Covariance-based fusion filtering for networked systems with random transmission delays and non-consecutive losses

The distributed and centralized fusion filtering problems for multi-sensor networked systems with transmission random one-step delays and non-consecutive packet losses are addressed. The signal evolution model is not required, as only covariance information is used. The measurements of individual sensors, subject to uncertainties modeled by random matrices and correlated noises, are transmitted to local processors through different communication channels and, due to random transmission failures, some of the data packets may be delayed or even definitely lost. The random transmission delays and non-consecutive packet losses are modeled by sequences of Bernoulli variables with different probabilities. By an innovation approach, local least squares linear filtering estimators are obtained by recursive algorithms; the distributed fusion framework is then used to obtain the optimal matrix-weighted combination of the local filters, using the mean squared error as optimality criterion. Also, a recursive least squares linear estimation algorithm is designed within the centralized fusion framework.