NDLI: Joint detection, tracking and classification of a manoeuvring target in the finite set statistics framework

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Joint detection, tracking and classification of a manoeuvring target in the finite set statistics framework

Content Provider	IET Digital Library
Author	Yang, Wei Wang, Zhongxun Fu, Yaowen Pan, Xiaogang Li, Xiang
Abstract	Target detection, tracking and classification are three essential and closely coupled subjects for most surveillance systems. In the finite set statistics (FISST) framework, this paper presents a Bayesian and recursive solution to joint detection, tracking and classification (JDTC) of a manoeuvring target in a cluttered environment, which is inspired by previous work on joint target tracking and classification in the classical Bayesian filter framework. The derived JDTC algorithm exploits the dependence of target state on target class by using class-dependent dynamical model sets. The relative merits of this JDTC algorithm are demonstrated via a two-dimensional example using a sequential Monte Carlo implementation. It is shown that handling those three closely coupled subjects jointly can achieve comparable detection and tracking performance to that of the exact filter in the FISST framework with a prior known class. The classification results are consistent with the previous work.
Starting Page	10
Ending Page	20
Page Count	11
ISSN	17519675
Volume Number	9
e-ISSN	17519683
Issue Number	Issue 1, Feb (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/9/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2013.0363
Journal	IET Signal Processing
Publisher Date	2015-02-23
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bayes Method Class Dependent Dynamical Model Sets Classical Bayesian Filter Cluttered Environment Filtering Method in Signal Processing Filtering Theory Finite Set Statistics FISST JDTC Algorithm Joint Detection Tracking And Classification Joint Target Tracking Manoeuvring Target Classification Manoeuvring Target Detection Manoeuvring Target Tracking Monte Carlo Method Object Detection Recursive Estimation Recursive Method Sequential Monte Carlo Implementation Signal Classification Signal Detection Signal Processing Theory Surveillance System Target Tracking
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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