Knowledge-Aided STAP Processing for Ground Moving Target Indication Radar Using Multilook Data
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Knowledge-Aided STAP Processing for Ground Moving Target Indication Radar Using Multilook Data Douglas Page and Gregory Owirka BAE Systems Advanced Information Technologies, 6 New England Executive Park, Burlington, MA 01803, USA Received 7 November 2004; Revised 16 February 2005; Accepted 8 March 2005 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. Copyright © 2006 D. Page and G. Owirka. 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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INTRODUCTION
The lack of training data in nonhomogeneous clutter environments can cause severe degradation in the performance of space-timeadaptive processing (STAP) algorithms (see [1, 2] and references therein). Surveillance radars typically perform STAP processing [3] on a limited number of pulses of data, which are referred to as a coherent processing interval (CPI). Each CPI is divided into a number of time samples which correspond to the radar range gates. In each range gate, the return signal in each antenna channel and on each pulse in the CPI is digitized into in-phase and quadrature components. The radar returns can thus be represented as complex numbers, whose real parts are the corresponding in-phase components, and whose imaginary parts are the quadrature components. Thus, in each range gate the returns from each channel and pulse can be represented as an NM by 1 complex column vector, where N is the number of antenna channels and M the number of pulses per CPI. Covariance estimation for STAP is usually performed by averaging the outer products of these return vectors with themselves over a number of training range gates from a single CPI. As was shown by Reed et al. [4], this is a maximum likelihood estimate
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