无需辅助变量约束总体最小二乘时差定位算法

    Constrained Total Least Squares Algorithm for TDOA Localization without the Auxiliary Variable

    • 摘要: 已有时差定位算法通常需将距离作为辅助变量来得到线性定位方程,需要两次目标状态解算且辅助变量变化时无法用于连续定位。文中推导了三维空间中只与目标状态有关的线性时差定位方程,在此基础上提出适于连续定位的约束总体最小二乘算法,无需处理任何辅助变量。该算法将新定位方程中观测矩阵和观测向量的每一列表示为矩阵与时差测量误差向量乘积的形式,从而将时差定位问题转化为约束总体最小二乘问题,最后通过牛顿法求得定位解。仿真结果表明,该提算法性能逼近克拉美罗下界且相对已有典型连续定位算法定位性能更好。

       

      Abstract: The existing time difference of arrival (TDOA) localization algorithms usually need to add the distance as an auxiliary variable to get the linear localization equation, which causes a two-step target state calculation and the unavailability for continuous localization when the auxiliary variable varies. Therefore, a linear localization equation is deduced relating only to the target state. Based on it, a constrained total least squares (CTLS) algorithm suitable for the continuous localization is proposed without dealing any auxiliary variable. expresses Each column of the measurement matrix and vector is expressed as the product of a matrix and the TDOA measurement error vector by the proposed algorithm. Then, the TDOA localization problem is transformed into a CTLS problem, which can be solved by the Newton method. Simulation results show that the proposed algorithm can achieve the Cramer-Rao lower bound and has better performance than existing typical continuous localization algorithms.

       

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