ABSTRACT
This chapter proposes a novel algorithm called the general similar sensing matrix pursuit (GSSMP) to reconstruct the K-sparse signal based on the original deterministic sensing matrix. The proposed algorithm consists of two components: the offline and online components. An orthogonal matching pursuit (OMP) algorithm is used to find an approximate estimate of the true support set, which contains the indices of the columns that contribute to the original sparse vector. The chapter considers the application of detecting an unknown number of moving targets in high clutter using an airborne radar system. Because the airborne radar scenario has a high clutter-to-signal ratio (CSR), the prominent elements of the spectral distribution focus along the clutter ridge in the direction of arrival (DOA)—Doppler plane. The chapter also introduces a general space-time model for airborne radar systems, which is represented in a compressed sensing framework. It explains the compressed sensing-based multiple target detection algorithm. Finally, the chapter presents numerical simulation and results.
