Estimating the Static Parameters in Linear Gaussian Multiple Target Tracking Models

2012/12/04 by Sinan Yıldırım, Sinan Yildirim, Lan Jiang +6
Computer Science · Mathematics · #Applications (stat.AP) #Computation (stat.CO) #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Methodology (stat.ME) #Target Tracking and Data Fusion in Sensor Networks #stat.AP #stat.CO #stat.ME

paper · pdf · doi:10.48550/arxiv.1212.0849

17 double column pages, 9 figures

arxiv created 2012/12/04 · openalex publication_date 2012/12/04 · arxiv updated 2014/10/09 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28

Abstract

We present both offline and online maximum likelihood estimation (MLE) techniques for inferring the static parameters of a multiple target tracking (MTT) model with linear Gaussian dynamics. We present the batch and online versions of the expectation-maximisation (EM) algorithm for short and long data sets respectively, and we show how Monte Carlo approximations of these methods can be implemented. Performance is assessed in numerical examples using simulated data for various scenarios and a comparison with a Bayesian estimation procedure is also provided.

Citations

• Convergence of a stochastic approximation version of the EM algorithm
• Multitarget bayes filtering via first-order multitarget moments
• Sequential monte carlo methods for multi-target filtering with random finite sets

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