Abstract
A clustering similarity particle filter based on state trajectory consistency is presented for the mathematical modeling, performance estimation, and smart sensing of nonlinear systems. Starting from an information fusion model based on the consistency principle of the spatial state trajectory, the predicted observation information of the current particle filter (original trajectory) and future multistage Gaussian particle filter (modified trajectory) are selected as the state trajectories of the sampling particles. Clustering similarity methods are used to measure the state trajectories of the sampling particles and the actual system (reference trajectory). The importance weight of a first-order Markov model is updated with the measurement results. By integrating the targeted compensation scheme of the latest measurement information into the sequential importance sampling process, the adverse effects of the particle degradation phenomenon are effectively reduced. The convergence theorems of the improved particle filter are proposed and proved. The improved filter is applied to practical cases of nonlinear process estimation, economic statistical prediction, and battery health assessment, and the simulation results show that the improved particle filter is superior to traditional filters in estimation accuracy, efficiency, and robustness.
Highlights
Nonlinear phenomena are common in natural engineering technology
Facing the difficulties of the standard particle filter (PF) algorithm in nonlinear system state estimation, such as low precision, instability, and low computational efficiency, we proposed an improved PF algorithm (CSPF) based on the consistency principle of the spatial state trajectory
Relying on the model construction of spatial trajectories between sampled particles and actual states, current and future multistage measurement information was predicted by sequential importance sampling (SIS) and Gaussian (sum) particle filter (GPF) to form trajectories combining the original and modified trajectories
Summary
Nonlinear phenomena are common in natural engineering technology. As a popular research topic with important theoretical and practical value in solving nonlinear problems, state estimation has been applied to problems such as target tracking and navigation, fault diagnosis and detection, process feedback and control, biochemical reaction and extraction, and economic prediction and control. The clustering similarity method is used to measure the state distance between the actual system and the sampling particles, including the observation information of the current state filtering and future multistage state prediction, to guide the generation and improvement of new distributions and update the weight calculation of the importance sampling process. This makes up for using the prior PDF instead of the importance function in the standard PF algorithm, which can prevent the occurrence of particle degradation and significantly improve the accuracy and robustness of estimation.
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