Open Access Research

Compressive sensing in distributed radar sensor networks using pulse compression waveforms

Lei Xu1*, Qilian Liang1, Xiuzhen Cheng2 and Dechang Chen3

Author Affiliations

1 Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX 76010, USA

2 Department of Computer Science, The George Washington University, Washington DC 20052, USA

3 Department of Preventive Medicine and Biometrics Uniformed Services, University of the Health Sciences Bethesda, Maryland 20814-4799, USA

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EURASIP Journal on Wireless Communications and Networking 2013, 2013:36  doi:10.1186/1687-1499-2013-36

Published: 19 February 2013

Abstract

Inspired by recent advances in compressive sensing (CS), we introduce CS to the radar sensor network (RSN) using pulse compression technique. Our idea is to employ a set of stepped-frequency (SF) waveforms as pulse compression codes for transmit sensors, and to use the same SF waveforms as the sparse matrix to compress the signal in the receiving sensor. We obtain that the signal samples along the time domain could be largely compressed so that they could be recovered by a small number of measurements. A diversity gain could also be obtained at the output of the matched filters. In addition, we also develop a maximum likelihood (ML) algorithm for radar cross section (RCS) parameter estimation and provide the Cramer-Rao lower bound (CRLB) to validate the theoretical result. Simulation results show that the signal could be perfectly reconstructed if the number of measurements is equal to or larger than the number of transmit sensors. Even if the signal could not be completely recovered, the probability of miss detection of target could be kept zero. It is also illustrated that the actual variance of the RCS parameter estimation <a onClick="popup('http://jwcn.eurasipjournals.com/content/2013/1/36/mathml/M1','MathML',630,470);return false;" target="_blank" href="http://jwcn.eurasipjournals.com/content/2013/1/36/mathml/M1">View MathML</a> satisfies the CRLB and our ML estimator is an accurate estimator on the target RCS parameter.

Keywords:
Compressive sensing; Radar sensor networks; Pulse compression; Stepped-frequency waveform; Target RCS