Multitarget Tracking with Lidar and Stereo Vision Based on Probabilistic Data Association FIR Filter

A key function of multitarget tracking (MTT) is the state estimation of unknown targets. In this paper, we propose a new state estimator, the probabilistic data association finite impulse response filter (PDAFIRF), which is designed to overcome the drawbacks of the existing state estimators used for MTT. Because the existing state estimation methods used for MTT employ all past measurements, they may exhibit poor performance because of the accumulated errors caused by modeling uncertainties and numerical errors. To overcome the weaknesses of the existing methods, the proposed PDAFIRF uses recent finite measurements, and therefore, can prevent accumulated errors. The proposed PDAFIRF requires recent finite measurements from lidar and stereo vision and processes data in a probabilistic manner. Owing to its FIR-type structure, the PDAFIRF overcomes the structural and conditional defects of the existing stochastic filters. An MTT algorithm employing the PDAFIRF and lidar and stereo vision data is developed for multi-object tracking and target information estimation with high accuracy. The fusion of lidar and stereo vision sensor data is provided for the PDAFIRF. For verifying the high accuracy of the PDAFIRF-based MTT, a simulation of tracking eight objects under fast-varying conditions is conducted. An experimental test is performed using lidar and stereo vision for tracking three pedestrians. It is demonstrated that the proposed PDAFIRF-based MTT considerably enhances the tracking performance compared to the existing Kalman filter-, unscented Kalman filter-, and particle filter-based algorithms.