Robust UFIR Observer for WSNs With Multistep Random Delays and Multiple Packet Dropouts

The robust unbiased finite impulse response (UFIR) filter is developed for wireless sensor networks with multistep random delays and multiple dropouts. The problem is solved by converting a model with delays to another without delays. Experimental testing is provided by Global Positioning System-based vehicle tracking with randomly delayed data, using the standard Kalman filter (KF) and game theory H-infinity filter as benchmarks. It is shown that the UFIR filter is the most robust to uncertainties and that the KF is the most accurate when the delay probabilities are precisely known. The game theory H-infinity filter is the most accurate under ideal conditions and prone to divergency otherwise.

Automated summary

The study presents the development of a robust unbiased finite impulse response (UFIR) filter for wireless sensor networks with multistep random delays and multiple dropouts. Experimental testing with randomly delayed data demonstrated that the UFIR filter is the most robust and accurate filter when uncertainties and delay probabilities are appropriately addressed.