Decentralized Information Filter with Noncommon States
Vinod Kumar Saini, Aditya Avinash Paranjape, Arnab Maity · Journal of Guidance Control and Dynamics · 2019
This paper addresses the problem of state estimation using a decentralized estimator in the presence of sensor or node-specific local state variables that arise from heterogeneous sensor dynamics, time-varying biases, or both. The state vector at each node is divided into two sets: states common to all nodes and states describing the local sensor dynamics and bias. Each node works as an independent Kalman filter to estimate the common as well as the local states associated with it, whereas communication between the notes is restricted to just the common states. The novelty of the proposed algorithm lies in the assimilation process of the decentralized estimator, wherein the local state vectors are fused without increasing the computational or the communication-related costs. Additionally, the proposed algorithm is extended by incorporating covariance intersection to address the problem of unknown correlation between the local estimators in decentralized estimation. The algorithm is validated using numerical simulations. The error characteristics of the proposed algorithm are comparable to those of a centralized Kalman filter, and the proposed algorithm is seen to offer a substantial reduction in the costs associated with estimation.