Topics in matrix analysis
Robust ℋ∞ filtering for uncertaindiscrete-time state-delayed systems
IEEE Transactions on Signal Processing
Brief paper: Optimal estimation of linear discrete-time systems with stochastic parameters
Automatica (Journal of IFAC)
Optimal recursive estimation with uncertain observation
IEEE Transactions on Information Theory
Brief paper: Robust filtering with stochastic nonlinearities and multiple missing measurements
Automatica (Journal of IFAC)
Signal estimation with multiple delayed sensors using covariance information
Digital Signal Processing
IEEE Transactions on Signal Processing
Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
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In this paper, linear minimum variance unbiased state estimation is considered for signals with sensor delay. The solutions that have been proposed for the sensor delay problem so far only involve sensors with identical delay characteristics. However, in a true sensor network system, there may be multiple sensors which may not have the same delay characteristics. Therefore, the main goal of this research is to extend and generalize the existing solutions by modeling multiple sensors having different delay characteristics. The probability of occurrence of the delay is assumed to be known from the queuing characteristics. Illustrative examples are provided to support the theory developed in this work. Simulation comparison of the solution developed in this framework to the traditional Kalman Filter shows superiority and efficiency of our technique in the case of sensor delay. Furthermore, the robustness of the proposed method is also shown by simulations.