SUBMIT ONLINE: https://www.easychair.org/conferences/?conf=prai2026  (Please choose Special Session 13)

As a core branch of signal processing, adaptive filtering technology has broad application prospects in pattern recognition, artificial intelligence, and various engineering fields. Its core advantage lies in dynamically adjusting filter parameters to adapt to the time-varying characteristics of signals and systems without prior knowledge of signal statistical characteristics. This special session is dedicated to the in-depth discussion of the latest theoretical research, algorithm innovation, and practical engineering applications of adaptive filtering, focusing on the integration of adaptive filtering theory with pattern recognition, machine learning, and deep learning technologies. It aims to gather researchers and engineers from academia and industry to share new theories, new algorithms, and new applications, promote the development and innovation of adaptive filtering technology, and expand its application scope in various engineering fields such as target tracking (integrated navigation, UAV trajectory estimation) and battery energy management (SOC estimation). The session will cover both theoretical breakthroughs and practical engineering practices, emphasizing the cross-integration of adaptive filtering with related disciplines to solve complex signal processing problems in real-world scenarios.

Call for Papers Topics (Sub-themes)
- Novel adaptive filtering theories and mathematical models (with Kalman filtering)
- Optimization of classic adaptive filtering algorithms (LMS, RLS, etc.)
- Robust adaptive filtering for non-Gaussian noise environments
- Adaptive filtering combined with deep learning
- Hardware implementation of adaptive filtering
- Adaptive filtering in communication systems
- Adaptive filtering in biomedical signal processing
- Active noise control based on adaptive filtering
- Adaptive filtering in target tracking and battery energy management
- Distributed adaptive filtering algorithms and applications (with distributed multi-task computing)