This paper generalizes the well-known covariance intersection algorithm for distributed estimation and information fusion of random vectors. Our focus will be on partially correlated random vectors. This is motivated by the restriction of the standard covariance intersection algorithm, which treats all random vectors with arbitrary cross correlations and the restriction of the classical Kalman filter, which requires complete knowledge of the cross correlations. We first give a result to characterize the conservatism of the standard covariance intersection algorithm. We then generalize the covariance intersection algorithm to two random vectors with a given correlation coefficient bound and show in what sense the resulting covariance bound is tight. Finally, we generalize the notion of correlation coefficient bound to multiple random vectors and provide a covariance intersection algorithm for this general case. Our results will make the already popular covariance intersection more applicable and more accurate for distributed estimation and information fusion problems.

This paper proposes fast convergent distributed algorithms for weighted average consensus of input data. For acyclic graphs, we give an algorithm that converges to the exact weighted average consensus in a finite number of iterations, equal to the graph diameter. For loopy (cyclic) graphs, we offer two remedies. In the first one, we give another distributed algorithm to enable our average consensus algorithm applicable to a loopy graph by converting it into a spanning tree. In the second one, we consider a slightly modified average consensus problem whose optimal solution approximates the consensus solution with arbitrary precision, and give a modified average consensus algorithm with guaranteed exponential convergence to the optimal solution. The proposed average consensus algorithms enjoy low complexities, robustness to transmission adversaries, and asynchronous implementation. Our algorithms are conceptually different from the popular graph Laplacian approach, and converge much faster than the latter approach.

This article studies the stability issue of networked switched systems (NSSs) under denial-of-service (DoS) attacks. To address this issue, the derived limitations imposed on both the frequency of DoS attacks on each subsystem and the upper limit of attack duration that each subsystem can tolerate are mode-dependent, which is more efficient and flexible than the current results for NSSs. Moreover, we reveal the relationship between the upper bound of the average maximum tolerable attack duration associated with the corresponding subsystem and the actual mode-dependent average dwell time. Furthermore, we identify that the total tolerable DoS attack duration as a percentage of the system runtime in this article can be higher than existing results. Finally, an example is given to demonstrate the effectiveness of our work.