基于粒子群优化算法的空中目标定位

受干扰条件下,雷达难以对空中目标准确定位。针对这一问题,提出了当多基雷达接收站在空间随机分布时,利用粒子群优化算法解决根据到达时间差对空中目标定位中遇到的非线性最优化问题。所建算法首先初始化一个随机粒子群,然后根据适应度值更新粒子速度和位置,通过迭代搜索最佳坐标。仿真结果表明,在参数设定合理的情况下,该算法性能稳定,能找到逼近全局最优点的解。

Location of Aerial Targets Based on Particle Swarm Optimization

On the condition of being disturbed, the radar is hard to fix the accurate position of the aerial target. Aiming at this problem, it puts forward the particle swarm optimization to solve the nonlinear optimization problem based on TDOA to locate the aerial target when the multi-base radar receiving stations distributed randomly in the space. Firstly, the algorithm initializes a random particle swarm, then alters the velocity and position of the particles in according with the fitness, searches the optimal coordinates through iterative searching method. The experimental results show that if the parameters are assumed reasonably, the algorithm is stable and can find out the global optimal solution.