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

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