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Knowledge-Aided STAP Processing for Ground Moving Target Indication Radar Using Multilook Data

EURASIP Journal on Advances in Signal Processing20062006:074838

https://doi.org/10.1155/ASP/2006/74838

Received: 7 November 2004

Accepted: 8 March 2005

Published: 3 January 2006

Abstract

Knowledge-aided space-time adaptive processing (KASTAP) using multiple coherent processing interval (CPI) radar data is described. The approach is based on forming earth-based clutter reflectivity maps to provide improved knowledge of clutter statistics in nonhomogeneous terrain environments. The maps are utilized to calculate predicted clutter covariance matrices as a function of range. Using a data set provided under the DARPA knowledge-aided sensor signal processing and expert reasoning (KASSPER) Program, predicted distributed clutter statistics are compared to measured statistics to verify the accuracy of the approach. Robust STAP weight vectors are calculated using a technique that combines covariance tapering, adaptive estimation of gain and phase corrections, knowledge-aided prewhitening, and eigenvalue rescaling. Techniques to suppress large discrete returns, expected in urban areas, are also described. Several performance metrics are presented, including signal-to-interference-plus-noise ratio (SINR) loss, target detections and false alarms, receiver operating characteristic (ROC) curves, and tracking performance. The results show more than an order of magnitude reduction in false alarm density when compared to standard STAP processing.

Keywords

RadarFalse AlarmTracking PerformanceRadar DataPhase Correction

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Authors’ Affiliations

(1)
BAE Systems Advanced Information Technologies, Burlington, USA

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Copyright

© D. Page and G. Owirka. 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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