NDLI: Improved multi-target multi-Bernoulli filter with modelling of spurious targets

Content Provider	IET Digital Library
Author	Baser, Erkan Kirubarajan, Thia Efe, Murat Balaji, Bhashyam
Abstract	The cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter removes the positive bias from the data-updated cardinality estimate in the multi-target multi-Bernoulli (MeMBer) filter. In this study, the relationship between the MeMBer corrector and the multi-Bernoulli random finite set (RFS) distribution is analysed. By utilising this relationship, a filter that offers a new statistical framework for the MeMBer data update process is proposed. Thus, the multi-Bernoulli RFS distribution is extended to model spurious targets arising from targets under the legacy track set with high probabilities of existence. Unlike the CBMeMBer filter, the proposed filter removes the bias observed in the MeMBer filter by distinguishing spurious targets from actual targets, and while doing this, it does not make any limiting assumption on the probability of target detection. In addition, the modelling of spurious targets allows the refinement of the existence probabilities of targets in light of measurements. As a result, the stability of the cardinality estimate is improved while removing the bias. The theoretical analysis performed on the joint detection and state estimation problem of a single target reveals the strengths and limitations of the proposed filter. In addition, numerical simulations are performed in a scenario involving targets with crossing trajectories to demonstrate the filter performance.
Starting Page	285
Ending Page	298
Page Count	14
ISSN	17518784
Volume Number	10
e-ISSN	17518792
Issue Number	Issue 2, Feb (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/10/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2015.0169
Journal	IET Radar, Sonar & Navigation
Publisher Date	2016-02-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Cardinality Balanced Multitarget Multi-Bernoulli Filter Filtering Method in Signal Processing Filtering Theory Joint Detection And State Estimation Problem MeMBer Corrector MeMBer Data Update Process MeMBer Filter Model Spurious Target Multi-Bernoulli Random Finite Set Distribution Multi-Bernoulli RFS Distribution Numerical Simulation Object Detection Random Processes Signal Processing Theory Stability State Estimation Statistical Distribution Statistical Framework Statistics Target Detection Probability Target Tracking
Content Type	Text
Resource Type	Article
Subject	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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