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The paper reviews nonlinear filtering algorithms based on Monte Carlo methods to assess their design principles and applications.

February 26, 2026

Monte Carlo Methods in Measurement Data Processing Using Resampling Procedures

Key Points

The paper reviews nonlinear filtering algorithms based on Monte Carlo methods to assess their design principles and applications.
Discusses nonlinear filtering algorithms and their advantages and disadvantages.
Focuses on sequential algorithms that use importance sampling and resampling procedures.
Demonstrates application of these algorithms in navigation data processing.
Identifies key advantages of Monte Carlo methods in filtering.
Highlights limitations of current algorithms and techniques for improvement.

Abstract

This paper presents a review of nonlinear filtering algorithms based on Monte Carlo methods (MCMs). The general principles of their design, as well as their advantages and disadvantages are discussed. The primary focus is on sequential (recursive) algorithms that use sequential importance sampling and resampling procedures. The application of these algorithms to navigation data processing is also demonstrated.

Monte Carlo Methods in Measurement Data Processing Using Resampling Procedures

Key Points

Abstract

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