Darányi András, Ruppert Tamás, Abonyi János
HUN-REN-PE Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem u. 10, H-8200, Veszprém, Hungary.
Heliyon. 2024 Apr 12;10(8):e29437. doi: 10.1016/j.heliyon.2024.e29437. eCollection 2024 Apr 30.
This paper presents a methodology that aims to enhance the accuracy of probability density estimation in mobility pattern analysis by integrating prior knowledge of system dynamics and contextual information into the particle filter algorithm. The quality of the data used for density estimation is often inadequate due to measurement noise, which significantly influences the distribution of the measurement data. Thus, it is crucial to augment the information content of the input data by incorporating additional sources of information beyond the measured position data. These other sources can include the dynamic model of movement and the spatial model of the environment, which influences motion patterns. To effectively combine the information provided by positional measurements with system and environment models, the particle filter algorithm is employed, which generates discrete probability distributions. By subjecting these discrete distributions to exploratory techniques, it becomes possible to extract more certain information compared to using raw measurement data alone. Consequently, this study proposes a methodology in which probability density estimation is not solely based on raw positional data but rather on probability-weighted samples generated through the particle filter. This approach yields more compact and precise modeling distributions. Specifically, the method is applied to process position measurement data using a nonparametric density estimator known as kernel density estimation. The proposed methodology is thoroughly tested and validated using information-theoretic and probability metrics. The applicability of the methodology is demonstrated through a practical example of mobility pattern analysis based on forklift data in a warehouse environment.
本文提出了一种方法，旨在通过将系统动力学的先验知识和上下文信息集成到粒子滤波算法中，提高移动模式分析中概率密度估计的准确性。由于测量噪声，用于密度估计的数据质量往往不足，这会显著影响测量数据的分布。因此，通过纳入测量位置数据之外的其他信息源来增加输入数据的信息含量至关重要。这些其他信息源可以包括运动的动态模型和影响运动模式的环境空间模型。为了有效地将位置测量提供的信息与系统和环境模型相结合，采用了粒子滤波算法，该算法生成离散概率分布。通过对这些离散分布应用探索性技术，与仅使用原始测量数据相比，可以提取更确定的信息。因此，本研究提出了一种方法，其中概率密度估计不仅基于原始位置数据，还基于通过粒子滤波生成的概率加权样本。这种方法产生更紧凑和精确的建模分布。具体而言，该方法应用非参数密度估计器（称为核密度估计）来处理位置测量数据。使用信息论和概率度量对所提出的方法进行了全面测试和验证。通过基于仓库环境中叉车数据的移动模式分析的实际示例，证明了该方法的适用性。
