Abstract It is known that due to uncertain delays and missing data wireless sensor networks (WSNs) may incur a significant loss in performance. In this work, we solve the problem… Click to show full abstract
Abstract It is known that due to uncertain delays and missing data wireless sensor networks (WSNs) may incur a significant loss in performance. In this work, we solve the problem in discrete-time state-space by developing the unbiased finite impulse response (UFIR) filter, Kalman filter (KF), and game theory H ∞ filter for systems with randomly delayed data and packet dropouts. The binary Bernoulli distribution is adopted for WSN channels to model the arrival data with supposedly known delay-time probability. The effectiveness of the UFIR filter, KF, and H ∞ filter is compared experimentally in terms of accuracy and robustness employing the GPS-measured vehicle coordinates transmitted with latency over WSN.
               
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