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Proceedings Paper

Joint passive radar tracking and target classification using radar cross section
Author(s): Shawn M. Herman
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Paper Abstract

We present a recursive Bayesian solution for the problem of joint tracking and classification of airborne targets. In our system, we allow for complications due to multiple targets, false alarms, and missed detections. More importantly, though, we utilize the full benefit of a joint approach by implementing our tracker using an aerodynamically valid flight model that requires aircraft-specific coefficients such as wing area and vehicle mass, which are provided by our classifier. A key feature that bridges the gap between tracking and classification is radar cross section (RCS). By modeling the true deterministic relationship that exists between RCS and target aspect, we are able to gain both valuable class information and an estimate of target orientation. However, the lack of a closed-form relationship between RCS and target aspect prevents us from using the Kalman filter or its variants. Instead, we rely upon a sequential Monte Carlo-based approach known as particle filtering. In addition to allowing us to include RCS as a measurement, the particle filter also simplifies the implementation of our nonlinear non-Gaussian flight model.

Paper Details

Date Published: 5 January 2004
PDF: 16 pages
Proc. SPIE 5204, Signal and Data Processing of Small Targets 2003, (5 January 2004); doi: 10.1117/12.505793
Show Author Affiliations
Shawn M. Herman, Numerica Corp. (United States)

Published in SPIE Proceedings Vol. 5204:
Signal and Data Processing of Small Targets 2003
Oliver E. Drummond, Editor(s)

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