A Probabilistic Model-Based Robust Waveform Design for MIMO Radar Detection

TL;DR

This paper introduces a probabilistic model for robust MIMO radar waveform design, employing relative entropy as a detection metric and an MM-based algorithm to enhance robustness against model mismatches.

Contribution

It proposes a novel probabilistic framework and an efficient MM algorithm for robust waveform design in MIMO radar detection, addressing model uncertainty.

Findings

Waveforms designed with the proposed method show improved robustness.

The algorithm outperforms traditional methods under model mismatches.

Numerical results confirm enhanced detection performance.

Abstract

This paper addresses robust waveform design for multiple-input-multiple-output (MIMO) radar detection. A probabilistic model is proposed to describe the target uncertainty. Considering that waveform design based on maximizing the probability of detection is intractable, the relative entropy between the distributions of the observations under two hypotheses (viz., the target is present/absent) is employed as the design metric. To tackle the resulting non-convex optimization problem, an efficient algorithm based on minorization-maximization (MM) is derived. Numerical results demonstrate that the waveform synthesized by the proposed algorithm is more robust to model mismatches.