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Gaussian mixture modeling for long range radar with higher representational efficiency

机译：具有高表示效率的远程雷达的高斯混合建模

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Recent advances in radar systems allow targets to be tracked at longer ranges with wider bandwidth waveforms. The combination of these circumstances leads to a well-known effect known as the contact-lens problem in which the measurement error distribution is highly non-Gaussian in Cartesian space. Recently, Gaussian mixture filters have been proposed to model the non-Gaussian measurement error distribution with higher fidelity. However, this approach suffers from inefficiencies in the number of components needed to achieve a useful result, as the measurement must be modeled with finer granularity as the track converges. This work presents a measurement pre-conditioning approach that may be used to improve the quality of the measurement mixture model in the vicinity of the state PDF without increasing the number of components in the model. The pre-conditioned measurement Gaussian mixture filter is then compared with other popular methods of handling the contact-lens problem in terms of estimation performance, covariance consistency, computational complexity, and gating region size. The improvement in measurement-to-track gating performance is studied and illustrated via Monte Carlo simulations.
机译：雷达系统的最新进展允许在更长的范围内以较宽的带宽波形跟踪目标。这些情况的组合会导致众所周知的效应，称为隐形眼镜问题，其中测量误差分布在笛卡尔空间中是高度非高斯的。最近，已经提出了高斯混合滤波器来对具有更高保真度的非高斯测量误差分布进行建模。但是，这种方法受困于获得有用结果所需的组件数量效率低下，因为在轨道收敛时必须以更精细的粒度对测量进行建模。这项工作提出了一种测量预处理方法，该方法可用于改善状态PDF附近的混合测量模型的质量，而无需增加模型中的组件数。然后将经过预处理的测量高斯混合滤波器与其他处理接触镜问题的常用方法进行比较，这些方法的估计性能，协方差一致性，计算复杂度和门控区域大小都很高。通过蒙特卡洛仿真研究并说明了测量到跟踪门控性能的改进。

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