The constrained total least squares algorithm for the passive location is presented based on the bearing-only measurements in this paper. By this algorithm the non-linear measurement equations are firstly transformed ...The constrained total least squares algorithm for the passive location is presented based on the bearing-only measurements in this paper. By this algorithm the non-linear measurement equations are firstly transformed into linear equations and the effect of the measurement noise on the linear equation coefficients is analyzed, therefore the problem of the passive location can be considered as the problem of constrained total least squares, then the problem is changed into the optimized question without restraint which can be solved by the Newton algorithm, and finally the analysis of the location accuracy is given. The simulation results prove that the new algorithm is effective and practicable.展开更多
Based on the constrained total least squares （CTLS） passive location algorithm with bearing-only measurements, in this paper, the same passive location problem is transformed into the structured total least squares ...Based on the constrained total least squares （CTLS） passive location algorithm with bearing-only measurements, in this paper, the same passive location problem is transformed into the structured total least squares （STLS） problem.The solution of the STLS problem for passive location can be obtained using the inverse iteration method.It also expatiates that both the STLS algorithm and the CTLS algorithm have the same location mean squares error under certain condition.Finally, the article presents a kind of location and tracking algorithm for moving target by combining STLS location algorithm with Kalman filter （KF）.The efficiency and superiority of the proposed algorithms can be confirmed by computer simulation results.展开更多
目前基于到达时间差(Time Difference of Arrival,TDOA)的无线定位算法既不能在基于距离平方差(Squared Range Difference,SRD)的误差平方和最小模型中获得总体最小二乘准则下的全局最优解,也不能在基于距离差(Range Difference,RD)的...目前基于到达时间差(Time Difference of Arrival,TDOA)的无线定位算法既不能在基于距离平方差(Squared Range Difference,SRD)的误差平方和最小模型中获得总体最小二乘准则下的全局最优解,也不能在基于距离差(Range Difference,RD)的误差平方和最小模型中获得普通最小二乘准则下的全局最优解。将泰勒级数法与约束总体最小二乘法(Constraint Total Least Square,CTLS)相结合,提出一种基于约束总体最小二乘的泰勒级数定位算法(CTLS Taylor)。利用CTLS方法获得目标节点的粗估计位置,并将该位置作为泰勒级数展开法的初始点,通过迭代,获得目标节点的精估计位置。仿真结果表明,CTLS Taylor算法不仅能够获得与QCLS Taylor算法相同的定位精度,而且迭代次数有了明显减少;同时与CTLS定位算法相比,当测量噪声较高时,CTLS Taylor算法的定位精度更高。展开更多
文摘The constrained total least squares algorithm for the passive location is presented based on the bearing-only measurements in this paper. By this algorithm the non-linear measurement equations are firstly transformed into linear equations and the effect of the measurement noise on the linear equation coefficients is analyzed, therefore the problem of the passive location can be considered as the problem of constrained total least squares, then the problem is changed into the optimized question without restraint which can be solved by the Newton algorithm, and finally the analysis of the location accuracy is given. The simulation results prove that the new algorithm is effective and practicable.
文摘Based on the constrained total least squares （CTLS） passive location algorithm with bearing-only measurements, in this paper, the same passive location problem is transformed into the structured total least squares （STLS） problem.The solution of the STLS problem for passive location can be obtained using the inverse iteration method.It also expatiates that both the STLS algorithm and the CTLS algorithm have the same location mean squares error under certain condition.Finally, the article presents a kind of location and tracking algorithm for moving target by combining STLS location algorithm with Kalman filter （KF）.The efficiency and superiority of the proposed algorithms can be confirmed by computer simulation results.
文摘目前基于到达时间差(Time Difference of Arrival,TDOA)的无线定位算法既不能在基于距离平方差(Squared Range Difference,SRD)的误差平方和最小模型中获得总体最小二乘准则下的全局最优解,也不能在基于距离差(Range Difference,RD)的误差平方和最小模型中获得普通最小二乘准则下的全局最优解。将泰勒级数法与约束总体最小二乘法(Constraint Total Least Square,CTLS)相结合,提出一种基于约束总体最小二乘的泰勒级数定位算法(CTLS Taylor)。利用CTLS方法获得目标节点的粗估计位置,并将该位置作为泰勒级数展开法的初始点,通过迭代,获得目标节点的精估计位置。仿真结果表明,CTLS Taylor算法不仅能够获得与QCLS Taylor算法相同的定位精度,而且迭代次数有了明显减少;同时与CTLS定位算法相比,当测量噪声较高时,CTLS Taylor算法的定位精度更高。
