Real time filtering algorithms

TL;DR

This paper systematically reviews recent nonlinear filtering algorithms, covering Kalman-type, Monte Carlo, and Yau-Yau methods, highlighting theoretical advances, practical applications, and the impact of AI innovations.

Contribution

It provides a comprehensive, unified review of nonlinear filtering methods across different frameworks, emphasizing recent AI-driven developments and their influence.

Findings

AI breakthroughs are transforming nonlinear filtering

The review covers theoretical and practical aspects of filtering algorithms

Unified perspective on continuous and discrete-time filtering methods

Abstract

This paper presents a systematic review of recent advances in nonlinear filtering algorithms, structured into three principal categories: Kalman-type methods, Monte Carlo methods, and the Yau-Yau algorithm. For each category, we provide a comprehensive synthesis of theoretical developments, algorithmic variants, and practical applications that have emerged in recent years. Importantly, this review addresses both continuous-time and discrete-time system formulations, offering a unified review of filtering methodologies across different frameworks. Furthermore, our analysis reveals the transformative influence of artificial intelligence breakthroughs on the entire nonlinear filtering field, particularly in areas such as learning-based filters, neural network-augmented algorithms, and data-driven approaches.