Optimizing Power Allocation for Enhanced Multitarget Detection in Multisite MIMO Radar Systems Under Low Grazing Angle Conditions With Imperfect Waveforms

Low-grazing angle detection (LGAD) is a critical challenge in radar systems due to the complex propagation environment and the high probability of signal cancellation. Multisite multiple input multiple output radar systems offer a solution by forming multiple orthogonal beams, each illuminating targets from different angles, thereby enhancing detection capabilities. This article proposes a novel power allocation strategy, LGAD-based power allocation (LGAD-PA), to optimize system efficiency in detecting multiple targets under low-grazing angle conditions. The strategy is based on a Neyman–Pearson detection model that accounts for multipath effects, imperfect waveforms, and measurement uncertainties. The signal-to-interference-plus-noise ratio is derived as the optimization metric, with the multipath distance difference incorporated to enhance the robustness of the max–min optimization model. The LGAD-PA problem is shown to be nonconvex, nonlinear, and nondifferentiable with respect to power variable constraints. To address this, an efficient two-stage technique, the smoothed proximal inexact augmented Lagrange multiplier method, is proposed. This method uses a smooth approximation to ensure a continuously differentiable utility function, enabling near-optimal power allocation with guaranteed convergence. Extensive simulations demonstrate the effectiveness and efficiency of the proposed LGAD-PA strategy in detection performance improvement.