Distributed Estimation Recovery under Sensor Failure
For engineers designing sensor networks, this work provides a recovery method for distributed estimation after sensor failure, though it is incremental as it extends existing single time-scale estimation concepts.
This paper addresses distributed estimation recovery under sensor failure in single time-scale networks, proposing polynomial-order algorithms to find equivalent state nodes for restoring observability, with separate handling for α and β sensors.
Single time-scale distributed estimation of dynamic systems via a network of sensors/estimators is addressed in this letter. In single time-scale distributed estimation, the two fusion steps, consensus and measurement exchange, are implemented only once, in contrast to, e.g., a large number of consensus iterations at every step of the system dynamics. We particularly discuss the problem of failure in the sensor/estimator network and how to recover for distributed estimation by adding new sensor measurements from equivalent states. We separately discuss the recovery for two types of sensors, namely αand βsensors. We propose polynomial order algorithms to find equivalent state nodes in graph representation of system to recover for distributed observability. The polynomial order solution is particularly significant for large-scale systems.