观测站有位置误差的多维标度时频差定位算法
发布时间：2018-07-13 18:10
【摘要】：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。
[Abstract]:The positioning accuracy is sensitive to the position error of the observation station. In this paper, a weighted multi-dimensional scalar time-frequency difference location algorithm is proposed for the errors in the position and velocity of the observation station. The algorithm takes advantage of the advantages of multi-dimension scale localization to suppress noise through feature structure and dimensional information. The residual error is expressed as a linear form of measurement error and observation station position error. Then the closed solution of target position and velocity estimation is given by weighted least squares. The simulation results show that the estimation of the target position and velocity can reach the lower bound of Clemelaw when the measurement error is small and the observation station position error is small. Compared with the two-step weighted least squares algorithm and constrained global least squares algorithm, the algorithm has higher positioning accuracy when the measurement noise and observation station position error are larger.
【作者单位】： 电子科技大学电子工程学院;盲信号处理重点实验室;
【基金】：国家自然科学基金联合基金(U1533125) 国家科技重大专项(2016ZX03001022)
【分类号】：TN95
[Abstract]:The positioning accuracy is sensitive to the position error of the observation station. In this paper, a weighted multi-dimensional scalar time-frequency difference location algorithm is proposed for the errors in the position and velocity of the observation station. The algorithm takes advantage of the advantages of multi-dimension scale localization to suppress noise through feature structure and dimensional information. The residual error is expressed as a linear form of measurement error and observation station position error. Then the closed solution of target position and velocity estimation is given by weighted least squares. The simulation results show that the estimation of the target position and velocity can reach the lower bound of Clemelaw when the measurement error is small and the observation station position error is small. Compared with the two-step weighted least squares algorithm and constrained global least squares algorithm, the algorithm has higher positioning accuracy when the measurement noise and observation station position error are larger.
【作者单位】： 电子科技大学电子工程学院;盲信号处理重点实验室;
【基金】：国家自然科学基金联合基金(U1533125) 国家科技重大专项(2016ZX03001022)
【分类号】：TN95
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