Previous research has produced CPHD filters that can detect and track multiple targets in unknown, dynamically changing clutter. The .first such filters employed Poisson clutter generators and, as a result, were combinatorially complex. Recent research has shown that replacing the Poisson clutter generators with Bernoulli clutter generators results in computationally tractable CPHD filters. However, Bernoulli clutter generators are insufficiently complex to model real-world clutter with high accuracy, because they are statistically first-degree. This paper addresses the derivation and implementation of CPHD filters when first-degree Bernoulli clutter generators are replaced by second-degree quadratic clutter generators. Because these filters are combinatorially second-order, they are more easily approximated. They can also be implemented in exact closed form using beta-Gaussian mixture (BGM) or Dirichlet-Gaussian mixture (DGM) techniques.

Date Published: 21 May 2015
PDF: 12 pages
Proc. SPIE 9474, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, 94740C (21 May 2015); doi: 10.1117/12.2177177

Published in SPIE Proceedings Vol. 9474:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV
Ivan Kadar, Editor(s)