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Proceedings Paper

Bayesian filtering in electronic surveillance
Author(s): Stefano Coraluppi; Craig Carthel
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Paper Abstract

Fusion of passive electronic support measures (ESM) with active radar data enables tracking and identification of platforms in air, ground, and maritime domains. An effective multi-sensor fusion architecture adopts hierarchical real-time multi-stage processing. This paper focuses on the recursive filtering challenges. The first challenge is to achieve effective platform identification based on noisy emitter type measurements; we show that while optimal processing is computationally infeasible, a good suboptimal solution is available via a sequential measurement processing approach. The second challenge is to process waveform feature measurements that enable disambiguation in multi-target scenarios where targets may be using the same emitters. We show that an approach that explicitly considers the Markov jump process outperforms the traditional Kalman filtering solution.

Paper Details

Date Published: 7 May 2012
PDF: 10 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 839202 (7 May 2012); doi: 10.1117/12.912964
Show Author Affiliations
Stefano Coraluppi, Compunetix Inc. (United States)
Craig Carthel, Compunetix Inc. (United States)

Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)

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