Distributed Recursive Filtering for Spatially Interconnected Systems with Randomly Occurred Missing Measurements
This work addresses state estimation in sensor networks with unreliable sensors, but it appears incremental as it builds on existing models like the Andrea model for distributed systems.
The paper tackled state estimation for spatially interconnected systems with randomly missing sensor measurements by proposing a distributed filtering method, and experimental results confirmed its effectiveness in handling missing measurements.
This paper proposed a distributed filter for spatially interconnected systems (SISs), which considers missing measurements in the sensors of sub-systems. An SIS is established by many similar sub-systems that directly interact or communicate with connective neighbors. Despite that the interactions are simple and tractable, the overall SIS can perform rich and complex behaviors. In actual projects, sensors of sub-systems in a sensor network may break down sometimes, which causes parts of the measurements unavailable unexpectedly. In this work, distributed characteristics of SISs are described by Andrea model and the losses of measurements are assumed to occur with known probabilities. Experimental results confirm that, this filtering method can be effectively employed for the state estimation of SISs, when missing measurements occur.