Enhancing Robustness for Polarimetric Radar: Worst-Case SINR Optimization With Constrained Transceiver

Polarization introduces an additional dimension to radar signals, providing significant advantages in aerospace applications. Constrained transceiver design for maximizing the Signal-to-Interference-plus-Noise Ratio (SINR) is critical in fully polarimetric radar systems. Existing methods primarily focus on average SINR maximization, which lacks robustness in uncertain regions. To address this, we propose a method for worst-case SINR maximization, aiming to maximize the minimum SINR within uncertain regions, subject to the constant modulus constraint of the waveform and the complex sphere constraint of the receive filter. The problem is non-smooth and non-convex, making existing methods inapplicable. To overcome this, we introduce an auxiliary variable to smooth the problem using the LogSumExp function, which transforms the non-smooth objective into a smooth one by converting the inequality constraints into a continuous, differentiable form. Additionally, we propose the Inequality Constrained Product Manifold Optimization (ICPMO) framework, which guarantees convergence. Experimental results based on measured data show that our method outperforms existing methods in terms of worst-case SINR and target detection probability.