Robust Beamforming for MIMO Radar with Imperfect Prior Distribution Information

TL;DR

This paper develops a robust beamforming method for MIMO radar that accounts for imperfect prior distribution information about target angles, optimizing worst-case sensing performance using convex optimization techniques.

Contribution

It introduces a tractable robust beamforming design that minimizes the worst-case posterior Cramér-Rao bound under distribution uncertainty, employing a quadratic approximation and S-procedure.

Findings

The proposed method effectively handles distribution imperfections.

Numerical results show near-optimal performance as uncertainty decreases.

The approach is computationally efficient with polynomial time complexity.

Abstract

This paper studies a multiple-input multiple-output (MIMO) radar system for sensing the unknown and random angular location (angle) of a point target, based on the target-reflected echo signals and known prior distribution information about the target's angle specified by a probability density function (PDF). We consider a challenging yet practical scenario where the knowledge of such PDF is imperfect, due to the inaccuracy in PDF acquisition or unpredicted change of target appearance pattern; while the real (actual) PDF is modeled as an unknown perturbed version of the imperfect known PDF bounded by a given uncertainty radius. Such PDF imperfection motivates us to study the robust transmit beamforming design to optimize the worst-case sensing performance among all possible real PDFs. Since the sensing mean-squared error (MSE) is difficult to be characterized explicitly, we adopt the worst-case posterior Cram\'{e}r-Rao bound (PCRB) as the performance metric. We formulate the beamforming optimization problem to minimize the maximum PCRB among all possible real PDFs, which is highly non-trivial since the PCRB has a complex intractable expression over the real PDF, and there are infinite constraints corresponding to the continuous set of real PDFs bounded by the uncertainty radius. To address these challenges, we derive a tractable quadratic approximation of the PCRB via second-order Taylor expansion, and leverage the S-procedure to equivalently transform the infinite constraints into a linear matrix inequality, based on which the problem is reformulated into a convex optimization problem solvable with polynomial time complexity. The obtained solution approaches the globally optimal robust beamforming solution as the uncertainty radius decreases. Numerical results validate the effectiveness of our proposed robust beamforming design.