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Proceedings Paper

Multi-object filtering with Poisson arrival-rate measurements
Author(s): Daniel Clark; Sharad Nagappa
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Paper Abstract

Recent interest in multi-object filtering has focussed on the problem of discrete-time filtering, where sets of measurements are collected at regular intervals from the sensor. Many sensors do not provide multiple measurements at regular intervals but instead provide single-measurement reports at irregular time-steps. In this paper we study the multi-object filtering problem for estimation from measurements where the target and clutter processes provide measurements with Poisson arrival rates. In particular, we show that the Probability Hypothesis Density (PHD) filter can be adapted to Poisson arrival rate measurements by modelling the probability of detection with an exponential distribution. We demonstrate the approach in simulated scenarios.

Paper Details

Date Published: 5 May 2011
PDF: 7 pages
Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80500L (5 May 2011); doi: 10.1117/12.884574
Show Author Affiliations
Daniel Clark, Heriot-Watt Univ. (United Kingdom)
Sharad Nagappa, Heriot-Watt Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 8050:
Signal Processing, Sensor Fusion, and Target Recognition XX
Ivan Kadar, Editor(s)

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