改进的非完全约束加权最小二乘TDOA/FDOA无源定位方法

针对目前时差定位/频差定位混合无源定位算法存在的定位均方根误差(root mean square error,RMSE)和定位偏差适应测量噪声能力差的问题,提出一种基于泰勒级数展开的非完全约束加权最小二乘法。首先将无源定位问题转化为二次规划问题,简化约束条件,应用拉格朗日乘子法求解目标定位的值。然后将得到的解在原约束条件下进行泰勒级数展开,利用获得的结果进一步优化解析解。计算机仿真对比了所提方法和两步加权最小二乘法(two-stage weighted least squares,TSWLS)、改进的约束加权最小二乘法(constrained weighted least squares,CWLS)、基于定位误差修正方法的定位性能,所提算法在兼顾实时性的同时,RMSE和定位偏差均低于TSWLS、CWLS、基于定位误差修正方法。

Improved incomplete constrained weighted least squares TDOA/FDOA passive location method#br#

In view of the existing problem that the root mean square error (RMSE) and the location deviation are adapted to the problem of poor measurement noise ability with time difference of arrival (TDOA)/ frequency difference of arrival (FDOA) hybrid passive location algorithm, an incomplete constrained weighted least squares (CWLS) method based on Taylor series expansion is proposed. Firstly, the passive location problem is transformed into the two programming problem, and the constraint condition is simplified, the Lagrange multiplier method is used to calculate the value beam condition of target location, Then, the Taylor series expansion is carried out under the original constraint condition, and the analytical solution is further optimized by using the obtained results. The computer simulation compares the positioning performance of the proposed method and the two stage weighted least squares (TSWLS) method, the modified CWLS method and the positioning error correction method. The proposed algorithm takes into account the real time performance, and the RMSE and positioning error are lower than TSWLS, CWLS, based on the positioning error correction method.