Adaptive Target Birth Intensity for ET-PHD Filter

Lu Miao,Xin-Xi Feng,Luo-Jia Chi,Changde Lu,Xuesheng Pei,Jianning Su

doi:10.1051/matecconf/201817603010

Lu Miao, Xin-Xi Feng + Show 4 more

Open Access

https://doi.org/10.1051/matecconf/201817603010

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Abstract

An adaptive tracking algorithm based on Extended target Probability Hypothesis Density (ETPHD) filter is proposed for extended target tracking problem with priori unknown target birth intensity.The algorithm is implemented by gaussian mixture, where the target birth intensity is generated by measurement-driven, and the persistent and the newborn targets intensity are respectively predicted and updated. The simulation results show that the proposed algorithm improves the performance of the probability hypothesis density filter in the extended target tracking.

Highlights

Multiple target tracking technology is widely used in sensor networks, machine recognition and positioning, and other fields
Due to the continuous improvement of sensor resolution, more and more attention and research are paid to the extended target tracking problem, and the extended target occupies multiple sensor resolution units at each observation moment
For the traditional extended target Probability Hypothesis Density (ET-PHD) filter, it is assumed that the target birth intensity is a priori known, but in situations where the targets may appear anywhere in the surveillance volume

Summary

INTRODUCTION

Multiple target tracking technology is widely used in sensor networks, machine recognition and positioning, and other fields. For the traditional ET-PHD filter, it is assumed that the target birth intensity is a priori known, but in situations where the targets may appear anywhere in the surveillance volume. For this problem, the literature [8] proposed a measurement-driven PHD filter. The literature [9] improves on this, and puts forward an adaptive particle PHD filtering algorithm based on measurement-driven. The algorithm is based on the measurement-driven to obtain the target birth intensity, and simultaneously predicts and updates the persistent and the newborn targets. An adaptive extended target tracking algorithm based on ET-PHD filter is proposed to solve the extended target tracking problem with priori unknown target birth intensity. The simulation results show that the proposed algorithm improves the stability of the extended target number estimation and has better state estimation accuracy

Adaptive ET-PHD filtering algorithm

Gaussian mixture implementation

Simulation Result and Analysis

Findings

Conclusions