Robustness Analysis of Distributed Kalman Filter for Estimation in Sensor Networks.

Area of Science:

• Control Systems Engineering
• Networked Systems
• Signal Processing

Background:

• Linear Quadratic Regulators (LQRs) and Kalman Filters (KF) offer guaranteed stability margins in control systems.
• Distributed estimation in sensor networks presents unique challenges for filter robustness.

Purpose of the Study:

• To extend frequency-domain robustness margin analysis to the Distributed Kalman-Consensus Filter (DKCF).
• To investigate the robustness of DKCF under different observation strategies and varying network communication topologies.

Main Methods:

• Derivation of loop transfer functions for two DKCF configurations: direct target observation and neighbor-based estimation.
• Frequency-domain analysis to determine gain and phase margin robustness for DKCF.
• Correlation analysis between sensor network coupling strengths and DKCF robustness margins.

Main Results:

• DKCF exhibits improved robustness margins compared to single-agent KF.
• Robustness margins are quantified for both direct and neighbor-based observation cases.
• A correlation between overall network coupling strength and DKCF robustness is identified.

Conclusions:

• The DKCF offers enhanced robustness for distributed estimation in sensor networks.
• Understanding the impact of network topology on robustness is crucial for DKCF design.
• Frequency-domain analysis provides valuable insights into the stability and performance of distributed filters.