![]() The RCS is the property of a scattering object, or target, which represents the magnitude of the echo signal returned to the radar by the target. While massive amounts of data will be generated by a penetrating sensor, it is important for the warfighters to find technologies that not only integrate information from diverse sources but also provide indications of information significance in ways that help them to make tactical decision. 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.Ĭurrent 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. Even if the signal could not be completely recovered, the probability of miss detection of target could be kept zero. 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. 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. A diversity gain could also be obtained at the output of the matched filters. 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. 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. Inspired by recent advances in compressive sensing (CS), we introduce CS to the radar sensor network (RSN) using pulse compression technique.
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