Multispectrally Constrained Low-PAPR Waveform Optimization for MIMO Radar Space-Time Adaptive Processing

This article focuses on the joint design of transmit waveforms and receive filters for airborne multiple-input–multiple-output radar systems in spectrally crowded environments. The purpose is to maximize the output signal-to-interference-plus-noise-ratio in the presence of signal-dependent clutter. To improve the practicability of the radar waveforms, both a multispectral constraint and a peak-to-average-power ratio constraint are imposed. A cyclic method is derived to iteratively optimize the transmit waveforms and receive filters. In particular, to tackle the encountered nonconvex constrained fractional programming in designing the waveforms (for fixed filters), we resort to the Dinkelbach's transform, minorization–maximization, and leverage the alternating direction method of multipliers. We highlight that the proposed algorithm can iterate from an infeasible initial point and the waveforms at convergence not only satisfy the stringent constraints, but also attain superior performance.