ABSTRACT
In this chapter, we develop an approach for distributed estimation using an information matrix filter on a distributed tracking system in whichN number of sensors are tracking the same target. The approach incorporates proposed engineered versions of information matrix filter derived from covariance intersection, weighted covariance, and Kalman-like particle filter respectively. The steady performance of these filters is evaluated with different feedback strategies. Moreover they were employed with commonly used measurement fusion methods, like measurement fusion and state-vector fusion respectively, to complete the picture. The proposed filters are then validated on an industrial utility boiler, ensuring the effectiveness and applicability of the scheme underpinning it.
