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Consistency checks for particle filters.

F van der Heijden¹

¹University of Twente, Faculty of EEMCS, PO Box 217, 7500AE Enschede, The Netherlands. F.vanderHeijden@utwente.nl

IEEE Transactions on Pattern Analysis and Machine Intelligence

|January 13, 2006

Summary

Particle filters can exhibit inconsistent behavior, leading to larger errors than expected. New test variables are introduced to detect this statistical inconsistency, with experiments confirming their effectiveness.

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Area of Science:

Signal Processing
Statistical Inference
Machine Learning

Background:

Particle filters are widely used for state estimation in dynamic systems.
Model-based error prediction is crucial for assessing filter performance.
Inconsistent filter behavior can compromise the reliability of estimation results.

Purpose of the Study:

To introduce and analyze novel test variables for detecting inconsistent particle filter behavior.
To provide a statistical framework for evaluating filter performance against model predictions.
To validate the proposed detection method through experimental validation.

Main Methods:

Definition and statistical analysis of two novel test variables.
Theoretical analysis of the statistical properties of these variables.
Experimental validation of the test variables' efficacy in detecting filter inconsistency.

Main Results:

The proposed test variables provide a statistically sound measure of filter inconsistency.
Analysis confirms the sensitivity of these variables to deviations from predicted error bounds.
Experiments demonstrate the practical utility of the variables for inconsistency detection.

Conclusions:

The introduced test variables are effective tools for identifying "inconsistent" particle filters.
This work offers a method to ensure particle filter reliability in real-world applications.
Further research can explore the application of these variables in adaptive filtering algorithms.

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