Compressed sensing joint range and cross-range MIMO Radar imaging

An efficient two-step procedure for compressed sensing 3D MIMO radar

“…Thus, the conditions on R(k) are similar to those found for joint range and cross-range recovery in [3]. Additionally, if C is a discrete Fourier matrix with equally spaced bases, we have the appropriate conditions to attain the lower bound for the mutual coherence of F derived in that paper, which assumes direct recovery of x from y.…”

“…Although the main part of the target is recovered, the detached piece with lower reflection coefficient is obscured by the interference pattern, even in high SNR. Figures 4 (a) and (b) show the result for recovering the image in a single CAMP step, as in [3]. The use of compressed sensing techniques for joint range and cross-range imaging greatly improves the reconstruction, even in low SNR.…”

“…The first one is a CAMP-based implementation of [3], where we recover x directly from y. In this version, each algorithm iteration requires the computation of SCx and C * S * y.…”

“…We present two efficient algorithms for CS radar imaging, and compare their simulated results: the first one is the iterative implementation of the joint range/cross-range formation of [3], while the second is the proposed two-step implementation. Besides efficiency, simulations indicate that the two-step version is more robust, as the regularization parameters can be adjusted separately.…”

“…Recently, in [3], we have approached the MIMO radar problem as a joint range/cross-range convolution model, and obtained approximate conditions for which the mutual coherence of the overall system sensing matrix is minimized. In this paper, we take a step further by decoupling the full Radar relation into two separate sparse problems, albeit ones that exhibit more structured models, suitable for efficient implementations.…”