Array Position Optimisation for Compressed Sensing MIMO Radar Based on Mutual Coherence Minimisation

In this paper, an optimization methodology for re-positioning antenna elements of a collocated Compressed Sensing (CS) based Multiple Input Multiple Output (MIMO) radar, to improve target detection performance, by minimizing the mutual coherence of the associated sensing matrix has been suggested. We initialize the problem as a mutual coherence of the sensing matrix resulting from a simple 3Tx/4Rx Uniform Linear Array (ULA) restricted by an array aperture of specified size, and then reposition the elements within the restricted aperture such that the value of mutual coherence reduces. The optimization problem is formulated as minimizing the l ∞ norm of the Gramian of the associated sensing matrix, the global optimization solver simulated annealing is considered to solve the nonconvex problem. The optimized array’s performance is evaluated against a ULA, Co-prime array, and Sparse array by comparing metrics such as the probability of perfect reconstruction (Recovery percentage) and Recovery error (root mean square error (rmse)) for scenes with multiple targets and different SNR values, using Monte Carlo simulations. The study demonstrates the methodology to generate a random array, which results in low mutual coherence of its respective sensing matrix, which consequently results in improved performance of the CS-MIMO radar.