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Distributed MIMO radar target altitude estimation for ground-based systems

机译:基于地面系统的分布式MIMO雷达目标高度估计

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This study presents novel target altitude estimation schemes optimised for ground-based multiple-input multiple-output (MIMO) radars with widely separated antennae, where the antennae are located in a plane so that the altitudes of the antennae are close together. For the ground-based systems, existing target localisation methods based on linear least squares such as least square (LS) and bistatic range measurement (BRM) suffer from performance degradation in target altitude estimation and eventually fail in altitude localisation for a perfect ground-based system where the altitude of antennae are the same. The authors propose three target altitude estimation schemes based on linear least squares for the ground-based systems resolving the drawback of existing algorithms for the ground-based systems; extended BRM approach, which estimates the square of target altitude from the auxiliary parameters produced by BRM algorithm, ellipsoid fitting approach, which directly estimates the square of target altitude with other target position information, and extended ellipsoid fitting approach, which utilise the estimation results of ellipsoid fitting approach to improve the target altitude estimation performance. Performance improvement of the proposed algorithms is analysed in terms of mean squared error and simulation results verify the performance.
机译:本研究提出了用于基于地基多输入多输出(MIMO)雷达的新型目标高度估计方案,其具有广泛分离的天线,其中天线位于平面中,使得天线的高度靠近在一起。对于基于地基的系统,基于线性最小二乘(例如最小二乘(LS)和双击范围测量(BRM)的现有目标定位方法遭受目标高度估计中的性能下降,最终在高度定位中失败,以实现完美的地面天线高度的系统是相同的。作者提出了基于基于地面系统的线性最小二乘的三个目标高度估计方案,解决了基于地面系统的现有算法的缺点;扩展BRM方法,其估计来自BRM算法产生的辅助参数,椭球拟合方法的辅助参数的平方,直接估计目标高度与其他目标位置信息的平方,并扩展椭圆拟合方法,其利用估计结果椭球拟合方法提高目标高度估计性能。在平均平方误差和仿真结果方面​​分析了所提出的算法的性能改进验证了性能。

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