Abstract
Compressive sensing is a novel research field which gains many interest. The idea of compressive sensing is based on sparsity and incoherence, which is related to signal characteristic and measurement scheme respectively. Many research fields have the motivation using the compressive sensing. For radar applications, compressive sensing can improve radar system by eliminating the need of matched filter and reducing the bandwidth requirement of analog to digital converter. The signal reconstruction problem of compressive sensing can be solved by reconstruction algorithms, for example, orthogonal matching pursuit(OMP) is one of the popular algorithms. The signal reconstruction algorithms are the key component of compressive sensing applications. Unfortunately, the complexity of reconstruction algorithms for compressive sensing radar increases with the resolution of compressive sensing radar system. Hence, complexity has become a critical issue of compressive sensing radar system. This work proposes a two-stage reconstruction algorithm for compressive sensing radar. The proposed two-stage reconstruction algorithm for compressive sensing radar has better positioning performance and lower complexity than conventional OMP algorithm under noisy environment. Furthermore, path loss model and human respiratory signal model are applied for simulations in this work, in order to improve the reality of simulation results. This work also presents a architecture design of two time consuming steps, coarse and fine positioning step, of proposed two-stage reconstruction algorithm. The computational complexity of proposed algorithm with block size b=4 is approximately 25% of conventional OMP algorithm.