NDLI: Extended object tracking using random matrix with converted measurements

Content Provider	IET Digital Library
Author	Li, Zhifei Zhang, Jianyun Wang, Jiegui Zhou, Qingsong
Abstract	The random matrix approach for extended object tracking (EOT) is appealing. This approach assumes that the measurements are linear in the kinematic state and measurement noise. In many practical applications, however, this linear condition cannot always be satisfied. First, this study derives a linearised measurement model. The model first employs a decorrelated unbiased technique to convert the non-linear measurements into Cartesian coordinates. Then, due to the property of the random matrix as the covariance of the extension noise, the model reformulates the likelihood function by calculating a product of two multivariate Gaussian distributions. The proposed linearised measurement model can be incorporated into existing random matrix approaches. Secondly, to describe a more complicated dynamics without the restriction of the existing random matrix framework, the authors propose a variational Bayesian (VB) approach for EOT. The VB approach minimises the Kullback–Leibler divergence between the true and approximate posterior density to obtain a convergent solution. The effectiveness of the proposed linearised model and the VB approach is illustrated by simulation results.
Starting Page	981
Ending Page	991
Page Count	11
ISSN	17518784
Volume Number	14
e-ISSN	17518792
Issue Number	Issue 7, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2019.0512
Journal	IET Radar, Sonar & Navigation
Publisher Date	2020-02-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Approximate Posterior Density Bayes Method Cartesian Coordinate Converted Measurement Covariance Matrices Covariance Random Matrix Decorrelated Unbiased Technique Decorrelation Extended Object Tracking Gaussian Distribution Kinematic State Kullback–Leibler Divergence Likelihood Function Linear Algebra Linearised Measurement Model Linearised Model Maximum Likelihood Estimation Measurement Error Measurement Noise Multivariate Gaussian Distribution Nonlinear Measurement Numerical Analysis Object Tracking Random Matrix Approach Random Matrix Framework Random Processes Signal Processing And Detection Signal Processing Theory Statistics Variational Bayesian Approach VB Approach
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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