Unbiased FIR Filtering under Bernoulli-Distributed Binary Randomly Delayed and Missing Data

This paper develops an unbiased finite impulse response (UFIR) filtering algorithm for networked systems where uncertain delays and packet dropouts can happen due to measurement failures and unreliable communication. The binary Bernoulli distribution with known delay probability is used to model the randomly arrived measures. A novel representation of the stochastic model is presented for FIR-type filter structures. To avoid packet dropouts and improve the estimation accuracy when a message arrives with no data, a predictive algorithm is used. An advantage of the UFIR filtering approach is demonstrated by comparing the mean square errors with the Kalman and H∞ filters under the same conditions. Experimental verifications are provided based on GPS vehicle tracking.