Anti-clutter Gaussian Inverse Wishart PHD Filter for Extended Target Tracking.

Yuan Huang,Wei An,Xueying Wang,Liping Wang

doi:10.3390/s19235140

Abstract

The extended target Gaussian inverse Wishart probability hypothesis density (ET-GIW-PHD) filter overestimates the number of targets under high clutter density. The reason for this is that the source of measurements cannot be determined correctly if only the number of measurements is used. To address this problem, we proposed an anti-clutter filter with hypothesis testing, we take into account the number of measurements in cells, the target state and spatial distribution of clutter to decide whether the measurements in cell are clutter. Specifically, the hypothesis testing method is adopted to determine the origination of the measurements. Then, the likelihood functions of targets and clutter are deduced based on the information mentioned above, resulting in the likelihood ratio test statistic. Next, the likelihood ratio test statistic is proved to be subject to a chi-square distribution and a threshold corresponding to the confidence coefficient is introduced and the measurements below this threshold are considered as clutter. Then the correction step of ET-GIW-PHD is revised based on hypothesis testing results. Extensive experiments have demonstrated the significant performance improvement of our proposed method.

Highlights

Extended target tracking (ETT) draws lots of attention in recent years because of its wide range of applications in traffic control [1], autonomous driving [2,3,4], person tracking [5,6] and etc. [7,8,9,10,11].Since one extended target generates more than one measurement per time step, its shape information can be obtained
We found that the number of targets will be overestimated which degrades the final performance when severe clutters are partitioned into one cell in ET-Gaussian inverse Wishart (GIW)-probability hypothesis density (PHD)
Presents the likelihood of the jth GIW component given the measurements of the Wth ( j) cell, ω p is the weight of pth partition, p D is the detection probability of jth GIW component, γ( j) is the expected number of measurements generated by jth GIW component, λc is the mean number of clutter measurements, ck is the spatial distribution of the clutter over the surveillance volume, δi,j is ( j,W )

Summary

Introduction

Extended target tracking (ETT) draws lots of attention in recent years because of its wide range of applications in traffic control [1], autonomous driving [2,3,4], person tracking [5,6] and etc. [7,8,9,10,11]. Since one extended target generates more than one measurement per time step, its shape information can be obtained. Using this information, the kinematic state and extent of the target can be estimated simultaneously. We found that the number of targets will be overestimated which degrades the final performance when severe clutters are partitioned into one cell in ET-GIW-PHD. We proposed an anti-clutter ET-GIW-PHD filter for better cardinality estimation performance. The reason why ET-GIW-PHD overestimates the number of targets is discussed detailedly, and the probability of the measurement generated by clutter against different scenario parameters is presented. In order to deal with the cardinality overestimation in ET-GIW-PHD, we proposed an anti-clutter ET-GIW-PHD filter which revises the correction step of ET-GIW-PHD with hypothesis testing.

ET-GIW-PHD Review

Analysis of ET-GIW-PHD

Anti-Clutter ET-GIW-PHD

10: Output

Simulation

Conclusions