Sparsity-based multi-target localization exploiting multi-frequency coprime array

In this paper, a novel sparsity-based multi-target localization approach is proposed by exploiting a coprime array operated with multiple narrowband signals of distinct but closely separated carrier frequencies. The cross-covariance matrix is formulated between baseband array data corresponding to different sensing frequencies to generate virtual difference coarrays, which enable direction-of-arrival (DOA) estimation of more targets than the number of physical sensors. In addition, the use of well designed multi-frequency signals unwraps the propagation phase information, thereby enabling unambiguous estimation of the target ranges. The DOA and range estimations are cast as a sparse reconstruction problem and are solved using the complex mulititask Bayesian compressive sensing (CMT-BCS) technique. The effectiveness of the proposed technique is verified through simulation results.