Monte Carlo Methods in Measurement Data Processing Using Resampling Procedures

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.