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首页> 外文期刊>IEEE systems journal >Efficient Weighted Least Squares Estimator for Moving Target Localization in Distributed MIMO Radar With Location Uncertainties
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Efficient Weighted Least Squares Estimator for Moving Target Localization in Distributed MIMO Radar With Location Uncertainties

机译：具有位置不确定性的分布式MIMO雷达中移动目标定位的有效加权最小二乘估计

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摘要

An asymptotically efficient estimator for determining the position and velocity of a moving target from time delay and Doppler shift measurements in the presence of location uncertainties in multiple-input multiple-output (MIMO) radar systems with widely separated antennas is presented. It is considered that the actual positions of transmitters and receivers are not available. By taking both the location and measurement errors into account, a novel closed-form two-stage weighted least-squares solution to the problem is developed and analyzed. In order to generate a substantial improvement in the performance of the method, a weighting matrix is employed at each stage of the process. The accuracy properties of the method as well as the Cramer-Rao lower bound (CRLB) for target localization accuracy are derived in the case of Gaussian observations. The proposed algorithm is shown analytically to achieve the CRLB under small noise conditions. Simulations are included to examine the algorithms performance and corroborate the theoretical developments.
机译：提出了一种渐近有效的估计器，该方法用于在具有广泛分离的天线的多输入多输出（MIMO）雷达系统中，在存在位置不确定性的情况下，根据时间延迟和多普勒频移测量确定运动目标的位置和速度。认为发射机和接收机的实际位置不可用。通过同时考虑位置和测量误差，开发并分析了该问题的新型闭合形式两阶段加权最小二乘解。为了在该方法的性能上产生实质性的改进，在过程的每个阶段都采用一个加权矩阵。在高斯观测的情况下，推导了该方法的精度属性以及用于目标定位精度的Cramer-Rao下界（CRLB）。分析性地示出了所提出的算法以在小噪声条件下实现CRLB。包括仿真以检查算法性能并证实理论发展。

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