PHD filtering with localised target number variance

Emmanuel Delande; Jérémie Houssineau; Daniel Clark

doi:10.1117/12.2015786

23 May 2013 PHD filtering with localised target number variance

Emmanuel Delande, Jérémie Houssineau, Daniel Clark

Proceedings Volume 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII; 87450E (2013) https://doi.org/10.1117/12.2015786
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Mahler’s Probability Hypothesis Density (PHD filter), proposed in 2000, addresses the challenges of the multipletarget detection and tracking problem by propagating a mean density of the targets in any region of the state space. However, when retrieving some local evidence on the target presence becomes a critical component of a larger process - e.g. for sensor management purposes - the local target number is insufficient unless some confidence on the estimation of the number of targets can be provided as well. In this paper, we propose a first implementation of a PHD filter that also includes an estimation of localised variance in the target number following each update step; we then illustrate the advantage of the PHD filter + variance on simulated data from a multiple-target scenario.

Citation Download Citation

Emmanuel Delande, Jérémie Houssineau, and Daniel Clark "PHD filtering with localised target number variance", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87450E (23 May 2013); https://doi.org/10.1117/12.2015786

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