Self-Triggered Consensus Filtering over Asynchronous Communication Sensor Networks

In this paper,a self-triggered consensus filtering is developed for a class of discrete-time distributed filtering systems.Different from existing event-triggered filtering,the self-triggered one does not require to continuously judge the trigger condition at each sampling instant and can save computational burden while achieving good state estimation.The triggering policy is presented for pre-computing the next execution time for measurements according to the filter’s own data and the latest released data of its neighbors at the current time.However,a challenging problem is that data will be asynchronously transmitted within the filtering network because each node self-triggers independently.Therefore,a co-design of the self-triggered policy and asynchronous distributed filter is developed to ensure consensus of the state estimates.Finally,a numerical example is given to illustrate the effectiveness of the consensus filtering approach.

An attack-resilient distributed extended Kalman consensus filtering algorithm with applications to multi-UAV tracking problems

This study investigates how the events of deception attacks are distributed during the fusion of multi-sensor nonlinear systems.First,a deception attack with limited energy(DALE)is introduced under the framework of distributed extended Kalman consensus filtering(DEKCF).Next,a hypothesis testing-based mechanism to detect the abnormal data generated by DALE,in the presence of the error term caused by the linearization of the nonlinear system,is established.Once the DALE is detected,a new rectification strategy can be triggered to recalibrate the abnormal data,restoring it to its normal state.Then,an attack-resilient DEKCF(AR-DEKCF)algorithm is proposed,and its fusion estimation errors are demonstrated to satisfy the mean square exponential boundedness performance,under appropriate conditions.Finally,the effectiveness of the AR-DEKCF algorithm is confirmed through simulations involving multi-unmanned aerial vehicle(multi-UAV)tracking problems.

Gradient-Based Differential kWTA Network With Application to Competitive Coordination of Multiple Robots

Aiming at the k-winners-take-all(kWTA)operation,this paper proposes a gradient-based differential kWTA(GDk WTA)network.After obtaining the network,theorems and related proofs are provided to guarantee the exponential convergence and noise resistance of the proposed GD-kWTA network.Then,numerical simulations are conducted to substantiate the preferable performance of the proposed network as compared with the traditional ones.Finally,the GD-k WTA network,backed with a consensus filter,is utilized as a robust control scheme for modeling the competition behavior in the multi-robot coordination,thereby further demonstrating its effectiveness and feasibility.

Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network

This paper presents the design of a new event-triggered Kalman consensus filter(ET-KCF)algorithm for use over a wireless sensor network(WSN).This algorithm is based on information freshness,which is calculated as the age of information(Aol)of the sampled data.The proposed algorithm integrates the traditional event-triggered mechanism,information freshness calculation method,and Kalman consensus filter(KCF)algorithm to estimate the concentrations of pollutants in the aircraft more efficiently.The proposed method also considers the influence of data packet loss and the aircraft's loss of communication path over the WSN,and presents an Aol-freshness-based threshold selection method for the ET-KCF algorithm,which compares the packet Aol to the minimum average Aol of the system.This method can obviously reduce the energy consumption because the transmission of expired information is reduced.Finally,the convergence of the algorithm is proved using the Lyapunov stability theory and matrix theory.Simulation results show that this algorithm has better fault tolerance compared to the existing KCF and lower power consumption than other ET-KCFs.

Multi-UAV cooperative target tracking with bounded noise for connectivity preservation

We investigate cooperative target tracking of multiple unmanned aerial vehicles(UAVs)with a limited communication range.This is an integration of UAV motion control,target state estimation,and network topology control.We first present the communication topology and basic notations for network connectivity,and introduce the distributed Kalman consensus filter.Then,convergence and boundedness of the estimation errors using the filter are analyzed,and potential functions are proposed for communication link maintenance and collision avoidance.By taking stable target tracking into account,a distributed potential function based UAV motion controller is discussed.Since only the estimation of the target state rather than the state itself is available for UAV motion control and UAV motion can also affect the accuracy of state estimation,it is clear that the UAV motion control and target state estimation are coupled.Finally,the stability and convergence properties of the coupled system under bounded noise are analyzed in detail and demonstrated by simulations.