DISTRIBUTED KALMAN FILTERING FUSION WITH PACKET LOSS OR INTERMITTENT COMMUNICATIONS FROM LOCAL ESTIMATORS TO FUSION CENTER

This paper considers the distributed Kalman filtering fusion with passive packet loss or initiative intermittent communications from local estimators to fusion center while the process noise does exist. When the local estimates are not lost too much, the authors propose an optimal distributed fusion algorithm which is equivalent to the corresponding centralized Kalman filtering fusion with complete communications even if the process noise does exist. When this condition is not satisfied, based on the above global optimality result and sensor data compression, the authors propose a suboptimal distributed fusion algorithm. Numerical examples show that this suboptimal algorithm still works well and significantly better than the standard distributed Kalman filtering fusion subject to packet loss even if the process noise power is quite large.

Robust trajectory planning for UAV communication systems in the presence of jammers

Unmanned Aerial Vehicle(UAV)has emerged as a promising novel application for the Sixth-Generation(6G)wireless communication by leveraging more favorable Line-of-Sight(Lo S)propagation.However,the jamming resistance by exploiting UAV’s mobility is a new challenge in the UAV-ground communication.This paper investigates the trajectory planning problem in an UAV communication system,where the UAV is operated by a Ground Control Unit(GCU)to perform certain tasks in the presence of multiple jammers with imperfect power and location information.To ensure the reliability of the GCU-to-UAV link,we formulate the problem as a non-convex semi-infinite optimization,aiming to maximize the average worst-case Signal-toInterference-plus-Noise Ratio(SINR)over a given flight duration by designing the robust trajectory of the UAV under stringent energy availability constraints.To handle this problem efficiently,we develop an iterative algorithm for the solution with the aid of S-procedure and Successive Convex Approximation(SCA)method.Numerous results demonstrate the efficacy of our proposed algorithm and offer some useful design insights to practical system.

Robust hypothesis testing for asymmetric nominal densities under a relative entropy tolerance

In this paper, we address an open problem raised by Levy(2009) regarding the design of a binary minimax test without the symmetry assumption on the nominal conditional probability densities of observations. In the binary minimax test, the nominal likelihood ratio is a monotonically increasing function and the probability densities of the observations are located in neighborhoods characterized by placing a bound on the relative entropy between the actual and nominal densities. The general minimax testing problem at hand is an infinite-dimensional optimization problem, which is quite difficult to solve. In this paper, we prove that the complicated minimax testing problem can be substantially reduced to solve a nonlinear system of two equations having only two unknown variables, which provides an efficient numerical solution.