Compressed sensing based range detection and Doppler estimation for portable surveillance radar

Compressed sensing is a novel technique of under sampling sparse or compressible signals using random linear non adaptive measurements. Under the sparsity constraints it allows for reconstruction of the data with small or zero error using minimum l1 norm reconstruction methods. Since most of the real world data are either sparse or compressible in some suitable basis, the proposed method has immediately found application in varied fields like imaging systems, MRI, Radar systems etc. But most of the applications considered in literature deal with real data. For radar applications the data is complex and suitable amendments for processing are considered. In this paper we proposed an application of compressed sensing towards portable radar signal processor. We have used the method to accurately detect target's range and doppler with highly reduced quantum of input data. Since the doppler information is corrupted during the signal transformation to lower dimension we have used a parallel processing channel for estimating the doppler information.