基于改进自适应乌鸦搜索算法的无源定位

为解决利用时差(time difference of arrival,TDOA)信息无源定位计算困难的问题,引入乌鸦搜索算法(crow search algorith,CSA),针对该算法易陷入局部极值,提出一种改进自适应乌鸦搜索算法(adaptive crow search algo-rithm,ACSA).综合考虑...

Improved adaptive crow search algorithm based on passive location

Crow search algorithm (CSA) is usually introduced to solve the problem of using time difference of arrival (TDOA) information to calculate the passive location, however it is easy to fall into local extremum. In this paper, an improved adaptive crow search algorithm (ACSA) is proposed. Considering the overall change of population increasing with evolutionary algebra, balancing the global search ability and local optimization ability in the iterative process of the algorithm, an adaptive perceptual probability model is designed to keep more excellent individuals in the initial stage, ensure the diversity of population, avoid local best and quickly converge in the later stage. The theoretical and simulation results show that the improved algorithm is superior to Taylor algorithm and other similar algorithms, and the convergence speed is also improved significantly, and the algorithm has the advantages of high precision and robustness.