  Kalman filters are widely used for object tracking, where process and
measurement noise are usually considered accurately known and constant.
However, the exact known and constant assumptions do not always hold in
practice. For example, when lidar is used to track noncooperative targets, the
measurement noise is different under different distances and weather
conditions. In addition, the process noise changes with the object's motion
state, especially when the tracking object is a pedestrian, and the process
noise changes more frequently. This paper proposes a new
estimation-calibration-correction closed-loop estimation method to estimate the
Kalman filter process and measurement noise covariance matrices online. First,
we decompose the noise covariance matrix into an element distribution matrix
and noise intensity and improve the Sage filter to estimate the element
distribution matrix. Second, we propose a calibration method to accurately
diagnose the noise intensity deviation. We then propose a correct method to
adaptively correct the noise intensity online. Third, under the assumption that
the system is detectable, the unbiased and convergence of the proposed method
is mathematically proven. Simulation results prove the effectiveness and
reliability of the proposed method. Finally, we apply the proposed method to
multiobject tracking of lidar and evaluate it on the official KITTI server. The
proposed method on the KITTI pedestrian multiobject tracking leaderboard
(http://www.cvlibs.net/datasets /kitti/eval_tracking.php) surpasses all
existing methods using lidar, proving the feasibility of the method in
practical applications. This work provides a new way to improve the performance
of the Kalman filter and multiobject tracking.
