Abstract

This paper proposes a novel joint probabilistic data association (JPDA) filter for joint target tracking and track maintenance in the presence of unknown detection probability and clutter rate. The proposed algorithm consists of two main parts: (1) the standard JPDA filter with a Poisson point process birth model for multi-object state estimation; and (2) a multi-Bernoulli filter for detection probability and clutter rate estimation. Performance evaluation shows that the proposed JPDA filter can rapidly recover the performance of the ideal JPDA filter with perfect knowledge of detection probability and clutter rate. Therefore, the suggested algorithm is more suitable for real applications in a complex environment for multi-target tracking.

Highlights

  • There has been increasing attention on utilisation of small autonomous systems in military and civil applications

  • Motivated by the above observations, this paper aims to propose an enhanced version of joint probabilistic data association (JPDA) that can accommodate the unknown detection probability and clutter rate

  • This paper developed an enhanced version of JPDA by incorporating with the multi-Bernoulli filter to accommodate the unknown detection probability and clutter rate

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Summary

Introduction

There has been increasing attention on utilisation of small autonomous systems in military and civil applications. The issue is that the operations of these small autonomous systems are constrained by limited payload, as well as limited operation time and endurance. This has led to proliferation of lightweight, low-cost and energy efficient on-board sensors. Reliable and autonomous target tracking is a fundamental aspect of situation awareness for autonomous systems [1,2,3]. Applying low-cost and lightweight on-board sensors in target tracking imposes additional challenges to the tracking problem since they are likely to contain some degree of uncertainties. Low-cost sensors are generally subject to a high clutter rate and low detection probability

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