Sequence optimisation for compressed sensing CDMA MIMO radar via mutual coherence minimisation

The authors focus on the waveform design for Code Division Multiple Access Multiple Input Multiple Output (CDMA-MIMO) radar systems, with a specific emphasis on Compressed Sensing (CS) based target estimation. The selection of an appropriate waveform is a critical determinant in the effectiveness of estimation algorithms. Recent studies show the possibilities of optimising waveform parameters to improve the efficiency of CS based estimation. The authors introduce an optimisation framework designed to modify the phase components of code sequences used in CS-CDMA MIMO radar systems. The objective of this optimisation is to minimise the l<inf>∞</inf> norm of off-diagonal elements within the Gramian matrix of the underlying sensing matrix, focusing on phase modulation of the waveform. Solving this optimisation problem requires dealing with a non-convex, combinatorial and non-linear scenario. Simulated Annealing is employed as the solution technique. To assess the effectiveness of the proposed optimisation approach, the resulting optimised sequence is rigorously compared against well-established Hadamard and Gold sequences across various performance metrics. These metrics encompass correlation properties, ambiguity function behaviour, recovery percentage and recovery error. The study demonstrates that the generated poly-phase sequences outperform existing sequences, leading to significantly improved target reconstruction results in the context of CDMA-MIMO radar systems with CS-based estimation.