NDLI: Online clutter estimation using a Gaussian kernel density estimator for multitarget tracking

Content Provider	IET Digital Library
Author	Chen, Xin Tharmarasa, Ratnasingham Kirubarajan, Thia Donald, Mike Mc
Abstract	In this study, the spatial distribution of false alarms is assumed to be a non-homogeneous Poisson point (NHPP) process. Then, a new method is developed under the kernel density estimation (KDE) framework to estimate the spatial intensity of false alarms for the multitarget tracking problem. In the proposed method, the false alarm spatial intensity estimation problem is decomposed into two subproblems: (i) estimating the number of false alarms in one scan and (ii) estimating the variation of the intensity function value in the measurement space. Under the NHPP assumption, the only parameter that needs to be estimated for the first subproblem is the mean of false alarm number, and the empirical mean is used here as the maximum likelihood estimate of that parameter. Then, for the second subproblem, an online multivariate local adaptive Gaussian kernel density estimator is proposed. Furthermore, the proposed estimation method is seamlessly integrated with widely used multitarget trackers, like the joint integrated probabilistic data association algorithm and the multiple hypotheses tracking algorithm. Simulation results show that the proposed KDE-based method can provide a better estimate of the false alarm spatial intensity and help the multitarget trackers yield superior performance in scenarios with spatially non-homogeneous false alarms.
Starting Page	1
Ending Page	9
Page Count	9
ISSN	17518784
Volume Number	9
e-ISSN	17518792
Issue Number	Issue 1, Jan (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/9/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2014.0037
Journal	IET Radar, Sonar & Navigation
Publisher Date	2014-06-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Gaussian Kernel Density Estimator Gaussian Processes Intensity Function Value Kernel Density Estimation Framework Mathematical Analysis Maximum Likelihood Estimation Multiple Hypotheses Tracking Algorithm Multitarget Trackers Multitarget Tracking Problem NHPP Model NHPP Process Non Homogeneous Poisson Point Online Clutter Estimation Poisson Equation Radar Equipment Space Measurement Spatial Distribution Spatial Variation Statistics System And Application 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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