Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Knowledge-Aided STAP Processing for Ground Moving Target Indication Radar Using Multilook Data | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 74838 Pages 1-16 DOI ASP 2006 74838 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 verily 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. 1. INTRODUCTION The lack of training data in nonhomogeneous .
