基于正则化约束总体最小二乘的单站DOA-TDOA无源定位算法

赵拥军; 赵勇胜; 赵闯

doi:10.11999/JEIT151379

基于正则化约束总体最小二乘的单站DOA-TDOA无源定位算法

doi: 10.11999/JEIT151379

基金项目:

国家自然科学基金(61401469, 41301481, 61501513)，国家高技术研究发展计划(2012AA7031015)

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出版历程
- 收稿日期: 2015-12-08
- 修回日期: 2016-04-27
- 刊出日期: 2016-09-19

Single-observer Passive DOA-TDOA Location Based on Regularized Constrained Total Least Squares

Funds:

The National Natural Science Foundation of China (61401469, 41301481, 61501513), The National High Technology Research and Development Program of China (2012AA7031015)

摘要

摘要: 针对利用单站外辐射源的目标无源定位问题，该文提出一种联合到达角度和时差信息的正则化约束总体最小二乘(RCTLS)定位算法。首先，将非线性的到达角度和时差的观测方程进行线性化处理，分析了方程系数矩阵可能出现的病态问题，将定位问题建立为RCTLS模型，并采用牛顿迭代方法对模型求解，从而得到目标位置估计。最后，推导了算法的理论误差，并按照均方误差最小的原则推导了正则化参数的最优值。仿真结果表明，算法的定位精度和鲁棒性均优于约束总体最小二乘(CTLS)算法。此外，对系统几何精度因子图的分析表明，目标及外辐射源的位置对定位精度也有影响。
- 无源定位 /
- 到达角度和时差 /
- 单站 /
- 外辐射源 /
- 正则化约束总体最小二乘
Abstract: To solve the single-observer passive location estimation using illuminators of opportunity, a jointing Direction Of Arrival (DOA) and Time Difference Of Arrival (TDOA) location method based on Regularized Constrained Total Least Squares (RCTLS) algorithm is proposed. Firstly, the DOA and TDOA measurement equations are linearized. Considering the errors in the location equations, the localization problem is established as a RCTLS model. Then the Newtons method is applied to solving the RCTLS model to obtain the target position. The theoretical error of the proposed algorithm is derived and an optimal regularization parameter is chosen by the least mean square error rule. Simulation results show that the proposed RCTLS algorithm has lower mean squares error than the Constrained Total Least Squares (CTLS) algorithm. Moreover, from the Geometric Dilution Of Precision (GDOP) figure, it can be concluded that positions of the target and illuminators are also important factors affecting the localization accuracy.
- Passive location /
- DOA and TDOA /
- Single-observer /
- Illuminator of opportunity /
- Regularized Constrained Total Least Squares (RCTLS)

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参考文献(25)

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