Multi-object filtering with Poisson arrival-rate measurements

Daniel Clark; Sharad Nagappa

doi:10.1117/12.884574

5 May 2011 Multi-object filtering with Poisson arrival-rate measurements

Daniel Clark, Sharad Nagappa

Proceedings Volume 8050, Signal Processing, Sensor Fusion, and Target Recognition XX; 80500L (2011) https://doi.org/10.1117/12.884574
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

Daniel Clark and Sharad Nagappa "Multi-object filtering with Poisson arrival-rate measurements", Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80500L (5 May 2011); https://doi.org/10.1117/12.884574

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