Abstract
Multiple-input-multiple-output (MIMO) radar can transmit and receive different waveforms at the same time, and its waveform design has great freedom. Under the constraints of constant modulus, similarity, integrated sidelobe level (ISL) and peak sidelobe level (PSL), It is a complex nonlinear constraint problem to suppress clutter so as to maximize the signal-to-interference-plus-noise ratio (SINR) in the optimal design of matched output waveform. In this paper, an improved genetic algorithm (IGA) with strong global and local searching ability is obtained by using chaotic map discretization to obtain initialized population, simulated annealing selection operator and adaptive crossover and mutation operator. Simulation examples demonstrate that the proposed algorithm has the advantage of lower computational complexity compared with the existing cyclic algorithms (CA) and primal dual type algorithm (PDT) for MIMO radar waveform design. The optimal waveform obtained has better performance.
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