提交更新

src/BaseTracker/BaseTrack.h

@@ -63,5 +63,5 @@ public:
     int m_updateValidCount = 3;//更新多少次认为是一个有效数据。
 
 
-    std::shared_ptr<mytracker_f::KalmanFilter> kf_ = nullptr;
+    std::shared_ptr<mytracker_f::KalmanFilter_f> kf_ = nullptr;
 };

src/BaseTracker/Track2D.cpp

@@ -3,7 +3,7 @@
 
 Track2D::Track2D():BaseTrack(8, 4)
 {
-    std::shared_ptr<mytracker_f::KalmanFilter> kf = std::make_shared<mytracker::KalmanFilter>(8, 4);
+    std::shared_ptr<mytracker_f::KalmanFilter_f> kf = std::make_shared<mytracker_f::KalmanFilter_f>(8, 4);
     kf->F_ <<
         1, 0, 0, 0, 1, 0, 0, 0,
         0, 1, 0, 0, 0, 1, 0, 0,

src/BaseTracker/kalman_filter_f.cpp

@@ -2,7 +2,7 @@
 
 namespace mytracker_f {
 
-KalmanFilter::KalmanFilter(unsigned int num_states, unsigned int num_obs) :
+KalmanFilter_f::KalmanFilter_f(unsigned int num_states, unsigned int num_obs) :
         num_states_(num_states), num_obs_(num_obs) {
     /*** Predict ***/
     // State vector
@@ -33,25 +33,25 @@ KalmanFilter::KalmanFilter(unsigned int num_states, unsigned int num_obs) :
 }
 
 
-void KalmanFilter::Coast() {
+void KalmanFilter_f::Coast() {
     x_predict_ = F_ * x_;
     P_predict_ = F_ * P_ * F_.transpose() + Q_;
 }
 
 
-void KalmanFilter::Predict() {
+void KalmanFilter_f::Predict() {
     Coast();
     x_ = x_predict_;
     P_ = P_predict_;
 }
 
 
-Eigen::VectorXf KalmanFilter::PredictionToObservation(const Eigen::VectorXf &state) {
+Eigen::VectorXf KalmanFilter_f::PredictionToObservation(const Eigen::VectorXf &state) {
     return (H_*state);
 }
 
 
-void KalmanFilter::Update(const Eigen::VectorXf& z) {
+void KalmanFilter_f::Update(const Eigen::VectorXf& z) {
     Eigen::VectorXf z_predict = PredictionToObservation(x_predict_);
 
     // y - innovation, z - real observation, z_predict - predicted observation
@@ -98,7 +98,7 @@ void KalmanFilter::Update(const Eigen::VectorXf& z) {
 }
 
 
-float KalmanFilter::CalculateLogLikelihood(const Eigen::VectorXf& y, const Eigen::MatrixXf& S) {
+float KalmanFilter_f::CalculateLogLikelihood(const Eigen::VectorXf& y, const Eigen::MatrixXf& S) {
     float log_likelihood;
 
     // Note: Computing log(M.determinant()) in Eigen C++ is risky for large matrices since it may overflow or underflow.

src/BaseTracker/kalman_filter_f.h

@@ -7,7 +7,7 @@ namespace mytracker_f {
 
 // abstract class for Kalman filter
 // implementation could be KF/EKF/UKF...
-class KalmanFilter {
+class KalmanFilter_f {
 public:
     /**
      * user need to define H matrix & R matrix
@@ -15,10 +15,10 @@ public:
      * @param num_obs
      */
     // constructor
-    explicit KalmanFilter(unsigned int num_states, unsigned int num_obs);
+    explicit KalmanFilter_f(unsigned int num_states, unsigned int num_obs);
 
     // destructor
-    virtual ~KalmanFilter() = default;
+    virtual ~KalmanFilter_f() = default;
 
     /**
      * Coast state and state covariance using the process model