基于约束加权最小二乘的多站无源定位

针对传统加权最小二乘算法在噪声较大时会出现门限效应的问题。本文将约束加权最小二乘算法应用于多站无源定位，分别提出了基于多站时差定位的约束加权最小二乘算法以及基于时差频差联合定位的约束加权最小二乘算法。算法首先将非线性观测方程转化为两个伪线性方程，然后对伪线性方程加入限制条件，得到目标位置。仿真实验表明，与经典加权最小二乘算法及其改进算法相比，新算法在计算量增大不多的情况下扩展了适用范围，当噪声超过门限值时依然能获得较高的定位精度。新算法对噪声具有较强的鲁棒性，能有效克服门限效应带来的影响。 

Multi-Station Passive Localization Based on CWLS Algorithm

The classical Weighted Least Squares(WLS) algorithm would encounter the thresholding effect when the noise level was in high level. The Constrained Weighted Least Squares(CWLS) algorithm was applied to multi-station passive localization, the CWLS algorithms based on TDOA(time differences of arrival) and TDOA jointed FDOA(frequency differences of arrival) were proposed. The algorithms first transformed the nonlinear equations into the two pseudo-linear equations, and then added the constraints to the pseudo-linear equations and finally achieved the location. Through simulation results, compared with the classical WLS algorithm and its improved algorithm, the new algorithms extended the operating range with little increase in the computation load. In addition, the new algorithms could still obtain an accurate location when the noises rose beyond the thresholding point. That means the new algorithms are robust to noise and can overcome the thresholding effect effectively.