7 May 2012 Bayesian filtering in electronic surveillance
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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.
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Stefano Coraluppi, Stefano Coraluppi, Craig Carthel, Craig Carthel, } "Bayesian filtering in electronic surveillance", Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 839202 (7 May 2012); doi: 10.1117/12.912964; https://doi.org/10.1117/12.912964
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