基于粒子群算法的多波位高度估计方法

为解决雷达斜视模式下对目标区域的高度估计问题,提出了一种基于粒子群算法的多波位高度估计方法。利用粒子群优化算法建立约束条件和目标函数,通过多次迭代使目标函数趋于一个最优值,同时得到高度的最优解。利用粒子群优化算法求解多波位测高方程组可减小方程组近似处理误差。此外,利用粒子群优化算法可随时更改波位数目,增强了该测高方法使用的灵活性,有效提高了高度估计精度。通过理论仿真和实测数据仿真分析,验证了粒子群优化算法在求解多波位测高方程组时的有效性。结果表明:该方法具有较高的高度估计精度。

Multi-Beam Altitude Estimation Method Based on Particle Swarm Optimization

In order to estimate the altitude of the target area in the radar squint mode, a kind of multi-beam altitude estimation method based on particle swarm optimization algorithm is proposed. The particle swarm optimization algorithm is used to establish constraints and objective function. Through multiple iterations, the objective function tends to be an optimal value, and a highly optimal solution is obtained. The particle swarm optimization reduces the approximate processing error of the equations. In addition, the use of particle swarm optimization algorithm allows the number of beams to be changed at any time, which enhances the flexibility of the altimetry method and effectively improves the accuracy of the estimation. Through theoretical simulation and actual data simulation analysis, the effectiveness of the particle swarm optimization algorithm in solving the multi-beam height measurement equations is verified. The results show that the algorithm achieves a relatively high accuracy in altitude estimation.