实时滤波算法
Real time filtering algorithms
AI总结：
本文综述了非线性滤波算法的最新进展，涵盖卡尔曼型方法、蒙特卡洛方法和Yau-Yau算法，并探讨了人工智能对滤波领域的影响。
摘要：
本文系统回顾了非线性滤波算法的最新进展，将其分为三个主要类别：卡尔曼型方法、蒙特卡洛方法以及Yau-Yau算法。针对每个类别，我们提供了理论发展、算法变体和实际应用的全面综述，涵盖了连续时间和离散时间系统形式。此外，本文分析了人工智能突破对整个非线性滤波领域的影响，特别是在基于学习的滤波器、神经网络增强算法和数据驱动方法等方面。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
