| Summary: | Compressive Sensing (CS), as a newly developed branch of sparse signal processing and representation approaches, has quickly found various applications in a large number of research topics in modern digital signal processing area. In this thesis, one such application of CS and sparse signal processing approaches and also to adapting the sparse signal processing in Range Detection and Ranging(RADAR) is discussed. In compressive sensing, the sampling strategy and reconstruction algorithms are two major components. Besides sparsity, underlying structures of the signal have been considered and exploited recently to enhance the performance of the standard sparse representation recovery methods.
One of the most commonly exploited structures in the literature is the block sparsity. In RADAR applications, usually Linear Modulated Frequency(LFM) signals are transmitted and it is reflected back again. This echo signal contains information about range and doppler frequencies of the target. In this thesis, the echo signal from the targets is considered to have block sparse structure and hence the algorithms are designed in such a way that the block sparsity among different blocks and internal sparsity within individual block are exploited by the proposed algorithm.
Compressive Sampling Matching Pursuit (CoSaMP) is a greedy iterative algorithm for approximating the sparse signal by reduced number of measurements and it’s used for CS reconstruction. The block sparse algorithms namely – block sparse CoSaMP and block sparse based Iterative Hard Thresholding (IHT) CoSaMP algorithm have imbibed the properties of CoSaMP algorithm and additionally utilize the union of subspaces to reduce the number of measurements required and improve the performance of reconstruction. The performance of the algorithms has been studied on both-one dimensional signal and two dimensional signals under different levels of Signal to Noise Ratio (SNR) at the receiving end.
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