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<p>To address the challenges of state estimation in switching time-varying systems with unknown but bounded noises, we first construct a feasible set from the particle distribution. This allows for a more effective representation of system uncertainty. The feasible set is then iteratively contracted to enhance compactness and tightly bound particle state deviations. Besides, the proposed algorithm combines the contracted zonotope with zonotopic Kalman filter to update the initial zonotope, refining the state estimate. The proposed set-valued based particle filter (SV-PF) can improve both the accuracy of state estimation and the representation of uncertainty in dynamic systems. Finally, the SV-PF algorithm is demonstrated to outperform other related methods in terms of robustness and precision, offering a reliable solution for real-time state estimation of switching time-varying systems.</p>

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Design of a Novel Set-Valued Particle Filter for Switching Time-Varying System with Bounded Noise

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Ziyun Wang,
  • Songsan Li,
  • Yan Wang,
  • Zhicheng Ji

摘要

To address the challenges of state estimation in switching time-varying systems with unknown but bounded noises, we first construct a feasible set from the particle distribution. This allows for a more effective representation of system uncertainty. The feasible set is then iteratively contracted to enhance compactness and tightly bound particle state deviations. Besides, the proposed algorithm combines the contracted zonotope with zonotopic Kalman filter to update the initial zonotope, refining the state estimate. The proposed set-valued based particle filter (SV-PF) can improve both the accuracy of state estimation and the representation of uncertainty in dynamic systems. Finally, the SV-PF algorithm is demonstrated to outperform other related methods in terms of robustness and precision, offering a reliable solution for real-time state estimation of switching time-varying systems.