Wang Zhiyi, Wang Min, Wu Xinghui, Yang Shuyuan
National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.
Information Center, Xi'an University of Finance and Economics, Xi'an 710100, China.
Sensors (Basel). 2025 Mar 21;25(7):1964. doi: 10.3390/s25071964.
When deploying multi-node small radar networks in cities or mountains, it is crucial to consider the influence of terrain. The propagation of radio waves in areas with known three-dimensional (3D) terrain differs significantly from that in free space. However, existing radar deployment optimization methods often rely on simplistic propagation models that do not accurately capture the variations in coverage at different heights. Therefore, the parabolic equation model (PEM) is first introduced to radar network deployment considering terrain constraints. After obtaining coverage results for different altitude layers, the Layered Effective Coverage Rate (LECR) is proposed as our optimization objective. Then, the nondominated sorting genetic algorithm III (NSGA-III) is employed to address this multi-objective optimization problem. Finally, the experimental results demonstrate the superiority of introducing PEM and the effectiveness of NSGA-III.
在城市或山区部署多节点小型雷达网络时，考虑地形的影响至关重要。在已知三维（3D）地形的区域中，无线电波的传播与在自由空间中的传播有显著差异。然而，现有的雷达部署优化方法通常依赖于简单的传播模型，这些模型无法准确捕捉不同高度处覆盖范围的变化。因此，首先将抛物线方程模型（PEM）引入到考虑地形约束的雷达网络部署中。在获得不同海拔层的覆盖结果后，提出分层有效覆盖率（LECR）作为我们的优化目标。然后，采用非支配排序遗传算法III（NSGA-III）来解决这个多目标优化问题。最后，实验结果证明了引入PEM的优越性以及NSGA-III的有效性。
