加权最小二乘联合遗传算法的无源定位

Passive Location Based on Weighted Least Squares and Genetic Algorithm

针对复杂环境下运动通信辐射源的无源定位,闭式解方法对于时频差模型中的测量噪声敏感且存在定位均方根误差较大问题.为了改善大观测误差下的定位性能,本文提出一种加权最小二乘联合遗传算法的递推式混合TDOA/FDOA定位方法.该方法首先利用已知站点观测大量时频差数据并建立误差模型,基于模型对定位过程中的多组时频差序列进行数据处理;其次通过加权最小二乘求解目标位置的初始值;然后采用改进的遗传算法在初始值的基础上通过多组时频差序列不断迭代、递推求解,修正位置坐标;最后利用位置估计和频差模型完成对目标速度估计.仿真结果表明,本文定位算法相比于经典两步加权最小二乘法具有更低的均方根误差,在大观测误差下能保持较高精度.同时相比于其他混合定位算法收敛速度快,可以有效减少计算量.

For the passive location of radiation sources for motion communications in complex environments,the closedform solution method is sensitive to measurement noise in time-frequency difference models and has a large root-meansquare error of location.To improve the location performance under large observation errors,this study proposes a recursive hybrid TDOA/FDOA location method,which is based on weighted least squares and the genetic algorithm.Firstly,massive time-frequency difference data are observed at known stations,and error models are built.On this basis of the models,multiple sets of time-frequency difference sequences are processed.Secondly,the initial value of the target position is solved by weighted least squares.Given the initial value,the improved genetic algorithm is used to solve and correct the position coordinates through multiple groups of time-frequency difference sequences iteratively and recursively.Finally,position estimation and the frequency difference model are used to estimate the target velocity.The simulations show that the proposed location algorithm has a lower root-mean-square error than the classical two-step weighted least squares method and can maintain high accuracy under large observation errors.Moreover,compared with other hybrid location algorithms,the proposed algorithm boasts a fast convergence speed and can effectively reduce the amount of computation.