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 θ ̂ satisfies the CRLB and our ML estimator is an accurate estimator on the target RCS parameter.
Highlights
Current requirements in warfighting functionality result in obtaining accurate and timely information about battlespace objects and events so that the warfighters can make decision about reliable location, tracking, combat identification and targeting information
Liang [6] studied the radar sensor network (RSN) design based on linear frequency modulation (LFM) waveforms and applied the LFM waveforms to RSN in the context of automatic target recognition (ATR) with delay-Doppler uncertainty
It is known that the pulse compression technique allows a radar to achieve both the energy of a long pulse and the resolution of a short pulse, without the high peak power which is required by a high energy short duration pulse [7]
Summary
Current requirements in warfighting functionality result in obtaining accurate and timely information about battlespace objects and events so that the warfighters can make decision about reliable location, tracking, combat identification and targeting information. Liang [5] studied constant frequency (CF) pulse waveform design and proposed Maximum-Likelihood (ML) automatic target recognition (ATR) approach for both nonfluctuating and fluctuating targets in a network of multiple radar sensors. The most common SF waveform employs a linear frequency stepping pattern, where the RF frequency of each pulse is increased by F This representation motivates the applicability of the recently proposed compressive sensing (CS) theory [8,9] that refers to such signals as ‘sparse’ or ‘compressible’. After applying compressive sensing to RSN, we perform target RCS value estimation.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
