<P><STRONG>1. Introduction</STRONG></P><P>1.1 2-D Systems</P><P>1.2 Communication Constraints</P><P>1.3 Recent Progress on Filtering for 2-D Systems</P><P>1.4 Outline</P><B><P>2. Minimum-Variance Recursive Filtering for Two-Dimensional Systems with Degraded Measurements: Boundedness and Monotonicity </P></B><P>2.1 Problem Formulation</P><P>2.2 The Minimum-Variance Filter Design</P><P>2.3 Performance Analysis</P><P>2.4 Numerical Example</P><P>2.5 Summary</P><B><P>3. Robust Kalman Filtering for Two-Dimensional Systems with Multiplicative Noises and Measurement Degradations</P></B><P>3.1 Problem Formulation and Preliminaries</P><P>3.2 Upper Bound for The Generalized Error Variance</P><P>3.3 Suboptimal Filter Design</P><P>3.4 Numerical Example</P><P>3.5 Summary</P><B><P>4. Robust Finite-Horizon Filtering for Two-Dimensional Systems with Randomly Varying Sensor Delays</P></B><P>4.1 Problem Formulation</P><P>4.2 Preliminaries</P><P>4.3 Finite-Horizon Robust Kalman Filter Design</P><P>4.4 Numerical Example</P><P>4.5 Summary</P><B><P>5. Recursive Filtering for Two-Dimensional Systems with Missing Measurements subject to Uncertain Probabilities</P></B><P>5.1 Problem Formulation</P><P>5.2 Recursive Filter Design</P><P>5.3 Numerical Example</P><P>5.4 Summary</P><B><P>6. Resilient State Estimation for Two-Dimensional Shift-Varying Systems with Redundant Channels</P></B><P>6.1 Problem Formulation and Preliminaries</P><P>6.2 Resilient Filter Design</P><P>6.3 Numerical Examples</P><P>6.4 Summary</P><B><P>7. Recursive Distributed Filtering for Two-Dimensional Shift-Varying Systems Over Sensor Networks Under Random Access Protocols</P></B><P>7.1 Problem Formulation and Preliminaries</P><P>7.2 Main Results</P><P>7.3 Numerical Example</P><P>7.4 Summary</P><B><P>8. Resilient Filtering for Linear Shift-Varying Repetitive Processes under Uniform Quantizations and Round-Robin Protocols</P></B><P>8.1 Problem Formulation </P><P>8.2 Main Results </P><P>8.3 Numerical Example</P><P>8.4 Summary</P><B><P>9. Event-Triggered Recursive Filtering for Shift-Varying Linear Repetitive Processes</P></B><P>9.1 Problem Formulation</P><P>9.2 Main Results</P><P>9.3 Numerical Example</P><P>9.4 Summary</P><B><P>10. Conclusions and Future Topics </P></B>

This book presents up-to-date research developments and novel methodologies regarding recursive filtering for 2-D shift-varying systems with various communication constraints. It investigates recursive filter/estimator design and performance analysis by a combination of intensive stochastic analysis, recursive Riccati-like equations, variance-constrained approach, and mathematical induction. Each chapter considers dynamics of the system, subtle design of filter gains, and effects of the communication constraints on filtering performance. Effectiveness of the derived theories and applicability of the developed filtering strategies are illustrated via simulation examples and practical insight. Features:- Covers recent advances of recursive filtering for 2-D shift-varying systems subjected to communication constraints from the engineering perspective. Includes the recursive filter design, resilience operation and performance analysis for the considered 2-D shift-varying systems. Captures the essence of the design for 2-D recursive filters. Develops a series of latest results about the robust Kalman filtering and protocol-based filtering. Analyzes recursive filter design and filtering performance for the considered systems. This book aims at graduate students and researchers in mechanical engineering, industrial engineering, communications networks, applied mathematics, robotics and control systems.

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