基于正则约束总体最小二乘无源测角定位  

Passive Localization Using Bearing-Only Measurements Based on Regularized Constrained Total Least Squares Algorithm

作　　者：朱颖童 许锦[1] 赵国庆[1] 饶鲜[1] 

机构地区：[1]西安电子科技大学电子信息攻防对抗与仿真技术教育部重点实验室,西安710071

出　　处：《北京邮电大学学报》2015年第6期55-59,共5页Journal of Beijing University of Posts and Telecommunications

基　　金：国家重点基础研究发展计划(973计划)项目(6131812012);中央高校基本科研业务费专项资金资助(JDZD140503;JDYB140810)

摘　　要：提出了一种基于正则约束总体最小二乘(RCTLS)无源测角定位算法.首先将非线性测角定位方程转化为线性方程,根据线性方程系数的一阶泰勒近似得到测角噪声与方程系数噪声之间的线性映射,再基于RCTLS算法得到定位目标函数,对其求偏导并忽略噪声高阶项得到定位结果的近似闭式解,通过对RCTLS算法的偏差和均方差进行分析确定正则化参数.理论分析和仿真实验表明,该算法在观测站数目较少和角度测量噪声较高时与之前的算法相比定位精度有所提高.The passive location algorithm based on regularized constrained total least squares algorithm（ RCTLS） using bearing-only measurements was presented. By this algorithm the non-linear measurement equations are firstly transformed into linear equations,and linear mapping from the measurement noise to the coefficients error is given in accordance with the first order term of Taylor expansion for the coefficients of linear equations about bearing measurements. A quasi-closed-form solution to location was obtained by taking partial derivative of the objective function formed on the basis of RCTLS and ignoring high order terms of the bearing measures. The regularization parameter is determined by analyzing the bias and MSE of RCTLS. Simulations prove that the proposed algorithm achieves better location accuracy than the previous algorithms in the case of fewer observations and higher measurement noise.

关 键 词：无源定位 正则约束总体最小二乘 仅测角 

分 类 号：TN971[电子电信—信号与信息处理]