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Analysis on Strong Tracking Filtering for Linear Dynamic Systems

机译：线性动力系统的强跟踪滤波分析

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摘要

Strong tracking filtering (STF) is a popular adaptive estimation method to effectively deal with state estimation for linear and nonlinear dynamic systems with inaccurate models or sudden change of state. The key of the STF is to use a time-variant fading factor, which can be evaluated based on the current measurement innovation in real time, to forcefully correct one step state prediction error covariance. The strong tracking filtering technology has been extensively applied in many practical systems, but the theoretical analysis is highly lacking. In an effort to better understand STF, a novel analysis framework is developed for the strong tracking filtering and some new problems are discussed for the first time. For this, we propose a new perspective that correcting the state prediction error covariance by using the fading factor can be thought of directly modifying the state model by correcting the covariance of the process noise. Based on this proposed point of view, the conditions for the STF function to be effective are deeply analyzed in a certain linear dynamic system. Meanwhile, issues of false alarm and alarm failure are also briefly discussed for the strong tracking filtering function. Some numerical simulation examples are demonstrated to validate the results.
机译：强跟踪滤波（STF）是一种流行的自适应估计方法，可以有效地处理模型不正确或状态突然变化的线性和非线性动态系统的状态估计。 STF的关键是使用时变衰落因子（可以基于当前的测量创新实时评估该时变衰落因子）来强制校正一步状态预测误差协方差。强跟踪滤波技术已经在许多实际系统中得到了广泛应用，但是却缺乏理论分析。为了更好地理解STF，开发了一种用于强跟踪滤波的新颖分析框架，并首次讨论了一些新问题。为此，我们提出了一个新的观点，即可以认为通过使用衰落因子来校正状态预测误差的协方差可以通过校正过程噪声的协方差来直接修改状态模型。基于这种观点，在一定的线性动力系统中深入分析了STF函数有效的条件。同时，还简要讨论了虚假警报和警报失败的问题，以实现强大的跟踪过滤功能。演示了一些数值示例，以验证结果。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第16期|648125.1-648125.9|共9页
  • 作者

    Ge Quanbo; Shao Teng; Wen Chenglin; Sun Ruoyu;

  • 作者单位

    Hangzhou Dianzi Univ, Inst Syst Sci & Control Engn, Sch Automat, Hangzhou 310018, Peoples R China.;

    Hangzhou Dianzi Univ, Inst Syst Sci & Control Engn, Sch Automat, Hangzhou 310018, Peoples R China.;

    Henan Univ Technol, Coll Elect Engn, Zhengzhou 450001, Peoples R China.;

    Univ Minnesota, Dept Elect & Comp Engn, Minneapolis, MN 55414 USA.;

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