运动多站无源时差/频差联合定位方法

鉴于无源定位技术已经成为现代信息化作战的核心技术,提出了一种新的运动多站无源时差(time difference of arrival, TDOA)频差(frequency difference of arrival, FDOA)联合定位方法去解决无源定位系统中的非线性最优化问题。通过智能算法的启发,将优化后的基于线性递减权重和物竞天择的粒子群算法(particle swarm optimization algorithm based on linear decreasing weight and natural selection, WSPSO)与经典加权最小二乘算法(weighted least squares, WLS)相联合对目标进行跟踪定位。加权最小二乘定位算法在4个基站的情况下无法实现对辐射源的定位,所得定位结果会出现多解。而所提的运动多站联合定位算法在4个基站的条件下不存在初始目标位置估计和局部收敛等问题就能够实现辐射源的精确定位。通过大量仿真结果分析,本文所提的智能优化定位算法具有更高的目标定位精度和更稳健的定位性能,优于标准粒子群算法与优化PSO算法。

Joint passive location method of TDOA and FDOA for moving multi-station

Passive location technology has become the core technology of modern information warfare, this paper proposes a new joint location method of time difference of arrival (TDOA) and frequency difference of arrival(FDOA) to solve the nonlinear optimization problems. Inspired by the intelligent algorithm, a new particle swarm optimization algorithm based on linear decreasing weight and natural selection (WSPSO) is obtained. The WSPSO algorithm and weighted least squares (WLS) algorithm are combined to realize the precise location of radiation source. The WLS algorithm needs more than four base stations to complete the accurate estimation of target location, otherwise there will be multiple solutions. In the case of four base stations, the multi station cooperative localization algorithm proposed in this paper can approximate the global optimal solution without the estimation of initial position and local convergence problem. Simulation results demonstrate that the proposed method has higher locating precision and stable performance compared to standard particle swarm algorithm and PSO algorithm with the increase of measurement noise.