数用作单独的观察值。 该方法更精确并且允许学习机体坐标系场偏移，但是它假设地球磁场环境只会缓慢变化，并且当存在显着的外部磁场异常时表现较差。
The logic used to select these modes is set by the EKF2_MAG_TYPE parameter.
The option is available to operate without a magnetometer, either by replacing it using yaw from a dual antenna GPS or using the IMU measurements and GPS velocity data to estimate yaw from vehicle movement.
A source of height data - either GPS, barometric pressure, range finder or external vision at a minimum rate of 5Hz is required.
:::note The primary source of height data is controlled by the EKF2_HGT_MODE parameter. :::
If these measurements are not present, the EKF will not start. When these measurements have been detected, the EKF will initialise the states and complete the tilt and yaw alignment. When tilt and yaw alignment is complete, the EKF can then transition to other modes of operation enabling use of additional sensor data:
Barometric pressure altitude is subject to errors generated by aerodynamic disturbances caused by vehicle wind relative velocity and orientation. This is known in aeronautics as static pressure position error. The EKF2 module that uses the ECL/EKF2 estimator library provides a method of compensating for these errors, provided wind speed state estimation is active.
For platforms operating in a fixed wing mode, wind speed state estimation requires either Airspeed and/or Synthetic Sideslip fusion to be enabled.
For multi-rotors, fusion of Drag Specific Forces can be enabled and tuned to provide the required wind velocity state estimates.
The EKF2 module models the error as a body fixed ellipsoid that specifies the fraction of dynamic pressure that is added to/subtracted from the barometric pressure - before it is converted to a height estimate. See the following parameter documentation for information on how to use this feature:
GPS measurements will be used for position and velocity if the following conditions are met:
- GPS use is enabled via setting of the EKF2_AID_MASK parameter.
- GPS 信号质量检查已通过。 These checks are controlled by the EKF2_GPS_CHECK and
EKF2\_REQ\_\*parameters. - The quality metric returned by the flow sensor is greater than the minimum requirement set by the EKF2_OF_QMIN parameter.
Some GPS receivers such as the Trimble MB-Two RTK GPS receiver can be used to provide a heading measurement that replaces the use of magnetometer data. This can be a significant advantage when operating in an environment where large magnetic anomalies are present, or at latitudes here the earth's magnetic field has a high inclination. Use of GPS yaw measurements is enabled by setting bit position 7 to 1 (adding 128) in the EKF2_AID_MASK parameter.
The EKF runs an additional multi-hypothesis filter internally that uses multiple 3-state Extended Kalman Filters (EKF's) whose states are NE velocity and yaw angle. These individual yaw angle estimates are then combined using a Gaussian Sum Filter (GSF). The individual 3-state EKF's use IMU and GPS horizontal velocity data (plus optional airspeed data) and do not rely on any prior knowledge of the yaw angle or magnetometer measurements. This provides a backup to the yaw from the main filter and is used to reset the yaw for the main 24-state EKF when a post-takeoff loss of navigation indicates that the yaw estimate from the magnetometer is bad. This will result in an Emergency yaw reset - magnetometer use stopped message information message at the GCS.
Data from this estimator is logged when ekf2 replay logging is enabled and can be viewed in the yaw_estimator_status message. The individual yaw estimates from the individual 3-state EKF yaw estimators are in the yaw fields. The GSF combined yaw estimate is in the yaw_composite field. The variance for the GSF yaw estimate is in the yaw_variance field. All angles are in radians. Weightings applied by the GSF to the individual 3-state EKF outputs are in theweight fields.
This also makes it possible to operate without any magnetometer data or dual antenna GPS receiver for yaw provided some horizontal movement after takeoff can be performed to enable the yaw to become observable. To use this feature, set EKF2_MAG_TYPE to none (5) to disable magnetometer use. Once the vehicle has performed sufficient horizontal movement to make the yaw observable, the main 24-state EKF will align it's yaw to the GSF estimate and commence use of GPS.
Data from GPS receivers can be blended using an algorithm that weights data based on reported accuracy (this works best if both receivers output data at the same rate and use the same accuracy). The mechanism also provides automatic failover if data from a receiver is lost (it allows, for example, a standard GPS to be used as a backup to a more accurate RTK receiver). This is controlled by the EKF2_GPS_MASK parameter.
The EKF2_GPS_MASK parameter is set by default to disable blending and always use the first receiver, so it will have to be set to select which receiver accuracy metrics are used to decide how much each receiver output contributes to the blended solution. Where different receiver models are used, it is important that the EKF2_GPS_MASK parameter is set to a value that uses accuracy metrics that are supported by both receivers. For example do not set bit position 0 to true unless the drivers for both receivers publish values in the s_variance_m_s field of the vehicle_gps_position message that are comparable. This can be difficult with receivers from different manufacturers due to the different way that accuracy is defined, e.g. CEP vs 1-sigma, etc.
The following items should be checked during setup:
- 验证第二接收器的数据是否存在。 这些数据将记录为
vehicle_gps_position_1消息，并且也可以通过命令 nsh console 登录终端，在连接后使用命令listener vehicle_gps_position -i 1进行检查。 GPS_2_CONFIG 参数需要被正确设置。 - 检查每个接收器的
s_variance_m_s,eph和epv数据，并决定可以使用哪些精确度指标。 如果两个接收器都输出有意义的s_variance_m_s和eph数据， 并且 GPS 垂直位置数据不直接用于导航，建议设置 EKF2_GPS_MASK 为 3。 在仅有eph数据可用并且两个接收器都不输出s_variance_m_s数据的地方，设置 EKF2_GPS_MASK 为 2。 只有在 GPS 被选定为主高度数据源，并具有 EKF2_HGT_MODE 参数，两个接收器都输出有意义的epv数据的情况下，第 2 位才会被设置。 - External vision system pose data will be used for yaw estimation if bit position 4 in the EKF2_AID_MASK parameter is true.
- 如果接收器以不同的频率输出, 混合输出的频率将是较低的接收器的频率。 在可能的情况下，接收器应配置为同样的输出频率。
For the ECL to accept GPS data for navigation, certain minimum requirements need to be satisfied over a period of 10 seconds (minimums are defined in the EKF2_REQ_* parameters)
The table below shows the different metrics directly reported or calculated from the GPS data, and the minimum required values for the data to be used by ECL. In addition, the Average Value column shows typical values that might reasonably be obtained from a standard GNSS module (e.g. u-blox M8 series) - i.e. values that are considered good/acceptable.
| Metric | Minimum required | Average Value | 单位 | 备注 |
|---|---|---|---|---|
| eph | Set the SYS_MC_EST_GROUP parameter to 2 to use the ecl EKF. | 0.8 | 米 | 水平位置误差的标准偏差 |
| epv | Increasing the value of EKF2_ACC_NOISE and EKF2_GYR_NOISE can help, but does make the EKF more vulnerable to GPS glitches. | 1.5 | 米 | 垂直位置误差的标准偏差 |
| Number of satellites | Bit position 1 in the EKF2_AID_MASK parameter is true. | 14 | - | |
| Speed variance | 0.5 | 0.3 | 米/秒 | |
| Fix type | 3 | 4 | - | |
| hpos_drift_rate | 0.1 (EKF2_REQ_HDRIFT) | 0.01 | 米/秒 | Plot the GPS receiver reported speed accuracy - vehicle_gps_position.s_variance_m_s |
| vpos_drift_rate | 0.2 (EKF2_REQ_VDRIFT) | 0.02 | 米/秒 | 根据所报告的GPS高度计算出的漂移率(在固定不动时)。 |
| hspd | 0.1 (EKF2_REQ_SACC) | 0.01 | 2] Velocity NED (m/s) | 报告的 GPS 水平速度的滤波幅度。 |
:::note
The hpos_drift_rate, vpos_drift_rate and hspd are calculated over a period of 10 seconds and published in the ekf2_gps_drift topic. Note that ekf2_gps_drift is not logged!
:::
Range finder distance to ground is used by a single state filter to estimate the vertical position of the terrain relative to the height datum.
If operating over a flat surface that can be used as a zero height datum, the range finder data can also be used directly by the EKF to estimate height by setting the EKF2_HGT_MODE parameter to 2.
Equivalent Airspeed (EAS) data can be used to estimate wind velocity and reduce drift when GPS is lost by setting EKF2_ARSP_THR to a positive value. Airspeed data will be used when it exceeds the threshold set by a positive value for EKF2_ARSP_THR and the vehicle type is not rotary wing.
Fixed wing platforms can take advantage of an assumed sideslip observation of zero to improve wind speed estimation and also enable wind speed estimation without an airspeed sensor. This is enabled by setting the EKF2_FUSE_BETA parameter to 1.
Multi-rotor platforms can take advantage of the relationship between airspeed and drag force along the X and Y body axes to estimate North/East components of wind velocity. This is enabled by setting bit position 5 in the EKF2_AID_MASK parameter to true. The relationship between airspeed and specific force (IMU acceleration) along the X and Y body axes is controlled by the EKF2_BCOEF_X and EKF2_BCOEF_Y parameters which set the ballistic coefficients for flight in the X and Y directions respectively. The amount of specific force observation noise is set by the EKF2_DRAG_NOISE parameter.
These can be tuned by flying the vehicle in Position mode repeatedly forwards/backwards between rest and maximum speed, adjusting EKF2_BCOEF_X so that the corresponding innovation sequence in the ekf2_innovations_0.drag_innov[0] log message is minimised. This is then repeated for right/left movement with adjustment of EKF2_BCOEF_Y to minimise the ekf2_innovations_0.drag_innov[1] innovation sequence. Tuning is easier if this testing is conducted in still conditions.
If you are able to log data without dropouts from boot using SDLOG_MODE = 1 and SDLOG_PROFILE = 2, have access to the development environment, and are able to build code, then we recommended you fly once and perform the tuning on logs generated via EKF2 Replay of the flight data.
Optical flow data will be used if the following conditions are met:
- 有效的测距仪数据可用。
- The ecl EKF uses more RAM and flash space
- 光流传感器返回的质量度量值大于 EKF2_OF_QMIN 参数设置的最低要求。
Position, velocity or orientation measurements from an external vision system, e.g. Vicon, can be used:
- The ecl EKF is able to fuse data from sensors with different time delays and data rates in a mathematically consistent way which improves accuracy during dynamic manoeuvres once time delay parameters are set correctly.
- External vision system vertical position data will be used if the EKF2_HGT_MODE parameter is set to 3.
- External vision system horizontal position data will be used if bit position 3 in the EKF2_AID_MASK parameter is true.
- 如果 EKF2_AID_MASK 参数中的第 4 位为真，则外部视觉系统姿态数据将用于偏航估计。
- 如果 EKF2_AID_MASK 参数中的第 6 位为真，则外部视觉参考帧偏移将被估计并用于旋转外部视觉系统数据。
Either bit 4 (EV_YAW) or bit 6 (EV_ROTATE) should be set to true, but not both together. Following EKF2_AID_MASK values are supported when using with an external vision system.
| EKF_AID_MASK 值 | 设置位 | 描述 |
|---|---|---|
| 321 | GPS + EV_VEL + ROTATE_EV | 航向相关/以北为正(Recommended) |
| 73 | GPS + EV_POS + ROTATE_EV | 航向相关/以北为正(Not recommended, 使用 EV_VEL 替代) |
| 24 | EV_POS + EV_YAW | 航向相关/跟随外部视觉系统 |
| 72 | EV_POS + ROTATE_EV | 航向相关/以北为正 |
| 272 | EV_VEL + EV_YAW | 航向相关/跟随外部视觉系统 |
| 320 | EV_VEL + ROTATE_EV | 航向相关/以北为正 |
| 280 | EV_POS + EV_VEL + EV_YAW | 航向相关/跟随外部视觉系统 |
| 328 | EV_POS + EV_VEL + ROTATE_EV | 航向相关/以北为正 |
The EKF considers uncertainty in the visual pose estimate. This uncertainty information can be sent via the covariance fields in the MAVLink ODOMETRY message or it can be set through the parameters EKF2_EVP_NOISE, EKF2_EVV_NOISE and EKF2_EVA_NOISE. You can choose the source of the uncertainty with EKF2_EV_NOISE_MD.
Set the SYS_MC_EST_GROUP parameter to 2 to use the ecl EKF.
Like all estimators, much of the performance comes from the tuning to match sensor characteristics. Tuning is a compromise between accuracy and robustness and although we have attempted to provide a tune that meets the needs of most users, there will be applications where tuning changes are required.
For this reason, no claims for accuracy relative to the legacy combination of attitude_estimator_q + local_position_estimator have been made and the best choice of estimator will depend on the application and tuning.
- ecl EKF 是一种复杂的算法，需要很好地理解扩展卡尔曼滤波器理论及其应用于导航中的问题才能成功调整。 因此，不知道怎么修改，用户就很难得到好结果。
- Local position output data is found in the vehicle_local_position message.
- ecl EKF 使用更多的日志空间。
- ecl EKF 能够以数学上一致的方式融合来自具有不同时间延迟和数据速率的传感器的数据，一旦正确设置时间延迟参数，就可以提高动态操作期间的准确性。
- ecl EKF 能够融合各种不同的传感器类型。
- 当 ecl EKF 检测并报告传感器数据中统计上显着的不一致性，将帮助诊断传感器错误。
- 对于固定翼飞机的操作，ecl EKF 可以使用或不使用空速传感器估计风速，并且能够将估计的风速与空速测量和侧滑假设结合使用，以延长 GPS 在飞行中丢失时的航位推算时间。
- ecl EKF估计3轴加速度计偏差，这提高了尾座式无人机和其它机体在飞行阶段之间经历大的姿态变化时的精度。
- The federated architecture (combined attitude and position/velocity estimation) means that attitude estimation benefits from all sensor measurements. 如果调整正确，这应该提供改善态度估计的潜力。
EKF outputs, states and status data are published to a number of uORB topics which are logged to the SD card during flight. The following guide assumes that data has been logged using the .ulog file format. The .ulog format data can be parsed in python by using the PX4 pyulog library.
Most of the EKF data is found in the estimator_innovations and estimator_status uORB messages that are logged to the .ulog file.
A python script that automatically generates analysis plots and metadata can be found here. To use this script file, cd to the Tools/ecl_ekf directory and enter python process_logdata_ekf.py <log_file.ulg>. This saves performance metadata in a csv file named <log_file>.mdat.csv and plots in a pdf file named <log_file>.pdf.
Multiple log files in a directory can be analysed using the batch_process_logdata_ekf.py script. When this has been done, the performance metadata files can be processed to provide a statistical assessment of the estimator performance across the population of logs using the batch_process_metadata_ekf.py script.
- Attitude output data is found in the vehicle_attitude message.
- Global (WGS-84) output data is found in the vehicle_global_position message.
- Wind velocity output data is found in the wind_estimate message.
- High frequency delta velocity vibration - estimator_status.vibe[2]
Refer to states[32] in estimator_status. The index map for states[32] is as follows:
- [0 ... 3] 四元数
- [4 ... 6] 速度 NED (m/s)
- [7 ... 9] 位置 NED (m)
- [10 ... 12] IMU 增量角度偏差 XYZ (rad)
- [13 ... 15] IMU 增量速度偏差 XYZ (m/s)
- [16 ... 18] 地球磁场 NED (gauss)
- [19 ... 21] 机体磁场 XYZ (gauss)
- [22 ... 23] 风速 NE (m/s)
- [24 ... 32] 未使用
Refer to covariances[28] in estimator_status. The index map for covariances[28] is as follows:
- [0 ... 3] 四元数
- [4 ... 6] 速度 NED (m/s)^2
- [7 ... 9] 位置 NED (m^2)
- [10 ... 12] IMU 增量角度偏差 XYZ (rad^2)
- [13 ... 15] IMU 增量速度偏差 XYZ (m/s)^2
- [16 ... 18] 地球磁场 NED (gauss^2)
- [19 ... 21] 机体磁场 XYZ (gauss^2)
- [22 ... 23] 风速 NE (m/s)^2
- [24 ... 28] 未使用
The observation estimator_innovations, estimator_innovation_variances, and estimator_innovation_test_ratios message fields are defined in estimator_innovations.msg. The messages all have the same field names/types (but different units).
:::note
The messages have the same fields because they are generated from the same field definition. The # TOPICS line (at the end of the file) lists the names of the set of messages to be created):
# TOPICS estimator_innovations estimator_innovation_variances estimator_innovation_test_ratios
:::
Some of the observations are:
- [0] Angular tracking error magnitude (rad)
- 偏航角度 (rad, rad^2) :
heading - True Airspeed (m/s)^2 : Refer to airspeed_innov_var in ekf2_innovations.
- Synthetic sideslip (rad^2) : Refer to beta_innov_var in ekf2_innovations.
- Optical flow XY (rad/sec)^2 : Refer to flow_innov_var in ekf2_innovations.
- Height above ground (m^2) : Refer to hagl_innov_var in ekf2_innovations.
- 阻力比力 ((m/s)^2):
drag - 速度和位置新息：每个传感器
In addition, each sensor has its own fields for horizontal and vertical position and/or velocity values (where appropriate). These are largely self documenting, and are reproduced below:
# GPS
float32[2] gps_hvel # horizontal GPS velocity innovation (m/sec) and innovation variance ((m/sec)**2)
float32 gps_vvel # vertical GPS velocity innovation (m/sec) and innovation variance ((m/sec)**2)
float32[2] gps_hpos # horizontal GPS position innovation (m) and innovation variance (m**2)
float32 gps_vpos # vertical GPS position innovation (m) and innovation variance (m**2)
# External Vision
float32[2] ev_hvel # horizontal external vision velocity innovation (m/sec) and innovation variance ((m/sec)**2)
float32 ev_vvel # vertical external vision velocity innovation (m/sec) and innovation variance ((m/sec)**2)
float32[2] ev_hpos # horizontal external vision position innovation (m) and innovation variance (m**2)
float32 ev_vpos # vertical external vision position innovation (m) and innovation variance (m**2)
# Fake Position and Velocity
float32[2] fake_hvel # fake horizontal velocity innovation (m/s) and innovation variance ((m/s)**2)
float32 fake_vvel # fake vertical velocity innovation (m/s) and innovation variance ((m/s)**2)
float32[2] fake_hpos # fake horizontal position innovation (m) and innovation variance (m**2)
float32 fake_vpos # fake vertical position innovation (m) and innovation variance (m**2)
# Height sensors
float32 rng_vpos # range sensor height innovation (m) and innovation variance (m**2)
float32 baro_vpos # barometer height innovation (m) and innovation variance (m**2)
# Auxiliary velocity
float32[2] aux_hvel # horizontal auxiliar velocity innovation from landing target measurement (m/sec) and innovation variance ((m/sec)**2)
float32 aux_vvel # vertical auxiliar velocity innovation from landing target measurement (m/sec) and innovation variance ((m/sec)**2)
The output complementary filter is used to propagate states forward from the fusion time horizon to current time. To check the magnitude of the angular, velocity and position tracking errors measured at the fusion time horizon, refer to output_tracking_error[3] in the ekf2_innovations message.
The index map is as follows:
- [0] 角度跟踪误差幅度 (rad)
- [1] Velocity tracking error magnitude (m/s). The velocity tracking time constant can be adjusted using the EKF2_TAU_VEL parameter. 减小此参数可减少稳态误差，但会增加 NED 速度输出上的观察噪声量。
- [2] Position tracking error magnitude (m). The position tracking time constant can be adjusted using the EKF2_TAU_POS parameter. 减小此参数可减少稳态误差，但会增加 NED 位置输出上的观察噪声量。
The EKF contains internal error checking for badly conditioned state and covariance updates. Refer to the filter_fault_flags in estimator_status.
There are two categories of observation faults:
- 数据丢失。 一个例子是测距仪无法提供返回数据。
- 新息，即状态预测和传感器观察之间的差异过度。 这种情况的一个例子是过度振动导致大的垂直位置误差，导致气压计高度测量被拒绝。
Both of these can result in observation data being rejected for long enough to cause the EKF to attempt a reset of the states using the sensor observations. All observations have a statistical confidence checks applied to the innovations. The number of standard deviations for the check are controlled by the EKF2_*_GATE parameter for each observation type.
Test levels are available in estimator_status as follows:
- mag_test_ratio : ratio of the largest magnetometer innovation component to the innovation test limit
- State variances (diagonals in the covariance matrix) are constrained to be non-negative.
- pos_test_ratio : ratio of the largest horizontal position innovation component to the innovation test limit
- hgt_test_ratio : ratio of the vertical position innovation to the innovation test limit
- tas_test_ratio : ratio of the true airspeed innovation to the innovation test limit
- vel_test_ratio : ratio of the largest velocity innovation component to the innovation test limit
For a binary pass/fail summary for each sensor, refer to innovation_check_flags in estimator_status.
The EKF applies a number of GPS quality checks before commencing GPS aiding. These checks are controlled by the EKF2_GPS_CHECK and EKF2_REQ_* parameters. The pass/fail status for these checks is logged in the estimator_status.gps_check_fail_flags message. This integer will be zero when all required GPS checks have passed. If the EKF is not commencing GPS alignment, check the value of the integer against the bitmask definition gps_check_fail_flags in estimator_status.
The EKF uses single precision floating point operations for all of its computations and first order approximations for derivation of the covariance prediction and update equations in order to reduce processing requirements. This means that it is possible when re-tuning the EKF to encounter conditions where the covariance matrix operations become badly conditioned enough to cause divergence or significant errors in the state estimates.
To prevent this, every covariance and state update step contains the following error detection and correction steps:
- If the innovation variance is less than the observation variance (this requires a negative state variance which is impossible) or the covariance update will produce a negative variance for any of the states, then:
- 跳过状态和协方差更新
- 协方差矩阵中的相应行和列被重置
- The failure is recorded in the estimator_status filter_fault_flags message
- estimator_status.hgt_test_ratio will be greater than 1.0
- 状态方差应用数值上限。
- 协方差矩阵强制对称。
After re-tuning the filter, particularly re-tuning that involve reducing the noise variables, the value of estimator_status.gps_check_fail_flags should be checked to ensure that it remains zero.
The most common cause of EKF height diverging away from GPS and altimeter measurements during flight is clipping and/or aliasing of the IMU measurements caused by vibration. If this is occurring, then the following signs should be evident in the data
- ekf2_innovations.vel_pos_innov[2] and ekf2_innovations.vel_pos_innov[5] will both have the same sign.
- Plot the height innovation test ratio - estimator_status.hgt_test_ratio
The recommended first step is to ensure that the autopilot is isolated from the airframe using an effective isolation mounting system. An isolation mount has 6 degrees of freedom, and therefore 6 resonant frequencies. As a general rule, the 6 resonant frequencies of the autopilot on the isolation mount should be above 25Hz to avoid interaction with the autopilot dynamics and below the frequency of the motors.
An isolation mount can make vibration worse if the resonant frequencies coincide with motor or propeller blade passage frequencies.
The EKF can be made more resistant to vibration induced height divergence by making the following parameter changes:
- 将主要的高度传感器的新息通道的值加倍。 If using barometric height this is EKF2_BARO_GATE.
- Increase the value of EKF2_ACC_NOISE to 0.5 initially. 如果仍然出现发散，则进一步增加 0.1，但不要超过 1.0。
Note that the effect of these changes will make the EKF more sensitive to errors in GPS vertical velocity and barometric pressure.
The most common causes of position divergence are:
- 高振动级别。
- 通过改进无人机的机械隔离来解决。
- 增加 EKF2_ACC_NOISE 和 EKF2_GYR_NOISE 的值会有所帮助，但确实会使 EKF 更容易受到 GPS 故障的影响。
- 过大的陀螺仪偏差偏移。
- 通过重新校准陀螺仪来修复。 Check for excessive temperature sensitivity (> 3 deg/sec bias change during warm-up from a cold start and replace the sensor if affected of insulate to slow the rate of temperature change.
- 不好的偏航对齐
- 检查磁力计校准和对齐。
- Check the heading shown QGC is within 15 deg truth
- GPS 精度差
- 检查是否有干扰
- 改善隔离和屏蔽
- 检查飞行位置是否有 GPS 信号障碍物和反射器（附近的高层建筑）
- GPS 数据丢失
Determining which of these is the primary cause requires a methodical approach to analysis of the EKF log data:
- Plot the velocity innovation test ratio - estimator_status.vel_test_ratio
- Plot the horizontal position innovation test ratio - estimator_status.pos_test_ratio
- Plot the magnetometer innovation test ratio - estimator_status.mag_test_ratio
- Magnetometer XYZ (gauss) : Refer to mag_innov[3] in ekf2_innovations.
- 绘制 GPS 接收器报告的速度精度-vehicle_gps_position.s_variance_m_s
- Plot the IMU delta angle state estimates - estimator_status.states[10], states[11] and states[12]
- 绘制 EKF 内部高频振动指标：
- Delta angle coning vibration - estimator_status.vibe[0]
- High frequency delta angle vibration - estimator_status.vibe[1]
- 高频增量速度振动 - estimator_status.vibe[2]
During normal operation, all the test ratios should remain below 0.5 with only occasional spikes above this as shown in the example below from a successful flight:
The following plot shows the EKF vibration metrics for a multirotor with good isolation. The landing shock and the increased vibration during takeoff and landing can be seen. Insufficient data has been gathered with these metrics to provide specific advice on maximum thresholds.
The above vibration metrics are of limited value as the presence of vibration at a frequency close to the IMU sampling frequency (1 kHz for most boards) will cause offsets to appear in the data that do not show up in the high frequency vibration metrics. The only way to detect aliasing errors is in their effect on inertial navigation accuracy and the rise in innovation levels.
In addition to generating large position and velocity test ratios of > 1.0, the different error mechanisms affect the other test ratios in different ways:
High vibration levels normally affect vertical position and velocity innovations as well as the horizontal components. Magnetometer test levels are only affected to a small extent.
(insert example plots showing bad vibration here)
Large gyro bias offsets are normally characterised by a change in the value of delta angle bias greater than 5E-4 during flight (equivalent to ~3 deg/sec) and can also cause a large increase in the magnetometer test ratio if the yaw axis is affected. Height is normally unaffected other than extreme cases. Switch on bias value of up to 5 deg/sec can be tolerated provided the filter is given time settle before flying. Pre-flight checks performed by the commander should prevent arming if the position is diverging.
(insert example plots showing bad gyro bias here)
Bad yaw alignment causes a velocity test ratio that increases rapidly when the vehicle starts moving due inconsistency in the direction of velocity calculated by the inertial nav and the GPS measurement. Magnetometer innovations are slightly affected. Height is normally unaffected.
(insert example plots showing bad yaw alignment here)
Poor GPS accuracy is normally accompanied by a rise in the reported velocity error of the receiver in conjunction with a rise in innovations. Transient errors due to multipath, obscuration and interference are more common causes. Here is an example of a temporary loss of GPS accuracy where the multi-rotor started drifting away from its loiter location and had to be corrected using the sticks. The rise in estimator_status.vel_test_ratio to greater than 1 indicates the GPs velocity was inconsistent with other measurements and has been rejected.
This is accompanied with rise in the GPS receivers reported velocity accuracy which indicates that it was likely a GPS error.
If we also look at the GPS horizontal velocity innovations and innovation variances, we can see the large spike in North velocity innovation that accompanies this GPS 'glitch' event.
Loss of GPS data will be shown by the velocity and position innovation test ratios 'flat-lining'. If this occurs, check the other GPS status data in vehicle_gps_position for further information.
The following plot shows the NED GPS velocity innovations ekf2_innovations_0.vel_pos_innov[0 ... 2], the GPS NE position innovations ekf2_innovations_0.vel_pos_innov[3 ... 4] and the Baro vertical position innovation ekf2_innovations_0.vel_pos_innov[5] generated from a simulated VTOL flight using SITL Gazebo.
The simulated GPS was made to lose lock at 73 seconds. Note the NED velocity innovations and NE position innovations 'flat-line' after GPS is lost. Note that after 10 seconds without GPS data, the EKF reverts back to a static position mode using the last known position and the NE position innovations start to change again.
If the vehicle has the tendency during landing to climb back into the air when close to the ground, the most likely cause is barometer ground effect.
This is caused when air pushed down by the propellers hits the ground and creates a high pressure zone below the drone. The result is a lower reading of pressure altitude, leading to an unwanted climb being commanded. The figure below shows a typical situation where the ground effect is present. Note how the barometer signal dips at the beginning and end of the flight.
You can enable ground effect compensation to fix this problem:
- 从绘图中估算出气压计在起飞或着陆期间的跌落程度。 在上面的绘图中，人们可以看到降落过程中大约6米的气压计下沉。
- 然后将参数 EKF2_GND_EFF_DZ 设置为该值，并添加 10% 的余量值。 因此，在这种情况下，6.6米的数值将是一个良好的起点。
If a terrain estimate is available (e.g. the vehicle is equipped with a range finder) then you can additionally specify EKF2_GND_MAX_HGT, the above ground-level altitude below which ground effect compensation should be activated. If no terrain estimate is available this parameter will have no effect and the system will use heuristics to determine if ground effect compensation should be activated.
- PX4 状态估计概述视频，PX4 开发者峰会 2019， (Dr. Paul Riseborough) 对状态估计器进行了概述，并且还进一步介绍了2018/19年以来的重大变化以及2019年期间计划的改进措施。