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Adaptive statistical filter providing improved performance for target motion analysis noise discrimination

机译:自适应统计滤波器可改善目标运动分析噪声识别的性能

摘要

The adaptive statistical filter providing improved performance target mot analysis noise discrimination of the present invention includes a bank of parallel Kalman filters. Each filter estimates a statistic vector of specific order, which in the exemplary third order bank of filters of the preferred embodiment, respectively constitute coefficients of a constant, linear and quadratic fit. In addition, each filter provides a sum-of-squares residuals performance index. A sequential comparator is disclosed that performs a likelihood ratio test performed pairwise for a given model order and the next lowest, which indicates whether the tested model orders provide significant information above the next model order. The optimum model order is selected based on testing the highest model orders. A robust, unbiased estimate of minimal rank for information retention providing computational efficiency and improved performance noise discrimination is therewith accomplished.
机译:本发明提供改进的性能目标运动分析噪声判别的自适应统计滤波器包括一组并行的卡尔曼滤波器。每个滤波器估计特定阶数的统计向量,其在优选实施例的示例性三阶滤波器组中分别构成恒定,线性和二次拟合的系数。另外,每个滤波器都提供平方和残差性能指标。公开了一种顺序比较器,该顺序比较器执行针对给定模型阶次和次低阶次成对执行的似然比测试,这表明所测试的模型阶次是否提供高于下一模型阶次的重要信息。基于测试最高模型订单来选择最佳模型订单。从而实现了信息保留的最小秩的鲁棒,无偏估计,从而提供了计算效率和改进的性能噪声辨别力。

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