DocumentCode :
1422248
Title :
Dimensionality Reduction Techniques for Efficient Adaptive Pulse Compression
Author :
Blunt, Shannon D. ; Higgins, Thomas
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Univ. of Kansas, Lawrence, KS, USA
Volume :
46
Issue :
1
fYear :
2010
Firstpage :
349
Lastpage :
362
Abstract :
Adaptive filtering for radar pulse compression has been shown to improve sidelobe suppression through the estimation of an appropriate pulse compression filter for each individual range cell of interest. However, the relatively high computational cost of full-dimension, adaptive range processing may limit practical implementation in many current real-time systems. Dimensionality reduction techniques are here employed to approximate the framework for pulse compression filter estimation. Within this approximate framework, two new minimum mean square error (MMSE) based adaptive algorithms are derived. The two algorithms are denoted as specific embodiments of the fast adaptive pulse compression (FAPC) method and are shown to maintain performance close to that of full-dimension adaptive processing, while reducing computation cost by nearly an order of magnitude (in terms of the discretized waveform length N).
Keywords :
adaptive filters; mean square error methods; pulse compression; radar signal processing; adaptive filtering; adaptive pulse compression; dimensionality reduction techniques; fast adaptive pulse compression method; minimum mean square error; radar pulse compression; sidelobe suppression; Adaptive filters; Computational efficiency; Filtering; Matched filters; Pulse compression methods; Pulse modulation; Radar cross section; Radar scattering; Real time systems; Signal to noise ratio;
fLanguage :
English
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9251
Type :
jour
DOI :
10.1109/TAES.2010.5417167
Filename :
5417167
Link To Document :
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