Radar Signal Reconnaissance Processing With Compressed Sensing

Radar reconnaissance is an important part in the field of ECM(ElectronicCounter-measure). Both the capture technology of digital receiver’s radar signals andthe detection processing algorithms after the radar signals are based on the Nyquistsampling theorem. In order to reconstruct signal accurately, the sampling rate mustreach more than twice as much as the signal bandwidth. The instantaneous bandwidthare becoming more and more wide, which makes the signal storage space andcomputation time required in the signal capture and the parameter estimation in trouble.The appearance of Compressed Sensing provides a new way for rdar reconnaissance.This thesis studies the applications of compressed sensing in the analog domain,including two algorithms: random sampling based on Compressed sensing and AIC(Analog-to-Information Conversion). Signal detection algorithm and frequencyestimation algorithm based on Compressed sensing in case of noreconstruction arestudied as well. Compression sensing signal detection algorithm based on orthogonalmatching pursuit, which use the Maximum projection coefficients of original signal inthe transform domain as a judgment basis, is one of the detection algorithms that basedon part of the signal reconstruction. Compression sensing signal detection method baseon numerical characteristics of sampling value, which according to the differentcharacteristics of the expectation of sampling values under different hypothesis,detection is accomplished by using the deviation of the actual sampling values from theexpectations under corresponding hypothesis as criterion; And signal detection viacompressed censing using minimax criterion, compression using minimax criterion anddetection threshold can be deduced from observations in this algorithm. Frequencyestimation with compressed sensing established atomic library based on the sparserepresentation of signals and completed the compression of the signal using the AICconversion.In the compressed domain, sparse coefficient is optimized and reconstructedusing the orthogonal matching pursuit algorithm, and then finding out the maximumposition of the sparse coefficient, of which the position of the atomic frequencyparameters for signal in the library is the frequency of the signal.