A bootstrapped PMHT with feature measurements and a new way to derive its information matrix

Qin Lu; Katherine Domrese; Peter Willett; Yaakov Bar-Shalom; Krishna Pattipati

doi:10.1117/12.2264347

2 May 2017 A bootstrapped PMHT with feature measurements and a new way to derive its information matrix

Qin Lu, Katherine Domrese, Peter Willett, Yaakov Bar-Shalom, Krishna Pattipati

Proceedings Volume 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI; 102001I (2017) https://doi.org/10.1117/12.2264347
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

The probabilistic multiple-hypothesis tracker (PMHT), a tracking algorithm of considerable theoretical elegance based on the expectation-maximization (EM) algorithm, will be considered for the problem of multiple target tracking (MTT) with multiple sensors in clutter. Aside from position observations, continuous measurements associated with the unique and constant feature of each target are incorporated to jointly estimate the states and feature of the targets for the sake of tracking and classification, leading to a bootstrapped implementation of the PMHT. In addition, we rederived the information matrix for the big state vector stacking states for all the targets at all the time steps during the observation time. Simulation results have been conducted for both closely spaced and well separated scenarios with and without feature measurements. The normalized estimation error squared (NEES) calculated using the information matrix for both scenarios with and without feature measurements are within the 95% probability region. In other words, the estimates are consistent with the corresponding covariances.

Citation Download Citation

Qin Lu, Katherine Domrese, Peter Willett, Yaakov Bar-Shalom, and Krishna Pattipati "A bootstrapped PMHT with feature measurements and a new way to derive its information matrix", Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 102001I (2 May 2017); https://doi.org/10.1117/12.2264347

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KEYWORDS

Expectation maximization algorithms

Detection and tracking algorithms

Sensors

Feature extraction

Motion measurement

Motion models

Algorithm development

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