NDLI: Probability hypothesis densities for multi-sensor, multi-target tracking with application to acoustic sensors array

Content Provider	IEEE Xplore Digital Library
Author	Lin Xiaodong Zhu Linhu Li Zhengxin
Copyright Year	2010
Description	Author affiliation: Engineering Institute, Air Force Engineering University, AFEU, Xi' an China (Lin Xiaodong; Zhu Linhu; Li Zhengxin)
Abstract	Random sets theory offers a uniform framework for the multi-source data fusion, and all problems of the data fusion could be describe, analyzed and solved in this framework. The multi-sensor multi-target tracking problem could be natural represented in the framework. It is of engineering importance to tracking low altitude moving targets with acoustic methods due to the blindness of the traditional radar detecting. In this paper, an algorithm for tracking the low altitude or ground moving targets is put forward based on the Probability Hypothesis Density (PHD) Filter. The PHD Filter based on Finite Set Statistics doesn't need consider data association for multi-target tracking, which propagates the PHD or first moment instead of the full multi-target posterior, and it could estimating the unknown and time-varying number of targets and their states under clutter environment. In the practical, we use the Sequential Monte Carlo (SMC) method to approximate the PHD. The paper presents a novel and fundamentally well-grounded framework for tracking multiple acoustic targets using PHD Filter and passive acoustic localization technique. Simulations are also presented to demonstrate the performance in tracking a randomly varying number of targets in a clutter environment.
Starting Page	218
Ending Page	222
File Size	150952
Page Count	5
File Format	PDF
ISBN	9781424458455
e-ISBN	9781424458486
DOI	10.1109/ICACC.2010.5487262
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monte Carlo method Target tracking Multi-target tracking Radar tracking Clutter Sensor arrays Filters Acoustical engineering Blindness Acoustic sensors Acoustic arrays Set theory Data fusion PHD Filter Random set Passive acoustic localization
Content Type	Text
Resource Type	Article

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