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定位精度对观测站位置误差很敏感。论文针对观测站位置和速度有误差的情况,提出了一种加权多维标度时频差定位算法。该算法利用了多维标度定位通过特征结构和维度信息抑制噪声的优势,它将定位残差表示成测量误差和观测站位置误差的线性形式,然后通过加权最小二乘给出了目标位置和速度估计的闭式解。仿真结果表明,在小的测量误差和观测站位置误差时,该算法对目标位置和速度的估计能够达到克拉美劳下界。与两步加权最小二乘和约束总体最小二乘算法相比,该算法在测量噪声和观测站位置误差较大时有更高的定位精度。
Positioning accuracy of the station is very sensitive to position error. In this paper, aiming at the error of observing station’s location and velocity, a weighted multi-dimensional scale time-frequency positioning algorithm is proposed. The algorithm utilizes the advantage of multidimensional scaling positioning to suppress noise through feature structure and dimensional information. It represents the positioning residual as a linear form of measurement error and station position error, and then gives the target position and velocity by weighted least squares Estimated closed solution. The simulation results show that the algorithm can estimate the target position and velocity to reach the Kramer boundary under the condition of small measurement error and station position error. Compared with the two-step weighted least squares and constrained total least squares algorithm, this algorithm has higher positioning accuracy when the measurement noise and the station position error are larger.