Abstract
This paper addresses the problem of the joint estimation of system state and generalized sensor bias (GSB) under a common unknown input (UI) in the case of bias evolution in a heterogeneous sensor network. First, the equivalent UI-free GSB dynamic model is derived and the local optimal estimates of system state and sensor bias are obtained in each sensor node; Second, based on the state and bias estimates obtained by each node from its neighbors, the UI is estimated via the least-squares method, and then the state estimates are fused via consensus processing; Finally, the multi-sensor bias estimates are further refined based on the consensus estimate of the UI. A numerical example of distributed multi-sensor target tracking is presented to illustrate the proposed filter.
Highlights
The well-known Kalman filter is optimal given a linear Gaussian model, but its performance will deteriorate in the presence of unknown biases, such as the unknown and time-varying delays in chemical processes [1,2,3,4], faults or failures in fault-tolerant diagnosis and control systems [5,6], registration errors in multi-sensor fusion [7,8,9,10], or inertial drift in navigation [11,12,13,14]
This paper presents the joint estimation of the system state and generalized sensor bias (GSB) under a common unknown input (UI) in the case of bias evolution in a heterogeneous sensor network
The joint estimation of system state and sensor bias based on a generalized system model is proposed
Summary
The well-known Kalman filter is optimal given a linear Gaussian model, but its performance will deteriorate in the presence of unknown biases, such as the unknown and time-varying delays in chemical processes [1,2,3,4], faults or failures in fault-tolerant diagnosis and control systems [5,6], registration errors in multi-sensor fusion [7,8,9,10], or inertial drift in navigation [11,12,13,14]. A corresponding filter has been applied for orbit determination for near-earth satellites [15,16] This type of filter design has been extended to time-varying covariance [17] and jump Markov stochastic systems [18]. An M-robust estimator [19]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
