Sparsity-based space-time adaptive processing using OFDM radar

We propose a sparsity-based space-time adaptive processing (STAP) algorithm to detect a slowly-moving target using an orthogonal frequency division multiplexing (OFDM) radar. We observe that the target and interference spectra are inherently sparse in the spatio-temporal domain, and hence we exploit that sparsity to develop an efficient STAP technique. In addition, the use of an OFDM signal increases the frequency diversity of our system, as different scattering centers of a target resonate at different frequencies, and thus improves the target detectability. First, we formulate a realistic sparse-measurement model for an OFDM radar considering both the clutter and jammer as the interfering sources. Then, we show that the optimal STAP-filter weight-vector is equal to the generalized eigenvector corresponding to the minimum generalized eigenvalue of the interference and target covariance matrices. To estimate the target and interference covariance matrices, we apply a residual sparse-recovery technique that enables us to incorporate the partially known support of the sparse vector. Our numerical results demonstrate that the sparsity-based STAP algorithm, with considerably lesser number of secondary data, produces an equivalent performance as the other existing STAP techniques.