490 lines
19 KiB
C
490 lines
19 KiB
C
/**
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******************************************************************************
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* @file QuaternionEKF.c
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* @author Wang Hongxi
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* @version V1.2.0
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* @date 2022/3/8
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* @brief attitude update with gyro bias estimate and chi-square test
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******************************************************************************
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* @attention
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* 1st order LPF transfer function:
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* 1
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* ———————
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* as + 1
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*
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******************************************************************************
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*/
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#include "QuaternionEKF.h"
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QEKF_INS_t QEKF_INS;
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const float IMU_QuaternionEKF_F[36] = {1, 0, 0, 0, 0, 0,
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0, 1, 0, 0, 0, 0,
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0, 0, 1, 0, 0, 0,
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0, 0, 0, 1, 0, 0,
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0, 0, 0, 0, 1, 0,
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0, 0, 0, 0, 0, 1};
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float IMU_QuaternionEKF_P[36] = {100000, 0.1, 0.1, 0.1, 0.1, 0.1,
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0.1, 100000, 0.1, 0.1, 0.1, 0.1,
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0.1, 0.1, 100000, 0.1, 0.1, 0.1,
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0.1, 0.1, 0.1, 100000, 0.1, 0.1,
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0.1, 0.1, 0.1, 0.1, 100, 0.1,
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0.1, 0.1, 0.1, 0.1, 0.1, 100};
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float IMU_QuaternionEKF_K[18];
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float IMU_QuaternionEKF_H[18];
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static float invSqrt(float x);
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static void IMU_QuaternionEKF_Observe(KalmanFilter_t *kf);
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static void IMU_QuaternionEKF_F_Linearization_P_Fading(KalmanFilter_t *kf);
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static void IMU_QuaternionEKF_SetH(KalmanFilter_t *kf);
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static void IMU_QuaternionEKF_xhatUpdate(KalmanFilter_t *kf);
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/**
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* @brief Quaternion EKF initialization and some reference value
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* @param[in] process_noise1 quaternion process noise 10
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* @param[in] process_noise2 gyro bias process noise 0.001
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* @param[in] measure_noise accel measure noise 1000000
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* @param[in] lambda fading coefficient 0.9996
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* @param[in] lpf lowpass filter coefficient 0
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*/
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void IMU_QuaternionEKF_Init(float* init_quaternion,float process_noise1, float process_noise2, float measure_noise, float lambda, float lpf)
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{
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QEKF_INS.Initialized = 1;
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QEKF_INS.Q1 = process_noise1;
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QEKF_INS.Q2 = process_noise2;
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QEKF_INS.R = measure_noise;
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QEKF_INS.ChiSquareTestThreshold = 1e-8;
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QEKF_INS.ConvergeFlag = 0;
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QEKF_INS.ErrorCount = 0;
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QEKF_INS.UpdateCount = 0;
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if (lambda > 1)
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{
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lambda = 1;
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}
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QEKF_INS.lambda = lambda;
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QEKF_INS.accLPFcoef = lpf;
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// 初始化矩阵维度信息
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Kalman_Filter_Init(&QEKF_INS.IMU_QuaternionEKF, 6, 0, 3);
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Matrix_Init(&QEKF_INS.ChiSquare, 1, 1, (float *)QEKF_INS.ChiSquare_Data);
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// 姿态初始化
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for(int i = 0; i < 4; i++)
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{
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QEKF_INS.IMU_QuaternionEKF.xhat_data[i] = init_quaternion[i];
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}
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// 自定义函数初始化,用于扩展或增加kf的基础功能
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QEKF_INS.IMU_QuaternionEKF.User_Func0_f = IMU_QuaternionEKF_Observe;
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QEKF_INS.IMU_QuaternionEKF.User_Func1_f = IMU_QuaternionEKF_F_Linearization_P_Fading;
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QEKF_INS.IMU_QuaternionEKF.User_Func2_f = IMU_QuaternionEKF_SetH;
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QEKF_INS.IMU_QuaternionEKF.User_Func3_f = IMU_QuaternionEKF_xhatUpdate;
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// 设定标志位,用自定函数替换kf标准步骤中的SetK(计算增益)以及xhatupdate(后验估计/融合)
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QEKF_INS.IMU_QuaternionEKF.SkipEq3 = TRUE;
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QEKF_INS.IMU_QuaternionEKF.SkipEq4 = TRUE;
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memcpy(QEKF_INS.IMU_QuaternionEKF.F_data, IMU_QuaternionEKF_F, sizeof(IMU_QuaternionEKF_F));
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memcpy(QEKF_INS.IMU_QuaternionEKF.P_data, IMU_QuaternionEKF_P, sizeof(IMU_QuaternionEKF_P));
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}
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/**
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* @brief Quaternion EKF update
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* @param[in] gyro x y z in rad/s
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* @param[in] accel x y z in m/s²
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* @param[in] update period in s
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*/
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void IMU_QuaternionEKF_Update(float gx, float gy, float gz, float ax, float ay, float az, float dt)
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{
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// 0.5(Ohm-Ohm^bias)*deltaT,用于更新工作点处的状态转移F矩阵
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static float halfgxdt, halfgydt, halfgzdt;
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static float accelInvNorm;
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/* F, number with * represent vals to be set
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0 1* 2* 3* 4 5
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6* 7 8* 9* 10 11
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12* 13* 14 15* 16 17
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18* 19* 20* 21 22 23
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24 25 26 27 28 29
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30 31 32 33 34 35
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*/
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QEKF_INS.dt = dt;
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QEKF_INS.Gyro[0] = gx - QEKF_INS.GyroBias[0];
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QEKF_INS.Gyro[1] = gy - QEKF_INS.GyroBias[1];
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QEKF_INS.Gyro[2] = gz - QEKF_INS.GyroBias[2];
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// set F
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halfgxdt = 0.5f * QEKF_INS.Gyro[0] * dt;
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halfgydt = 0.5f * QEKF_INS.Gyro[1] * dt;
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halfgzdt = 0.5f * QEKF_INS.Gyro[2] * dt;
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// 此部分设定状态转移矩阵F的左上角部分 4x4子矩阵,即0.5(Ohm-Ohm^bias)*deltaT,右下角有一个2x2单位阵已经初始化好了
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// 注意在predict步F的右上角是4x2的零矩阵,因此每次predict的时候都会调用memcpy用单位阵覆盖前一轮线性化后的矩阵
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memcpy(QEKF_INS.IMU_QuaternionEKF.F_data, IMU_QuaternionEKF_F, sizeof(IMU_QuaternionEKF_F));
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QEKF_INS.IMU_QuaternionEKF.F_data[1] = -halfgxdt;
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QEKF_INS.IMU_QuaternionEKF.F_data[2] = -halfgydt;
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QEKF_INS.IMU_QuaternionEKF.F_data[3] = -halfgzdt;
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QEKF_INS.IMU_QuaternionEKF.F_data[6] = halfgxdt;
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QEKF_INS.IMU_QuaternionEKF.F_data[8] = halfgzdt;
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QEKF_INS.IMU_QuaternionEKF.F_data[9] = -halfgydt;
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QEKF_INS.IMU_QuaternionEKF.F_data[12] = halfgydt;
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QEKF_INS.IMU_QuaternionEKF.F_data[13] = -halfgzdt;
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QEKF_INS.IMU_QuaternionEKF.F_data[15] = halfgxdt;
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QEKF_INS.IMU_QuaternionEKF.F_data[18] = halfgzdt;
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QEKF_INS.IMU_QuaternionEKF.F_data[19] = halfgydt;
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QEKF_INS.IMU_QuaternionEKF.F_data[20] = -halfgxdt;
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// accel low pass filter,加速度过一下低通滤波平滑数据,降低撞击和异常的影响
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if (QEKF_INS.UpdateCount == 0) // 如果是第一次进入,需要初始化低通滤波
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{
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QEKF_INS.Accel[0] = ax;
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QEKF_INS.Accel[1] = ay;
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QEKF_INS.Accel[2] = az;
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}
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QEKF_INS.Accel[0] = QEKF_INS.Accel[0] * QEKF_INS.accLPFcoef / (QEKF_INS.dt + QEKF_INS.accLPFcoef) + ax * QEKF_INS.dt / (QEKF_INS.dt + QEKF_INS.accLPFcoef);
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QEKF_INS.Accel[1] = QEKF_INS.Accel[1] * QEKF_INS.accLPFcoef / (QEKF_INS.dt + QEKF_INS.accLPFcoef) + ay * QEKF_INS.dt / (QEKF_INS.dt + QEKF_INS.accLPFcoef);
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QEKF_INS.Accel[2] = QEKF_INS.Accel[2] * QEKF_INS.accLPFcoef / (QEKF_INS.dt + QEKF_INS.accLPFcoef) + az * QEKF_INS.dt / (QEKF_INS.dt + QEKF_INS.accLPFcoef);
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// set z,单位化重力加速度向量
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accelInvNorm = invSqrt(QEKF_INS.Accel[0] * QEKF_INS.Accel[0] + QEKF_INS.Accel[1] * QEKF_INS.Accel[1] + QEKF_INS.Accel[2] * QEKF_INS.Accel[2]);
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for (uint8_t i = 0; i < 3; ++i)
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{
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QEKF_INS.IMU_QuaternionEKF.MeasuredVector[i] = QEKF_INS.Accel[i] * accelInvNorm; // 用加速度向量更新量测值
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}
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// get body state
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QEKF_INS.gyro_norm = 1.0f / invSqrt(QEKF_INS.Gyro[0] * QEKF_INS.Gyro[0] +
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QEKF_INS.Gyro[1] * QEKF_INS.Gyro[1] +
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QEKF_INS.Gyro[2] * QEKF_INS.Gyro[2]);
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QEKF_INS.accl_norm = 1.0f / accelInvNorm;
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// 如果角速度小于阈值且加速度处于设定范围内,认为运动稳定,加速度可以用于修正角速度
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// 稍后在最后的姿态更新部分会利用StableFlag来确定
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if (QEKF_INS.gyro_norm < 0.3f && QEKF_INS.accl_norm > 9.8f - 0.5f && QEKF_INS.accl_norm < 9.8f + 0.5f)
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{
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QEKF_INS.StableFlag = 1;
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}
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else
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{
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QEKF_INS.StableFlag = 0;
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}
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// set Q R,过程噪声和观测噪声矩阵
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QEKF_INS.IMU_QuaternionEKF.Q_data[0] = QEKF_INS.Q1 * QEKF_INS.dt;
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QEKF_INS.IMU_QuaternionEKF.Q_data[7] = QEKF_INS.Q1 * QEKF_INS.dt;
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QEKF_INS.IMU_QuaternionEKF.Q_data[14] = QEKF_INS.Q1 * QEKF_INS.dt;
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QEKF_INS.IMU_QuaternionEKF.Q_data[21] = QEKF_INS.Q1 * QEKF_INS.dt;
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QEKF_INS.IMU_QuaternionEKF.Q_data[28] = QEKF_INS.Q2 * QEKF_INS.dt;
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QEKF_INS.IMU_QuaternionEKF.Q_data[35] = QEKF_INS.Q2 * QEKF_INS.dt;
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QEKF_INS.IMU_QuaternionEKF.R_data[0] = QEKF_INS.R;
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QEKF_INS.IMU_QuaternionEKF.R_data[4] = QEKF_INS.R;
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QEKF_INS.IMU_QuaternionEKF.R_data[8] = QEKF_INS.R;
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// 调用kalman_filter.c封装好的函数,注意几个User_Funcx_f的调用
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Kalman_Filter_Update(&QEKF_INS.IMU_QuaternionEKF);
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// 获取融合后的数据,包括四元数和xy零飘值
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QEKF_INS.q[0] = QEKF_INS.IMU_QuaternionEKF.FilteredValue[0];
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QEKF_INS.q[1] = QEKF_INS.IMU_QuaternionEKF.FilteredValue[1];
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QEKF_INS.q[2] = QEKF_INS.IMU_QuaternionEKF.FilteredValue[2];
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QEKF_INS.q[3] = QEKF_INS.IMU_QuaternionEKF.FilteredValue[3];
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QEKF_INS.GyroBias[0] = QEKF_INS.IMU_QuaternionEKF.FilteredValue[4];
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QEKF_INS.GyroBias[1] = QEKF_INS.IMU_QuaternionEKF.FilteredValue[5];
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QEKF_INS.GyroBias[2] = 0; // 大部分时候z轴通天,无法观测yaw的漂移
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// 利用四元数反解欧拉角
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QEKF_INS.Yaw = atan2f(2.0f * (QEKF_INS.q[0] * QEKF_INS.q[3] + QEKF_INS.q[1] * QEKF_INS.q[2]), 2.0f * (QEKF_INS.q[0] * QEKF_INS.q[0] + QEKF_INS.q[1] * QEKF_INS.q[1]) - 1.0f) * 57.295779513f;
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QEKF_INS.Pitch = atan2f(2.0f * (QEKF_INS.q[0] * QEKF_INS.q[1] + QEKF_INS.q[2] * QEKF_INS.q[3]), 2.0f * (QEKF_INS.q[0] * QEKF_INS.q[0] + QEKF_INS.q[3] * QEKF_INS.q[3]) - 1.0f) * 57.295779513f;
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QEKF_INS.Roll = asinf(-2.0f * (QEKF_INS.q[1] * QEKF_INS.q[3] - QEKF_INS.q[0] * QEKF_INS.q[2])) * 57.295779513f;
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// get Yaw total, yaw数据可能会超过360,处理一下方便其他功能使用(如小陀螺)
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if (QEKF_INS.Yaw - QEKF_INS.YawAngleLast > 180.0f)
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{
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QEKF_INS.YawRoundCount--;
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}
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else if (QEKF_INS.Yaw - QEKF_INS.YawAngleLast < -180.0f)
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{
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QEKF_INS.YawRoundCount++;
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}
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QEKF_INS.YawTotalAngle = 360.0f * QEKF_INS.YawRoundCount + QEKF_INS.Yaw;
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QEKF_INS.YawAngleLast = QEKF_INS.Yaw;
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QEKF_INS.UpdateCount++; // 初始化低通滤波用,计数测试用
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}
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/**
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* @brief 用于更新线性化后的状态转移矩阵F右上角的一个4x2分块矩阵,稍后用于协方差矩阵P的更新;
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* 并对零漂的方差进行限制,防止过度收敛并限幅防止发散
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*
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* @param kf
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*/
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static void IMU_QuaternionEKF_F_Linearization_P_Fading(KalmanFilter_t *kf)
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{
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static float q0, q1, q2, q3;
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static float qInvNorm;
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q0 = kf->xhatminus_data[0];
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q1 = kf->xhatminus_data[1];
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q2 = kf->xhatminus_data[2];
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q3 = kf->xhatminus_data[3];
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// quaternion normalize
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qInvNorm = invSqrt(q0 * q0 + q1 * q1 + q2 * q2 + q3 * q3);
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for (uint8_t i = 0; i < 4; ++i)
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{
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kf->xhatminus_data[i] *= qInvNorm;
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}
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/* F, number with * represent vals to be set
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0 1 2 3 4* 5*
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6 7 8 9 10* 11*
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12 13 14 15 16* 17*
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18 19 20 21 22* 23*
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24 25 26 27 28 29
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30 31 32 33 34 35
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*/
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// set F
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kf->F_data[4] = q1 * QEKF_INS.dt / 2;
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kf->F_data[5] = q2 * QEKF_INS.dt / 2;
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kf->F_data[10] = -q0 * QEKF_INS.dt / 2;
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kf->F_data[11] = q3 * QEKF_INS.dt / 2;
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kf->F_data[16] = -q3 * QEKF_INS.dt / 2;
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kf->F_data[17] = -q0 * QEKF_INS.dt / 2;
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kf->F_data[22] = q2 * QEKF_INS.dt / 2;
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kf->F_data[23] = -q1 * QEKF_INS.dt / 2;
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// fading filter,防止零飘参数过度收敛
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kf->P_data[28] /= QEKF_INS.lambda;
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kf->P_data[35] /= QEKF_INS.lambda;
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// 限幅,防止发散
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if (kf->P_data[28] > 10000)
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{
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kf->P_data[28] = 10000;
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}
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if (kf->P_data[35] > 10000)
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{
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kf->P_data[35] = 10000;
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}
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}
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/**
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* @brief 在工作点处计算观测函数h(x)的Jacobi矩阵H
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*
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* @param kf
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*/
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static void IMU_QuaternionEKF_SetH(KalmanFilter_t *kf)
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{
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static float doubleq0, doubleq1, doubleq2, doubleq3;
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/* H
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0 1 2 3 4 5
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6 7 8 9 10 11
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12 13 14 15 16 17
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last two cols are zero
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*/
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// set H
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doubleq0 = 2 * kf->xhatminus_data[0];
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doubleq1 = 2 * kf->xhatminus_data[1];
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doubleq2 = 2 * kf->xhatminus_data[2];
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doubleq3 = 2 * kf->xhatminus_data[3];
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memset(kf->H_data, 0, sizeof_float * kf->zSize * kf->xhatSize);
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kf->H_data[0] = -doubleq2;
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kf->H_data[1] = doubleq3;
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kf->H_data[2] = -doubleq0;
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kf->H_data[3] = doubleq1;
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kf->H_data[6] = doubleq1;
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kf->H_data[7] = doubleq0;
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kf->H_data[8] = doubleq3;
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kf->H_data[9] = doubleq2;
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kf->H_data[12] = doubleq0;
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kf->H_data[13] = -doubleq1;
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kf->H_data[14] = -doubleq2;
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kf->H_data[15] = doubleq3;
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}
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/**
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* @brief 利用观测值和先验估计得到最优的后验估计
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* 加入了卡方检验以判断融合加速度的条件是否满足
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* 同时引入发散保护保证恶劣工况下的必要量测更新
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*
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* @param kf
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*/
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static void IMU_QuaternionEKF_xhatUpdate(KalmanFilter_t *kf)
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{
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static float q0, q1, q2, q3;
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kf->MatStatus = Matrix_Transpose(&kf->H, &kf->HT); // z|x => x|z
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kf->temp_matrix.numRows = kf->H.numRows;
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kf->temp_matrix.numCols = kf->Pminus.numCols;
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kf->MatStatus = Matrix_Multiply(&kf->H, &kf->Pminus, &kf->temp_matrix); // temp_matrix = H·P'(k)
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kf->temp_matrix1.numRows = kf->temp_matrix.numRows;
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kf->temp_matrix1.numCols = kf->HT.numCols;
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kf->MatStatus = Matrix_Multiply(&kf->temp_matrix, &kf->HT, &kf->temp_matrix1); // temp_matrix1 = H·P'(k)·HT
|
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kf->S.numRows = kf->R.numRows;
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kf->S.numCols = kf->R.numCols;
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kf->MatStatus = Matrix_Add(&kf->temp_matrix1, &kf->R, &kf->S); // S = H P'(k) HT + R
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kf->MatStatus = Matrix_Inverse(&kf->S, &kf->temp_matrix1); // temp_matrix1 = inv(H·P'(k)·HT + R)
|
|
|
|
q0 = kf->xhatminus_data[0];
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q1 = kf->xhatminus_data[1];
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q2 = kf->xhatminus_data[2];
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q3 = kf->xhatminus_data[3];
|
|
|
|
kf->temp_vector.numRows = kf->H.numRows;
|
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kf->temp_vector.numCols = 1;
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// 计算预测得到的重力加速度方向(通过姿态获取的)
|
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kf->temp_vector_data[0] = 2 * (q1 * q3 - q0 * q2);
|
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kf->temp_vector_data[1] = 2 * (q0 * q1 + q2 * q3);
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kf->temp_vector_data[2] = q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3; // temp_vector = h(xhat'(k))
|
|
|
|
// 计算预测值和各个轴的方向余弦
|
|
for (uint8_t i = 0; i < 3; ++i)
|
|
{
|
|
QEKF_INS.OrientationCosine[i] = acosf(fabsf(kf->temp_vector_data[i]));
|
|
}
|
|
|
|
// 利用加速度计数据修正
|
|
kf->temp_vector1.numRows = kf->z.numRows;
|
|
kf->temp_vector1.numCols = 1;
|
|
kf->MatStatus = Matrix_Subtract(&kf->z, &kf->temp_vector, &kf->temp_vector1); // temp_vector1 = z(k) - h(xhat'(k))
|
|
|
|
// chi-square test,卡方检验
|
|
kf->temp_matrix.numRows = kf->temp_vector1.numRows;
|
|
kf->temp_matrix.numCols = 1;
|
|
kf->MatStatus = Matrix_Multiply(&kf->temp_matrix1, &kf->temp_vector1, &kf->temp_matrix); // temp_matrix = inv(H·P'(k)·HT + R)·(z(k) - h(xhat'(k)))
|
|
kf->temp_vector.numRows = 1;
|
|
kf->temp_vector.numCols = kf->temp_vector1.numRows;
|
|
kf->MatStatus = Matrix_Transpose(&kf->temp_vector1, &kf->temp_vector); // temp_vector = z(k) - h(xhat'(k))'
|
|
kf->MatStatus = Matrix_Multiply(&kf->temp_vector, &kf->temp_matrix, &QEKF_INS.ChiSquare);
|
|
// rk is small,filter converged/converging
|
|
if (QEKF_INS.ChiSquare_Data[0] < 0.5f * QEKF_INS.ChiSquareTestThreshold)
|
|
{
|
|
QEKF_INS.ConvergeFlag = 1;
|
|
}
|
|
// rk is bigger than thre but once converged
|
|
if (QEKF_INS.ChiSquare_Data[0] > QEKF_INS.ChiSquareTestThreshold && QEKF_INS.ConvergeFlag)
|
|
{
|
|
if (QEKF_INS.StableFlag)
|
|
{
|
|
QEKF_INS.ErrorCount++; // 载体静止时仍无法通过卡方检验
|
|
}
|
|
else
|
|
{
|
|
QEKF_INS.ErrorCount = 0;
|
|
}
|
|
|
|
if (QEKF_INS.ErrorCount > 50)
|
|
{
|
|
// 滤波器发散
|
|
QEKF_INS.ConvergeFlag = 0;
|
|
kf->SkipEq5 = FALSE; // step-5 is cov mat P updating
|
|
}
|
|
else
|
|
{
|
|
// 残差未通过卡方检验 仅预测
|
|
// xhat(k) = xhat'(k)
|
|
// P(k) = P'(k)
|
|
memcpy(kf->xhat_data, kf->xhatminus_data, sizeof_float * kf->xhatSize);
|
|
memcpy(kf->P_data, kf->Pminus_data, sizeof_float * kf->xhatSize * kf->xhatSize);
|
|
kf->SkipEq5 = TRUE; // part5 is P updating
|
|
return;
|
|
}
|
|
}
|
|
else // if divergent or rk is not that big/acceptable,use adaptive gain
|
|
{
|
|
// scale adaptive,rk越小则增益越大,否则更相信预测值
|
|
if (QEKF_INS.ChiSquare_Data[0] > 0.1f * QEKF_INS.ChiSquareTestThreshold && QEKF_INS.ConvergeFlag)
|
|
{
|
|
QEKF_INS.AdaptiveGainScale = (QEKF_INS.ChiSquareTestThreshold - QEKF_INS.ChiSquare_Data[0]) / (0.9f * QEKF_INS.ChiSquareTestThreshold);
|
|
}
|
|
else
|
|
{
|
|
QEKF_INS.AdaptiveGainScale = 1;
|
|
}
|
|
QEKF_INS.ErrorCount = 0;
|
|
kf->SkipEq5 = FALSE;
|
|
}
|
|
|
|
// cal kf-gain K
|
|
kf->temp_matrix.numRows = kf->Pminus.numRows;
|
|
kf->temp_matrix.numCols = kf->HT.numCols;
|
|
kf->MatStatus = Matrix_Multiply(&kf->Pminus, &kf->HT, &kf->temp_matrix); // temp_matrix = P'(k)·HT
|
|
kf->MatStatus = Matrix_Multiply(&kf->temp_matrix, &kf->temp_matrix1, &kf->K);
|
|
|
|
// implement adaptive
|
|
for (uint8_t i = 0; i < kf->K.numRows * kf->K.numCols; ++i)
|
|
{
|
|
kf->K_data[i] *= QEKF_INS.AdaptiveGainScale;
|
|
}
|
|
for (uint8_t i = 4; i < 6; ++i)
|
|
{
|
|
for (uint8_t j = 0; j < 3; ++j)
|
|
{
|
|
kf->K_data[i * 3 + j] *= QEKF_INS.OrientationCosine[i - 4] / 1.5707963f; // 1 rad
|
|
}
|
|
}
|
|
|
|
kf->temp_vector.numRows = kf->K.numRows;
|
|
kf->temp_vector.numCols = 1;
|
|
kf->MatStatus = Matrix_Multiply(&kf->K, &kf->temp_vector1, &kf->temp_vector); // temp_vector = K(k)·(z(k) - H·xhat'(k))
|
|
|
|
// 零漂修正限幅,一般不会有过大的漂移
|
|
if (QEKF_INS.ConvergeFlag)
|
|
{
|
|
for (uint8_t i = 4; i < 6; ++i)
|
|
{
|
|
if (kf->temp_vector.pData[i] > 1e-2f * QEKF_INS.dt)
|
|
{
|
|
kf->temp_vector.pData[i] = 1e-2f * QEKF_INS.dt;
|
|
}
|
|
if (kf->temp_vector.pData[i] < -1e-2f * QEKF_INS.dt)
|
|
{
|
|
kf->temp_vector.pData[i] = -1e-2f * QEKF_INS.dt;
|
|
}
|
|
}
|
|
}
|
|
|
|
// 不修正yaw轴数据
|
|
kf->temp_vector.pData[3] = 0;
|
|
kf->MatStatus = Matrix_Add(&kf->xhatminus, &kf->temp_vector, &kf->xhat);
|
|
}
|
|
|
|
/**
|
|
* @brief EKF观测环节,其实就是把数据复制一下
|
|
*
|
|
* @param kf kf类型定义
|
|
*/
|
|
static void IMU_QuaternionEKF_Observe(KalmanFilter_t *kf)
|
|
{
|
|
memcpy(IMU_QuaternionEKF_P, kf->P_data, sizeof(IMU_QuaternionEKF_P));
|
|
memcpy(IMU_QuaternionEKF_K, kf->K_data, sizeof(IMU_QuaternionEKF_K));
|
|
memcpy(IMU_QuaternionEKF_H, kf->H_data, sizeof(IMU_QuaternionEKF_H));
|
|
}
|
|
|
|
/**
|
|
* @brief 自定义1/sqrt(x),速度更快
|
|
*
|
|
* @param x x
|
|
* @return float
|
|
*/
|
|
static float invSqrt(float x)
|
|
{
|
|
float halfx = 0.5f * x;
|
|
float y = x;
|
|
long i = *(long *)&y;
|
|
i = 0x5f375a86 - (i >> 1);
|
|
y = *(float *)&i;
|
|
y = y * (1.5f - (halfx * y * y));
|
|
return y;
|
|
}
|