Adaptive Filtering Under Minimum Mean p-Power Error Criterion

Computers & Technology

Chen, Badong

Ma, Wentao

Software

Chapman and Hall/CRC

Multimodal Perception and Secure State Estimation for Robotic Mobility Platforms Enables readers to understand important new trends in multimodal perception for mobile robotics This book provides a novel perspective on secure state estimation and multimodal perception for robotic mobility platforms such as autonomous vehicles. It thoroughly evaluates filter-based secure dynamic pose estimation approaches for autonomous vehicles over multiple attack signals and shows that they outperform conventional Kalman filtered results. As a modern learning resource, it contains extensive simulative and experimental results that have been successfully implemented on various models and real platforms. To aid in reader comprehension, detailed and illustrative examples on algorithm implementation and performance evaluation are also presented. Written by four qualified authors in the field, sample topics covered in the book include: <ul> <li>Secure state estimation that focuses on system robustness under cyber-attacks</li> <li>Multi-sensor fusion that helps improve system performance based on the complementary characteristics of different sensors</li> <li>A geometric pose estimation framework to incorporate measurements and constraints into a unified fusion scheme, which has been validated using public and self-collected data</li> <li>How to achieve real-time road-constrained and heading-assisted pose estimation</li> </ul> This book will appeal to graduate-level students and professionals in the fields of ground vehicle pose estimation and perception who are looking for modern and updated insight into key concepts related to the field of robotic mobility platforms.

Multimodal Perception and Secure State Estimation for Robotic Mobility Platforms

Ge, Shuzhi Sam

Jiang, Rui

Liu, Xinghua

Computer Science

Wiley-IEEE Press

Efficient Nonlinear Adaptive Filters: Design, Analysis and Applications

Zhao, Haiquan

Springer

This book provides several efficient Kalman filters (linear or nonlinear) under information theoretic criteria. They achieve excellent performance in complicated non-Gaussian noises with low computation complexity and have great practical application potential. The book combines all these perspectives and results in a single resource for students and practitioners in relevant application fields. Each chapter starts with a brief review of fundamentals, presents the material focused on the most important properties and evaluates comparatively the models discussing free parameters and their effect on the results. Proofs are provided at the end of each chapter. The book is geared to senior undergraduates with a basic understanding of linear algebra, signal processing and statistics, as well as graduate students or practitioners with experience in Kalman filtering.

Kalman Filtering Under Information Theoretic Criteria

Business & Money

Principe, José C.

Zheng, Nanning

Dang, Lujuan

Economics

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