Beampattern-Aware Topology and Antenna Pointing Optimization for Distributed Radar Network

Abstract

To enhance the spatial perception capabilities of distributed radar networks (DRNs) in complex environments, this article investigates the coordinated optimization of beampattern-aware topology and antenna pointing for coverage maximization. Unlike prior works that focus exclusively on radar node placement, the proposed framework introduces antenna pointing as an additional optimization dimension, enabling cooperative beam direction adjustment and substantially improving regional coverage. A unified system and signal model is first established, encompassing both transmit and receive channels. Based on this model, we derive the antenna gain for the uniform linear array (ULA) and a detection probability expression that explicitly incorporates antenna pointing effects. Furthermore, a beampattern model is constructed to characterize the influence of pointing errors on radar coverage, and a corresponding quantitative metric is defined to evaluate performance. Leveraging this metric, the coverage maximization problem is formulated as a nonconvex joint optimization over both node positions and antenna orientations. To efficiently solve this high-dimensional problem, an enhanced particle swarm optimization algorithm integrated with the butterfly-optimization-aided particle swarm optimization (BOA-PSO) is adopted, which improves global convergence and search efficiency. Numerical simulations under representative deployment scenarios demonstrate that the proposed method achieves superior coverage and convergence stability, highlighting its potential for practical DRN configuration and resource management.