Distributed Information Fusion Research Articles

Advancements in amphibious robot navigation through wheeled odometer uncertainty extension and distributed information fusion

The advancement and safeguarding of the water-land interface region is of paramount importance, and amphibious robots with the capacity for autonomous operation can play a pivotal role in this domain. However, the inability of the majority of reliable navigation sensors to adapt to the water-land interface environment presents a significant challenge for amphibious robots, as obtaining positional information is crucial for autonomous operation. To address this issue, we have proposed a positioning and navigation framework, designated as NAWR (Navigation Algorithm for Amphibious Wheeled Robots), with the objective of enhancing the navigation capabilities of amphibious robots. Firstly, a method for representing the odometer's confidence based on a simplified wheel-terrain interaction model has been developed. This method quantitatively assesses the reliability of each odometer by estimating the slip rate. Secondly, we have introduced an improved split covariance intersection filter (I-SCIF), which maximizes the utilization of navigation information sources to enhance the accuracy of positional estimation. Finally, we will integrate these two methods to form the NAWR framework and validate the effectiveness of the proposed methods through multiple robot field trials. The results from both field trials and ablation tests collectively demonstrate that the modules and overall approach within the NAWR framework effectively enhance the navigation capabilities of amphibious robots.

Enhancing Multi-Object Tracking Through Distributed Information Fusion in Connected Vehicle Networks

Enhancing Multi-Object Tracking Through Distributed Information Fusion in Connected Vehicle Networks

A novel robust multivariate Laplace distribution-based distributed consensus information fusion

The multivariate Laplace distribution (MLD) possesses a lesser set of prior parameters compared to the Student’s t-distribution, thus simplifying the modeling of measurement data subject to heavy-tailed noise corruption. This short communication introduces a novel distributed hybrid consensus fusion algorithm utilizing the MLD modeling and variational Bayesian approach. Firstly, we formulate the measurement noise associated with individual sensor nodes and introduce the hierarchical Gaussian form of MLD to enhance the feasibility of variational inference. Secondly, we opt for the inverse-Wishart (IW) distribution to serve as the conjugate prior to the positive definite matrix, enabling adaptive parameter learning within the MLD context. As a result, a novel hierarchical Gaussian state–space model (SSM) is established for multi-sensors network. Subsequently, we derive a novel local information filter to acquire higher precision information and innovation vector–matrix pairs, outperforming the Student’s t distribution-based filter. These derivations are grounded in the new hierarchical SSM, and the vector–matrix pairs aid in information fusion across the network. Lastly, we present a distributed robust consensus filter for fusing local information using a hybrid consensus approach that devoid of a central fusion node. Simulation results demonstrate that the proposed consensus filter can obtain better estimation accuracy under the interference of heavy-tailed measurement outliers.

Information Fusion for Radar Signal Sorting with the Distributed Reconnaissance Receivers

The conventional method of centralizing information fusion is commonly employed for sorting radar signals in reconnaissance receivers. However, challenges arise when the distance between reconnaissance receivers and the fusion center is distant, or when the fusion center is compromised by hostile forces. To address these issues, this paper proposes a novel distributed information fusion method. In this method, each reconnaissance receiver is restricted to accessing adjacent nodes within an undirected graph for information transmission and local computation. The distributed Dempster’s combination rule and the cautious conjunctive rule are implemented using weight functions and consensus algorithms. Furthermore, an innovative outlier detection algorithm is incorporated into the fusion process to enhance its robustness. Experimental results demonstrate that the proposed method effectively improves the accuracy of radar signal sorting. When the sorting accuracy of a single reconnaissance receiver is equal to or higher than 60%, both fusion rules achieve a sorting accuracy of 100%. Even when the sorting accuracy of a single reconnaissance receiver is as low as 50%, the fused result still maintains a sorting accuracy of over 97%.

Distributed Maximum Correntropy Cubature Information Filtering for Tracking Unmanned Aerial Vehicle

Aiming at improving the performance of tracking unmanned aerial vehicle in the battlefield, this paper focuses on the algorithm involving tracking the maneuvering target with a distributed sensor network under non-Gaussian measurement noise. A novel distributed maximum correntropy cubature information filtering based on interactive multiple model is proposed. Taking advantage of the correntropy, we design a maximum correntropy cubature information filtering for each node to estimate the target state under non-Gaussian measurement noise. Then, distributed information fusion based on weighted average consensus is conducted to improve the stability of the sensor network. After that, the information pair is changed, so that a distributed state estimation algorithm based on interactive multiple model is developed to increase the accuracy of maneuvering target tracking. Simulation results and comparison with other algorithms in three typical non-Gaussian measurement noise scenarios are given to evaluate the effectiveness of the proposed algorithm.

Distributed Consensus Kalman Filter Design with Dual Energy-Saving Strategy: Event-Triggered Schedule and Topological Transformation.

In the distributed information fusion of wireless sensor networks (WSNs), the filtering accuracy is commonly negatively correlated with energy consumption. Therefore, a class of distributed consensus Kalman filters was designed to balance the contradiction between them in this paper. Firstly, an event-triggered schedule was designed based on historical data within a timeliness window. Furthermore, considering the relationship between energy consumption and communication distance, a topological transformation schedule with energy-saving is proposed. The energy-saving distributed consensus Kalman filter with a dual event-driven (or event-triggered) strategy is proposed by combining the above two schedules. The sufficient condition of stability for the filter is given by the second Lyapunov stability theory. Finally, the effectiveness of the proposed filter was verified by a simulation.

Construction of Teaching and Education Quality Improvement Model Based on Distributed Information Fusion

Following the growth of educational informatization, the integrated adoption of the Internet in classroom teaching has facilitated the change of instructional pattern from the conventional instructional pattern to a novel information-based instructional pattern. Based on the background of “Internet+,” a variety of new information-based classroom teaching models have emerged. However, in practice, there are still many deficiencies due to the immature application of the model. This paper proposes to use the design-based research paradigm to construct and optimize the teaching mode, and to clarify the design principles of the teaching mode. This paper analyses and designs the English lesson instruction pattern in the distributed information convergence condition from five dimensions: theoretical foundation, online instructional setting, instructional targets, instructional events, and instructional assessment, which construct an instructional pattern. It adopts the method of combining quantitative research with qualitative research and combines the experimental method, questionnaire survey method, classroom observation method, and interview method to assist the research. Through the investigation of 122 valid data, it is shown that the overall amount of classroom codes before the experiment is 57, and the overall amount of classroom codes after the experiment is 150. There is a big difference between the two. It validates the effectiveness of the English lesson instruction pattern in distributed information integration terms in improving students’ English achievement, cooperative learning ability, information literacy, and problem-solving ability.

Fully Distributed Filtering With a Stochastic Event-Triggered Mechanism

This article focuses on distributed filtering for a discrete time-varying system observed by multiple smart sensors, where every sensor only measures partial state information of the target system and then sends it to a corresponding remote estimator. Subsequently, the estimator performs the local Kalman filter and shares its estimates with the estimators in its neighborhood in a distributed way. This article aims to reduce the communication rate between sensors and estimators, and guarantee the estimation performance, simultaneously. To achieve this goal, a novel distributed information fusion algorithm is designed by embedding a stochastic event-triggered communication mechanism. Based on a new developed mathematics technique, the consistency and stability of the proposed distributed state estimation algorithms are both ensured. Furthermore, compared with the literature, the stability can be guaranteed with a milder collectively uniformly observable condition. Moreover, the tradeoff between the communication rate and estimation performance is analyzed in a closed-form expression. Finally, the effectiveness of the theoretical results is demonstrated by several comparative numerical examples.

Research on Multi-point Hopping Communication System in Power Network Based on Information Fusion Model

Multi-source heterogeneous information will cause access burden to the power network, resulting in poor performance of multi-point hop communication. Therefore, a multi-point hop communication system based on information fusion model is designed. The IEC61970/61968CIM is selected as the integrated bus general data acquisition scheme of the information fusion model. The local-global distributed information fusion mechanism is used to realize the communication of the power multi-point hop communication system. In this paper, the artificial neural network algorithm is used to fuse local information features, and according to D-S evidence theory, the global decision-level fusion is carried out from both the spatial domain and the time domain. Through the information fusion model, the multi-point hop communication information in the power network realizes efficient transmission. The experimental results show that the application of the system in the power network to implement multi-point hop communication, the packet loss rate is less than 0.25%, the transmission delay is less than 30ms, and the communication performance of the power network is improved

Distributed state estimation for heterogeneous mobile sensor networks with stochastic observation loss

The problem of distributed fusion and random observation loss for mobile sensor networks is investigated herein. In view of the fact that the measured values, sampling frequency and noise of various sensors are different, the observation model of a heterogeneous network is constructed. A binary random variable is introduced to describe the drop of observation component and the topology switching problem caused by complete observation loss is also considered. A cubature information filtering algorithm is adopted to design local filters for each observer to suppress the negative effects of measurement noise. To derive a consistent and accurate estimation result, a novel weighted average consensus-based filtering approach is put forward. For the sensor that suffers from observation loss, its local prediction information vector is fused with the information contribution vectors of the neighbors to obtain the local estimation. Then the consensus weight matrix is designed for consensus-based distributed collaborative information fusion. The boundness of the estimation errors is proved by employing the stochastic stability theory. In the end, two numerical examples are offered to assert the validity of the presented method.

Research on Computer Programming Optimization System Based on Big Data Technology

To improve network programming efficiency, the paper proposes a network automatic programming method based on big data analysis technology. Build a big data analysis model for automatic network programming. The dissertation uses the distributed information fusion method to extract the significant data feature of automatic network programming, extract the associated feature quantity of automatic network programming big data, and combine the fuzzy membership analysis method to carry out the synthesis operation and fuzzy comprehensive evaluation decision of automatic network programming and build network automation Programming multi-level index evaluation system to realize automatic network programming optimization.

Distributed information fusion in tangle networks

The potential of a decentralized/distributed system to behave intelligently as a whole hinges on the capacity of its constituents to exchange and process information. In sensor networks and multiagent platforms this may be realized by means of distributed information fusion techniques. The very nature of these approaches demands specifying, or at least approximating, the statistical interdependencies between individual entities in the network. This, however, becomes impractical with the increase in network size. In the past years a number of scalable techniques have been devised, which relax this constraint while yet maintaining a number of desired statistical properties like consistency and convergence to consensus. Here, we present a methodological approach for designing and analyzing information fusion in potentially large-scale networks. Tangle networks, the objects of study in our formalism, are flexible diagrammatic models that capture key properties of scalable information fusion. A tangle network comes equipped with a natural notion of equivalence: two networks are equivalent if they can be transformed one into the other by successive application of local deformations. Any such deformation preserves the information content and consistency of estimators in the network. We derive novel particle-filtering-based algorithms for distributed information fusion over tangle networks and analyze their performance in various settings. The algebraic properties of tangle networks are shown to bear resemblance to algebraic properties of graphs. In particular, we show that the agreement between estimators in the network is governed by the spectral gap of the network’s associated matrix, the analog of a graph Laplacian. The utility of the framework is demonstrated through comparison with state-of-the-art distributed information fusion techniques.

Distributed optimal linear fusion estimators

This paper is concerned with the distributed information fusion estimation problem in multi-sensor environments. A universal distributed optimal linear fusion estimation (DOLFE) algorithm, which has a Kalman-type structure with matrix gains, is presented under the linear unbiased minimum variance criterion. To reduce the computational burden, two suboptimal linear fusion estimation algorithms with diagonal-matrix gains and scalar gains are also presented. Based on the proposed DOLFE algorithm, a distributed optimal linear fusion filter (DOLFF) is presented for multi-sensor linear discrete-time stochastic systems. It has better accuracy than that based on the matrix-weighted fusion of local estimators but worse accuracy than the centralized fusion filter (CFF). The stability and steady-state property of the proposed DOLFF are analyzed. Then, the corresponding multi-step predictor and smoother are also developed based on DOLFF. To obtain the distributed fusion estimators, some estimation error cross-covariance matrices that are used to compute the gains are derived. At last, distributed optimal linear fusion estimators with feedback are also presented. Furthermore, it is strictly proved that the proposed distributed optimal linear fusion filter with feedback (DOLFFWF) has the same accuracy as the CFF. Two simulation examples show the effectiveness of the proposed algorithms.

Efficient Multi-View Multi-Target Tracking Using a Distributed Camera Network

In this paper, we propose a multi-target tracking method using a distributed camera network, which can effectively handle the occlusion and reidenfication problems by combining advanced deep learning and distributed information fusion. The targets are first detected using a fast object detection method based on deep learning. We then combine the deep visual feature information and spatial trajectory information in the Hungarian algorithm for robust targets association. The deep visual feature information is extracted from a convolutional neural network, which is pre-trained using a large-scale person reidentification dataset. The spatial trajectories of multiple targets in our framework are derived from a multiple view information fusion method, which employs an information weighted consensus filter for fusion and tracking. In addition, we also propose an efficient track processing method for ID assignment using multiple view information. The experiments on public datasets show that the proposed method is robust to solve the occlusion problem and reidentification problem, and can achieve superior performance compared to the state of the art methods.

Secure Information Fusion using Local Posterior for Distributed Cyber-Physical Systems

In modern distributed cyber-physical systems (CPS), information fusion often plays a key role in automate and self-adaptive decision making process. However, given the heterogeneous and distributed nature of modern CPSs, it is a great challenge to operate CPSs with the compromised data integrity and unreliable communication links. In this paper, we study the distributed state estimation problem under the false data injection attack (FDIA) with probabilistic communication networks. We propose an integrated ”detection + fusion” solution, which is based on the Kullback-Leibler divergences (KLD) between local posteriors and therefore does not require the exchange of raw sensor data. For the FDIA detection step, the KLDs are used to cluster nodes in the probability space and to partition the space into secure and insecure subspaces. By approximating the distribution of the KLDs with a general <inline-formula><tex-math notation="LaTeX">$\chi ^2$</tex-math></inline-formula> distribution and calculating its tail probability, we provide an analysis of the detection error rate. For the information fusion step, we discuss the potential risk of double counting the shared prior information in the KLD-based consensus formulation method. We show that if the local posteriors are updated from the shared prior, the increased number of neighbouring nodes will lead to the diminished information gain. To overcome this problem, we propose a near-optimal distributed information fusion solution with properly weighted prior and data likelihood. Finally, we present simulation results for the integrated solution. We discuss the impact of network connectivity on the empirical detection error rate and the accuracy of state estimation.

Interacting Multiple Model-Based Human Pose Estimation Using a Distributed 3D Camera Network

Distributed camera network for human pose estimation can solve the problem of limited view and occlusion of single view, which has great potential for wide area surveillance applications. To fuse different field of view information, we propose a distributed human pose estimation method by combining the interactive multiple model (IMM) algorithm with the distributed information fusion of human skeleton joints. Compared with the state-of-the-art works which often use a single motion model to depict the motion of human skeleton joints, e.g., constant velocity, the novelty of our work is that the maneuvering property of human action is handled by the IMM, i.e., the motion model of human skeleton joints in the filter is approximated using constant velocity, constant acceleration, and Singer motion models. Based on the advantages of IMM algorithm for maneuvering target tracking, our method can not only solve the single-view occlusion problem, but also solve the problem of joint point fluctuation caused by the estimation error of each sensor node after distributed information fusion. The final human action recognition experimental results show that the proposed method can improve the action recognition rate on the datasets captured by Kinect V2. In addition, we built a distributed camera network using embedded machine learning boards, such that deep learning-based human pose estimation methods can be employed in our framework to handle the limitations of original Kinect SDK.

Privacy-Preserving Average Consensus via State Decomposition

Average consensus underpins key functionalities of distributed systems ranging from distributed information fusion, decision-making, distributed optimization, to load balancing and decentralized control. Existing distributed average consensus algorithms require each node to exchange and disclose state information to its neighbors, which is undesirable in cases where the state is private or contains sensitive information. In this paper, we propose a novel approach that avoids disclosing individual state information in average consensus by letting each node decompose its state into 2 substates. For each node, one of the two substates involves in computation and internode interactions as if it were the original state, while the other substate interacts only with the first substate of the same node, being completely invisible to other nodes. The initial values of the two substates are chosen randomly but with their mean fixed to the initial value of the original state, which is key to guarantee convergence to the desired consensus value. In direct contrast to differential-privacy based privacy-preserving average-consensus approaches, which enable privacy by compromising accuracy in the consensus value, the proposed approach can guarantee convergence to the exact desired value without any error. Not only is the proposed approach able to prevent the disclosure of a node's initial state to honest-but-curious neighbors, it can also provide protection against inference by external eavesdroppers able to wiretap communication links. Numerical simulations demonstrate the effectiveness of the approach and its advantages over state-of-the-art counterparts.

Secure and Privacy-Preserving Consensus

Consensus is fundamental for distributed systems since it underpins key functionalities of such systems ranging from distributed information fusion, decision making, to decentralized control. In order to reach an agreement, existing consensus algorithms require each agent to exchange explicit state information with its neighbors. This leads to the disclosure of private state information, which is undesirable in cases where privacy is of concern. In this paper, we propose a novel approach for undirected networks, which can enable secure and privacy-preserving average consensus in a decentralized architecture in the absence of an aggregator or third party. By leveraging partial homomorphic cryptography to embed secrecy in pairwise interaction dynamics, our approach can guarantee convergence to the consensus value (subject to a quantization error) in a deterministic manner without disclosing a node's state to its neighbors. We provide a new privacy definition for dynamical systems, and give a new framework to rigorously prove that a node's privacy can be protected as long as it has at least one legitimate neighbor, which follows the consensus protocol faithfully without attempts to infer other nodes’ states. In addition to enabling resilience to passive attackers aiming to steal state information, the approach also allows easy incorporation of defending mechanisms against active attackers who try to alter the content of exchanged messages. Furthermore, in contrast to existing noise-injection-based privacy-preserving mechanisms that have to reconfigure the entire network when the topology or number of nodes varies, our approach is applicable to dynamic environments with time-varying coupling topologies. This secure and privacy-preserving approach is also applicable to weighted average consensus as well as maximum/minimum consensus under a new update rule. Numerical simulations and comparison with existing approaches confirm the theoretical results. Experimental results on a Raspberry-Pi board based microcontroller network are also presented to verify the effectiveness and efficiency of the approach.

Maneuvering target recognition method based on multi-perspective light field reconstruction

It is difficult to reconstruct the complete light field, and the reconstructed light field can only recognize specific fixed targets. These have limited the applications of the light field in practice. To solve the problems above, this article introduces the multi-perspective distributed information fusion into light field reconstruction to monitor and recognize the maneuvering targets. First, the light field is represented as sub-light fields at different perspectives (i.e. the Multi-sensor distributed network), and sparse representation and reconstruction are then performed. Second, we establish the multi-perspective distributed information fusion under the condition of regional full-coverage constraints. Finally, the light field data from multiple perspectives are fused and the states of the maneuvering targets are estimated. Experimental results show that the light field reconstruction time of the proposed method is less than 583 s, and the reconstruction accuracy exceeds 92.447% compared with the existing spatially variable bidirectional reflectance distribution function, micro-lens array, and others. In the aspect of maneuvering target recognition, the recognition time of the algorithm in this article is no more than 3.5 s. The recognition accuracy of the algorithm in this article is up to 86.739%. Moreover, the more viewing angles used, the higher the accuracy.

Multi-feature analysis fatigue driving based on Conditional Local Neural Fields algorithm detection method

In order to effectively strengthen the monitoring technology of fatigue driving and reduce the incidence of traffic accidents, this paper proposes a new method based on conditional local neural fields algorithm for comprehensive evaluation of fatigue driving state. At present, most of the fatigue driving detection algorithms are based on extracting a single characteristic index, which is strict in environmental requirements and not high in detection. In this paper, the HOG feature is combined with the CLNF algorithm to implement face detection and feature point localization. Then, the EPnP algorithm is used to estimate the head anomaly frequency based on the feature point information, and the blink frequency is calculated according to the EAR eye length aspect ratio concept according to the feature points around the eye. Finally the threshold set by the P80 fatigue detection standard in the PERCLOS method is integrated, and the distributed information fusion strategy is used for fatigue evaluation. Experimental results confirm the effectiveness of the method.