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Random Finite Set Based Parameter Estimation Algorithm for Identifying Stochastic Systems.

Peng Wang1, Ge Li1, Yong Peng1

  • 1College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.

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|December 3, 2020
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Summary
This summary is machine-generated.

This study introduces a novel random finite set algorithm for Bayesian parameter estimation in stochastic systems. The new method effectively handles varying numbers of elements, false detections, and noise, improving system identification accuracy.

Keywords:
Markov Chain Monte Carlo (MCMC)Probability Hypothesis Density (PHD)Random Finite Set (RFS)importance samplingparameter estimationsimulated tempering

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Area of Science:

  • System Identification
  • Stochastic Systems
  • Bayesian Inference

Background:

  • Parameter estimation is crucial for system identification, with Bayesian algorithms being vital for stochastic systems.
  • Existing Bayesian methods face challenges with complex systems and noisy data.

Purpose of the Study:

  • To propose a novel random finite set-based algorithm for Bayesian parameter estimation.
  • To address limitations of current algorithms in identifying stochastic systems with varying components and unreliable measurements.

Main Methods:

  • Development of a random finite set-based system and measurement model.
  • Detailed derivation of the proposed algorithm's principles and formulas.
  • Implementation using sequential Monte Carlo Probability Hypothesis Density (PHD) filter and simulated tempering importance sampling.

Main Results:

  • The algorithm successfully estimates unknown parameters in stochastic systems with false detections, missed detections, and noise.
  • Experiments on systematic errors in multiple sensors demonstrate the algorithm's advantages.
  • Sensitivity analysis confirms the algorithm's robust mechanism.

Conclusions:

  • The proposed random finite set algorithm offers a superior approach to Bayesian parameter estimation for complex stochastic systems.
  • The method effectively handles measurement uncertainties and system variations.
  • Experimental validation confirms the algorithm's effectiveness and advantages over existing techniques.