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Improved target tracking with the converted-measurement Kalman filter.

机译:使用转换测量卡尔曼滤波器改善目标跟踪。

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摘要

For several decades, researchers have studied the problem of tracking a dynamic target given measurements in sensor coordinates. Since the sensor's measurement is a nonlinear function of the target's state corrupted by additive measurement noise, this problem properly qualifies as one of nonlinear estimation. Due to the mathematical intractability of the problem's theoretically optimal solution, researchers have developed numerous suboptimal but mathematically tractable approaches. The converted-measurement Kalman filter (CMKF), in which the sensor's measurement is converted to Cartesian coordinates and applied to the traditional Kalman filter's tracking algorithm, represents an approach popular in literature and in practice. This dissertation presents two significant contributions to the field of CMKF tracking.First, this dissertation corrects an error in the original algorithm for the debiased CMKF (CMKF-D), an early practical CMKF implementation. In particular, the original paper on the CMKF-D specified, with incorrect mathematical justification, a requirement for evaluating the average true converted-measurement-error bias and covariance with the best available polar target-position estimate. This dissertation provides the correct explanation for the tracking improvement obtained by using the specified requirement.Second, this dissertation contributes a CMKF algorithm employing expressions for the raw converted measurement's error bias and the debiased converted measurement's error covariance conditioned on the best practically available target-position estimate---either the sensor's measurement or the CMKF's Cartesian prediction. A simple test determines the more accurate target-position estimate for use in conditioning the bias and covariance. If the sensor's measurement is more accurate than the CMKF's Cartesian prediction, the resulting sensor-measurement-conditioned bias and covariance produce a CMKF mathematically equivalent to the modified unbiased CMKF (MUCMKF). If, however, the CMKF's prediction is more accurate than the sensor's measurement, two new approaches allow bias and covariance conditioning on the CMKF's prediction. In the first approach, the unscented transformation (UT) produces a target-position estimate in sensor coordinates from the CMKF's Cartesian position prediction and approximates the bias and covariance conditioned on that estimate. In the second approach, the UT approximately conditions the bias and covariance directly on the CMKF's Cartesian position prediction. Simulations demonstrate the improved performance of the new CMKF over the MUCMKF.
机译:几十年来,研究人员研究了在给定传感器坐标的情况下跟踪动态目标的问题。由于传感器的测量值是目标状态的非线性函数,该目标状态会因附加测量噪声而损坏,因此该问题可适当地视为非线性估计之一。由于该问题在理论上是最优解的数学难解性,研究人员开发了许多次优但在数学上易处理的方法。转换后的测量卡尔曼滤波器(CMKF)将传感器的测量结果转换为笛卡尔坐标并应用于传统的卡尔曼滤波器的跟踪算法,它代表了一种在文学和实践中流行的方法。本文为CMKF跟踪领域提供了两个重要的贡献。首先,本文纠正了早期的CMKF实际实现方法,即去偏CMKF(CMKF-D)的原始算法中的错误。特别是,在CMKF-D上的原始论文特别指出了数学上的不正确之处,要求评估平均真实转换测量误差偏差和协方差以及最佳可用极地目标位置估计值。第二,本文提出了一种CMKF算法,该算法利用表达式表示原始转换测量的误差偏差和以最佳可行目标位置为条件的偏移转换测量的误差协方差。估计-传感器的测量值或CMKF的笛卡尔预测值。一个简单的测试可以确定更准确的目标位置估算值,以用于调整偏差和协方差。如果传感器的测量比CMKF的笛卡尔预测更准确,则所得的传感器测量条件偏置和协方差将在数学上等效于修改的无偏CMKF(MUCMKF)。但是,如果CMKF的预测比传感器的测量更为准确,则可以使用两种新方法对CMKF的预测进行偏差和协方差调节。在第一种方法中,无味变换(UT)根据CMKF的笛卡尔位置预测在传感器坐标中生成目标位置估算值,并根据该估算值近似偏差和协方差。在第二种方法中,UT直接在CMKF的笛卡尔位置预测上近似地调节偏差和协方差。仿真表明,新CMKF的性能优于MUCMKF。

著录项

  • 作者

    Spitzmiller, John N.;

  • 作者单位

    The University of Alabama in Huntsville.;

  • 授予单位 The University of Alabama in Huntsville.;
  • 学科 Engineering, Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 165 p.
  • 总页数 165
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 TS97-4;
  • 原文服务方 国家工程技术数字图书馆
  • 关键词

    ;

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