On the genericity properties in networked estimation: Topology design and sensor placement

Abstract

In this paper, we consider networked estimation of linear, discrete-time dynamical systems monitored by a network of agents. In order to minimize the power requirement at the (possibly, battery-operated) agents, we require that the agents can exchange information with their neighbors only \emph{once per dynamical system time-step}; in contrast to consensus-based estimation where the agents exchange information until they reach a consensus. It can be verified that with this restriction on information exchange, measurement fusion alone results in an unbounded estimation error at every such agent that does not have an observable set of measurements in its neighborhood. To over come this challenge, state-estimate fusion has been proposed to recover the system observability. However, we show that adding state-estimate fusion may not recover observability when the system matrix is structured-rank ($S$-rank) deficient. In this context, we characterize the state-estimate fusion and measurement fusion under both full $S$-rank and $S$-rank deficient system matrices.

submitted for IEEE journal publication

References in corpus (1)

• Diffusion Strategies Outperform Consensus Strategies for Distributed Estimation over Adaptive Networks

Cited by in corpus (13)

• Consensus+Innovations Distributed Kalman Filter with Optimized Gains
• Cyber-Social Systems: Modeling, Inference, and Optimal Design
• Graphic-theoretic distributed inference in social networks
• Distributed Estimation Recovery under Sensor Failure
• Structural cost-optimal design of sensor networks for distributed estimation
• PALoc: Advancing SLAM Benchmarking with Prior-Assisted 6-DoF Trajectory Generation and Uncertainty Estimation
• Structural Systems Theory: an overview of the last 15 years
• Decentralized Mobile Target Tracking Using Consensus-Based Estimation with Nearly-Constant-Velocity Modeling
• Efficient constrained sensor placement for observability of linear systems
• Distributed Kalman Filtering over Massive Data Sets: Analysis Through Large Deviations of Random Riccati Equations
• On the Controllability of Clustered Scale-Free Networks
• Detection and Mitigation of Biasing Attacks on Distributed Estimation Networks
• Recovering the Structural Observability of Composite Networks via Cartesian Product