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4 May 2009 Probing waveform synthesis and receive filter design for active sensing systems
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Probing waveform synthesis and receive filter design play crucial roles in achievable performance for active sensing applications, including radar, sonar, and medical imaging. We focus herein on conventional single-input single-output (SISO) radar systems. A flexible receive filter design approach, at the costs of lower signal-to-noise ratio (SNR) and higher computational complexity, can be used to compensate for missing features of the probing waveforms. A well synthesized waveform, meaning one with good autocorrelation properties, can reduce computational burden at the receiver and improve performance. Herein, we will highlight the interplay between waveform synthesis and receiver design. We will review a novel, cyclic approach to waveform design, and then compare the merit factors of these waveforms to other well-known sequences. In our comparisons, we will consider chirp, Frank, Golomb, and P4 sequences. Furthermore, we will overview several advanced techniques for receiver design, including data-independent instrumental variables (IV) filters, a data-adaptive iterative adaptive approach (IAA), and a data-adaptive Sparse Bayesian Learning (SBL) algorithm. We will show how these designs can significantly outperform conventional matched filter (MF) techniques for range compression as well as for range-Doppler imaging.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
William Roberts, Hao He, Xing Tan, Ming Xue, Duc Vu, Jian Li, and Petre Stoica "Probing waveform synthesis and receive filter design for active sensing systems", Proc. SPIE 7335, Automatic Target Recognition XIX, 73350H (4 May 2009);


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