Distributed RADAR waveform design based on compressive sensing considerations

In this paper we describe a joint waveform design methodology for distributed imaging RADARs using the concepts of compressive sensing. Compressive sensing is an active area of research that offers the promise of good object reconstruction with a sparse measurement set. The measurement set of the scene is based on a set of dasiaprobes,psila the radar waveforms. The set of measurements must satisfy the restricted isometry property and the scene being interrogated must be dasiacompressiblepsila meaning that it can be sparsely represented in some basis. We examine waveform and position considerations for a distributed radar system to satisfy these constraints and show their impact on waveform design and image reconstruction.

[1]  Calvin H. Wilcox,et al.  The Synthesis Problem for Radar Ambiguity Functions , 1991 .

[2]  Terence Tao,et al.  The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.

[3]  Dong Wei,et al.  An evaluation of SAR image compression techniques , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  Mark R. Bell.  Information theory and radar waveform design , 1993, IEEE Trans. Inf. Theory.

[5]  Charles A. Stutt,et al.  A 'best' mismatched filter response for radar clutter discrimination , 1968, IEEE Trans. Inf. Theory.

[6]  S. Kay,et al.  Optimal Signal Design for Detection of Gaussian Point Targets in Stationary Gaussian Clutter/Reverberation , 2007, IEEE Journal of Selected Topics in Signal Processing.

[7]  Steven M. Sussman,et al.  Least-square synthesis of radar ambiguity functions , 1962, IRE Trans. Inf. Theory.

[8]  Bhaskar D. Rao,et al.  Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm , 1997, IEEE Trans. Signal Process..

[9]  Lloyd J. Spafford.  Optimum radar signal processing in clutter , 1968, IEEE Trans. Inf. Theory.

[10]  Michael C. Wicks.  Sensors as robots , 2006, 2006 International Waveform Diversity & Design Conference.

[11]  K. Cooper,et al.  Conditional and constrained joint optimization of RADAR waveforms , 2007, 2007 International Waveform Diversity and Design Conference.

[12]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[13]  Joseph R. Guerci,et al.  Optimum transmit-receiver design in the presence of signal-dependent interference and channel noise , 1999 .

[14]  Benjamin Bachelor,et al.  Joint RADAR waveform design for networked ISR systems , 2006, 2006 International Waveform Diversity & Design Conference.

[15]  Joseph R. Guerci,et al.  Multichannel matched transmit-receiver design in presence of signal-dependent interference and noise , 2000, Proceedings of the 2000 IEEE Sensor Array and Multichannel Signal Processing Workshop. SAM 2000 (Cat. No.00EX410).

[16]  Jian Li,et al.  MIMO Radar with Colocated Antennas , 2007, IEEE Signal Processing Magazine.

[17]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[18]  A. W. Rihaczek.  Principles of high-resolution radar , 1969 .

[19]  Robert D. Nowak,et al.  Signal Reconstruction From Noisy Random Projections , 2006, IEEE Transactions on Information Theory.