NDLI: Robust adaptive filtering for extended target tracking with heavy-tailed noise in clutter

Content Provider	IET Digital Library
Author	Gao, Lei Jing, Zhongliang Li, Minzhe Pan, Han
Abstract	A robust adaptive filter is proposed by using the variational Bayesian (VB) inference to extended target tracking with heavy-tailed noise in clutter. An explicit distribution is used to describe the non-Gaussian heavy-tailed noise based on Student's t-distribution. The need for arbitrary decisions is then eliminated, and the robust operation is provided which is less sensitive to extreme observation. Moreover, an approximate measurement update using the analytical techniques of VB methods is derived to approximate the posterior states at each time step. To obtain a more accurate result, clutter estimation is also integrated considering the uncertainty of target tracking in a cluttered environment. The performance of the proposed algorithm is demonstrated with simulated data.
Starting Page	826
Ending Page	835
Page Count	10
ISSN	17519675
Volume Number	12
e-ISSN	17519683
Issue Number	Issue 7, Sep (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/12/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2017.0249
Journal	IET Signal Processing
Publisher Date	2018-03-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Filter Approximation Theory Bayes Method Clutter Digital Signal Processing Extended Target Tracking Filtering Method in Signal Processing Gaussian Distribution Gaussian Noise Inference Mechanisms Interpolation And Function Approximation Knowledge Engineering Technique NonGaussian Heavy-tailed Noise Numerical Analysis Robust Adaptive Filtering Statistics Student's T-distribution Target Tracking Variational Bayesian Inference Method Variational Technique VB Inference Method
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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