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• W2266654166 abstract "We present both offline and online maximum like- lihood 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. I. I NTRODUCTION The multiple target tracking (MTT) problem concerns the analysis of data from multiple moving objects which are par- tially observed in noise to extract accurate motion traject ories. The MTT framework has been traditionally applied to solve surveillance problems but more recently there has been a surge of interest in Biological Signal Processing, e.g. see (34). The MTT framework is comprised of the following ingre- dients. A set of multiple independent targets moving in the surveillance region in a Markov fashion. The number of targets varies over time due to departure of existing targets (known as death) and the arrival of new targets (known as birth). The initial number of targets are unknown and the maximum number of targets present at any given time is unrestricted. At each time each target may generate an observation which is a noisy record of its state. Targets that do not generate observa- tions are said to be undetected at that time. Additionally, t here may be spurious observations generated which are unrelated to targets (known as clutter). The observation set at each ti me is the collection of all target generated and false measurem ents recorded at that time, but without any information on the origin or association of the measurements. False measure- ments, unknown origin of recorded measurements, undetected targets and a time varying number of targets render the task of extracting the motion trajectory of the underlying targe ts from the observation record, which is known as tracking in the literature, a highly challenging problem. There is a large body of work on the development of algorithms for tracking multiple moving targets. These al- gorithms can be categorised by how they handle the data association (or unknown origin of recorded measurements)" @default.
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• W2266654166 date "2012-12-04" @default.
• W2266654166 modified "2023-09-27" @default.
• W2266654166 title "Estimating the Static Parameters in Linear Gaussian Multiple Target Tracking Models" @default.
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