Estimating Parameters of Nonlinear Systems Using the Elitist Particle Filter Based on Evolutionary Strategies
Estimating Parameters of Nonlinear Systems Using the Elitist Particle Filter Based on Evolutionary Strategies
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DOI：
10.1109/taslp.2017.2788183
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发表时间：
2016-04
期刊：
影响因子：
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通讯作者：
Christian Huemmer;Christian Hofmann;R. Maas;Walter Kellermann
中科院分区：
文献类型：
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作者：
Christian Huemmer;Christian Hofmann;R. Maas;Walter Kellermann
在本文中，我们提出了基于进化策略的精英粒子滤波器（EPFES）作为一种有效的方法来估计潜在的状态向量捕获的非线性系统的相关信息的统计。类似于经典的粒子滤波，EPFES由一组粒子和各自的权重组成，这些权重表示潜在状态向量的不同实现及其作为优化问题的解决方案的可能性。作为主要的创新，EPFES包括一个进化的精英粒子选择方案，它结合了长期的信息与瞬时采样从一个近似的连续后验分布。在本文中，我们提出了两个先前公布的精英粒子选择过程的进步。此外，EPFES被证明是广泛使用的高斯粒子滤波器的推广，因此相对于后者进行了评估：首先，我们考虑具有时变潜在状态变量的单变量非平稳增长模型来评估EPFES的跟踪能力即时计算的粒子权重。其次是解决单通道非线性声学回声消除的问题，作为一个具有挑战性的基准任务，用于识别一个未知的系统的大搜索空间：非线性声学回声路径建模的级联参数化的预处理器（模型的扬声器信号失真）和线性FIR滤波器（模型的声波传播和麦克风）。通过使用长期的信息，我们突出了良好的概括EPFES在估计预处理器参数的模拟场景和真实的智能手机记录的功效。最后，我们说明了EPFES和进化算法之间的相似之处，通过融合这两个领域的研究成果来概述未来的改进。
In this paper, we present the elitist particle filter based on evolutionary strategies (EPFES) as an efficient approach to estimate the statistics of a latent state vector capturing the relevant information of a nonlinear system. Similar to classical particle filtering, the EPFES consists of a set of particles and respective weights which represent different realizations of the latent state vector and their likelihood of being the solution of the optimization problem. As main innovation, the EPFES includes an evolutionary elitist-particle selection scheme which combines long-term information with instantaneous sampling from an approximated continuous posterior distribution. In this paper, we propose two advancements of the previously published elitist-particle selection process. Further, the EPFES is shown to be a generalization of the widely-used Gaussian particle filter and thus evaluated with respect to the latter: First, we consider the univariate nonstationary growth model with time-variant latent state variable to evaluate the tracking capabilities of the EPFES for instantaneously calculated particle weights. This is followed by addressing the problem of single-channel nonlinear acoustic echo cancellation as a challenging benchmark task for identifying an unknown system of large search space: the nonlinear acoustic echo path is modeled by a cascade of a parameterized preprocessor (to model the loudspeaker signal distortions) and a linear FIR filter (to model the sound wave propagation and the microphone). By using long-term information, we highlight the efficacy of the well-generalizing EPFES in estimating the preprocessor parameters for a simulated scenario and a real smartphone recording. Finally, we illustrate similarities between the EPFES and evolutionary algorithms to outline future improvements by fusing the achievements of both fields of research.