Alternating direction method of multipliers for radar waveform design in spectrally crowded environments

This paper addresses the radar waveform design problem in spectrally crowded environments. The aim is to maximize the output signal-to-interference-plus-noise ratio (SINR) of the waveform under spectral and similarity constraints. The existing algorithm proposes to tackle such a problem via semidefinite relaxation (SDR) and rank-one decomposition, resulting in high complexity and limited applications. Motivated by the decomposability and superior convergence properties of alternating direction method of multipliers (ADMM), we propose a novel algorithm to tackle the waveform optimization problem. Since simpler subproblems are involved at each iteration and they can be tackled efficiently, the proposed algorithm has a much lower computational complexity than the existing algorithm. Numerical examples demonstrate the effectiveness of the proposed algorithm.