A New Adaptive Noise Covariance Matrices Estimation and Filtering Method: Application to Multi-Object Tracking

TL;DR

This paper introduces an adaptive method for online estimation and correction of noise covariance matrices in Kalman filters, enhancing multi-object tracking accuracy in lidar applications under varying conditions.

Contribution

It presents a novel closed-loop estimation approach that decomposes noise covariance, estimates its components, and adaptively corrects noise intensity online, validated through simulations and real-world lidar tracking.

Findings

Improves Kalman filter performance in dynamic noise environments.

Outperforms existing methods on the KITTI pedestrian tracking benchmark.

Proves convergence and unbiasedness of the proposed estimation method.

Abstract

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.