Outlier-resistant distributed fusion filtering for nonlinear discrete-time singular systems under a dynamic event-triggered scheme

This paper investigates the problem of outlier-resistant distributed fusion filtering(DFF)for a class of multi-sensor nonlinear singular systems(MSNSSs)under a dynamic event-triggered scheme(DETS).To relieve the effect of measurement outliers in data transmission,a self-adaptive saturation function is used.Moreover,to further reduce the energy consumption of each sensor node and improve the efficiency of resource utilization,a DETS is adopted to regulate the frequency of data transmission.For the addressed MSNSSs,our purpose is to construct the local outlier-resistant filter under the effects of the measurement outliers and the DETS;the local upper bound(UB)on the filtering error covariance(FEC)is derived by solving the difference equations and minimized by designing proper filter gains.Furthermore,according to the local filters and their UBs,a DFF algorithm is presented in terms of the inverse covariance intersection fusion rule.As such,the proposed DFF algorithm has the advantages of reducing the frequency of data transmission and the impact of measurement outliers,thereby improving the estimation performance.Moreover,the uniform boundedness of the filtering error is discussed and a corresponding sufficient condition is presented.Finally,the validity of the developed algorithm is checked using a simulation example.

Distributed Privacy-Preserving Fusion Estimation Using Homomorphic Encryption

The privacy-preserving problem for distributed fusion estimation scheme is concerned in this paper.When legitimate user wants to obtain consistent information from multiple sensors,it always employs a fusion center(FC)to gather local data and compute distributed fusion estimates(DFEs).Due to the existence of potential eavesdropper,the data exchanged among sensors,FC and user imperatively require privacy preservation.Hence,we propose a distributed confidentiality fusion structure against eavesdropper by using Paillier homomorphic encryption approach.In this case,FC cannot acquire real values of local state estimates,while it only helps calculate encrypted DFEs.Then,the legitimate user can successfully obtain the true values of DFEs according to the encrypted information and secret keys,which is based on the homomorphism of encryption.Finally,an illustrative example is provided to verify the effectiveness of the proposed methods.

Distributed Radar Target Tracking with Low Communication Cost

In distributed radar,most of existing radar networks operate in the tracking fusion mode which combines radar target tracks for a higher positioning accuracy.However,as the filtering covariance matrix indicating positioning accuracy often occupies many bits,the communication cost from local sensors to the fusion is not always sufficiently low for some wireless communication chan-nels.This paper studies how to compress data for distributed tracking fusion algorithms.Based on the K-singular value decomposition(K-SVD)algorithm,a sparse coding algorithm is presented to sparsely represent the filtering covariance matrix.Then the least square quantization(LSQ)algo-rithm is used to quantize the data according to the statistical characteristics of the sparse coeffi-cients.Quantized results are then coded with an arithmetic coding method which can further com-press data.Numerical results indicate that this tracking data compression algorithm drops the com-munication bandwidth to 4%at the cost of a 16%root mean squared error(RMSE)loss.

On Asynchrony in Multisensor Distributed Detection

In this paper, the asynchrony problem of distributed detection is analyzed and discussed.Two approaches are proposed and related results are given. It is shown that all fusion rules can beunified in the framework with asynchrony which could be much ciooer to industrial practice.

Distributed Resilient Fusion Filtering for Nonlinear Systems with Random Sensor Delays:Optimized Algorithm Design and Boundedness Analysis

This paper is concerned with the distributed resilient fusion filtering(DRFF)problem for a class of time-varying multi-sensor nonlinear stochastic systems(MNSSs)with random sensor delays(RSDs).The phenomenon of the RSDs is modeled by a set of random variables with certain statistical features.In addition,the nonlinear function is handled via Taylor expansion in order to deal with the nonlinear fusion filtering problem.The aim of the addressed issue is to propose a DRFF scheme for MNSSs such that,for both RSDs and estimator gain perturbations,certain upper bounds of estimation error covariance(EEC)are given and locally minimized at every sample time.In the light of the obtained local filters,a new DRFF algorithm is developed via the matrix-weighted fusion method.Furthermore,a sufficient condition is presented,which can guarantee that the local upper bound of the EEC is bounded.Finally,a numerical example is provided,which can show the usefulness of the developed DRFF approach.

Optimal fusion state estimator for a multi-sensor system subject to multiple packet dropouts

In this note,we study the state estimation problem for a multi-sensor system subject to multiple packet dropouts.A novel optimal distributed fusion estimator is derived by applying a resending mechanism and a parallel information filtering structure.It is shown that the proposed distributed fusion estimator has smaller estimation error covariance and less computation complexity when compared with the centralised Kalman like estimator with multiple intermittent measurements.

Mission-driven autonomous perception and fusion based on UAV swarm

Distributed autonomous situational awareness is one of the most important foundation for Unmanned Aerial Vehicle(UAV)swarm to implement various missions.Considering the application environment being usually characterized by strong confrontation,high dynamics,and deep uncertainty,the distributed situational awareness system based on UAV swarm needs to be driven by the mission requirements,while each node in the network can autonomously avoid collisions and perform detection mission through limited resource sharing as well as complementarity of respective advantages.By efficiently solving the problems of self-avoidance,autonomous flocking and splitting,joint estimation and control,etc.,perception data from multi-platform multi-source should be extracted and fused reasonably,to generate refined,tailored target information and provide reliable support for decision-making.

Cooperative control and communication of intelligent swarms:a survey

Individuals exchange information,experience and strategy based on communication.Communication is the basis for individuals to form swarms and the bridge of swarms to realize cooperative control.In this paper,the multirobot swarm and its cooperative control and communication methods are reviewed,and we summarize these methods from the task,control,and perception levels.Based on the research,the cooperative control and communication methods of intelligent swarms are divided into the following four categories:task assignment based methods(divided into market-based methods and alliance based methods),bio-inspired methods(divided into biochemical information inspired methods,vision based methods and self-organization based methods),distributed sensor fusion and reinforcement learning based methods,and we briefly define each method and introduce its basic ideas.Based on WOS database,we divide the development of each method into several stages according to the time distribution of the literature,and outline the main research content of each stage.Finally,we discuss the communication problems of intelligent swarms and the key issues,challenges and future work of each method.