Distributed Information Fusion Research Articles

Distributed Fusion of Sensor Data in a Constrained Wireless Network.

Smart buildings with connected lighting and sensors are likely to become one of the first large-scale applications of the Internet of Things (IoT). However, as the number of interconnected IoT devices is expected to rise exponentially, the amount of collected data will be enormous but highly redundant. Devices will be required to pre-process data locally or at least in their vicinity. Thus, local data fusion, subject to constraint communications will become necessary. In that sense, distributed architectures will become increasingly unavoidable. Anticipating this trend, this paper addresses the problem of presence detection in a building as a distributed sensing of a hidden Markov model (DS-HMM) with limitations on the communication. The key idea in our work is the use of a posteriori probabilities or likelihood ratios (LR) as an appropriate “interface” between heterogeneous sensors with different error profiles. We propose an efficient transmission policy, jointly with a fusion algorithm, to merge data from various HMMs running separately on all sensor nodes but with all the models observing the same Markovian process. To test the feasibility of our DS-HMM concept, a simple proof-of-concept prototype was used in a typical office environment. The experimental results show full functionality and validate the benefits. Our proposed scheme achieved high accuracy while reducing the communication requirements. The concept of DS-HMM and a posteriori probabilities as an interface is suitable for many other applications for distributed information fusion in wireless sensor networks.

Systemic design of distributed multi-UAV cooperative decision-making for multi-target tracking

In this paper, we consider the cooperative decision-making problem for multi-target tracking in multi-unmanned aerial vehicle (UAV) systems. The multi-UAV decision-making problem is modeled in the framework of distributed multi-agent partially observable Markov decision processes (MPOMDPs). Specifically, the state of the targets is represented by the joint multi-target probability distribution (JMTPD), which is estimated by a distributed information fusion strategy. In the information fusion process, the most accurate estimation is selected to propagate through the whole network in finite time. We propose a max-consensus protocol to guarantee the consistency of the JMTPD. It is proven that the max-consensus can be achieved in the connected communication graph after a limited number of iterations. Based on the consistent JMTPD, the distributed partially observable Markov decision algorithm is used to make tracking decisions. The proposed method uses the Fisher information to bid for targets in a distributed auction. The bid is based upon the reward value of the individual UAV’s POMDPs, thereby removing the need to optimize the global reward in the MPOMDPs. Finally, the cooperative decision-making approach is deployed in a simulation of a multi-target tracking problem. We compare our proposed algorithm with the centralized method and the greedy approach. The simulation results show that the proposed distributed method has a similar performance to the centralized method, and outperforms the greedy approach.

An Advanced Cubature Information Filtering for Indoor Multiple Wideband Source Tracking With a Distributed Noise Statistics Estimator

Acoustic vector sensor (AVS) is an effective tool to tracking acoustic sources. However, for the problem of tracking multiple wideband sources using distributed AVS array (DAVS), there are still unsolved issues which include measurements-to-targets association and targets tracking under incorrect or unknown statistics of measurement noise. Joint probabilistic data association (JPDA) is an effective algorithm to solve data association between measurements and targets and JPDA based cubature information filter (MTCIF) is designed for nonlinear system. Meanwhile, noise statistics estimator (NSE) based on modified Sage-Husa maximum posterior (SHMP) is constructed to cope with incorrect or unknown statistics of measurement noise. Then, a two-step distributed information fusion based on weighted average consensus (WAC) is built for DAVS to improve the stability and accuracy of state estimator and NSE. Numerical simulations demonstrate the effectiveness of the proposed algorithms.

Towards a Dempster–Shafer Fuzzy Description Logic—Handling Imprecision in the Semantic Web

Vague information has been emerged as a main issue in Semantic Web community. Vagueness is traditionally represented by fuzzy set theory. Besides vagueness, Semantic Web queries often have to deal with information incompleteness, aka uncertainty. This kind of information can be represented through Dempster–Shafer theory, that also enables distributed information fusion. Vagueness along with information incompleteness are often referred to under the common term imprecise information . Imprecise information should be represented and manipulated under a common framework. We propose such a framework by defining a fuzzy Description Logic extended with Dempster–Shafer theory. Furthermore, we regard our method as a DL extension and we implemented it by a metaontology that captures Dempster–Shafer Fuzzy statements.

Cooperative robotic networks for underwater surveillance: an overview

Underwater surveillance has traditionally been carried out by means of surface and undersea manned vessels equipped with advanced sensor systems. This approach is often costly and manpower intensive. Marine robotics is an emerging technological area that enables the development of advanced networks for underwater surveillance applications. In contrast with the use of standard assets, these advanced networks are typically composed of small, low-power, and possibly mobile robots, which have limited endurance, processing and wireless communication capabilities. When deployed in a region of interest, these robots can cooperatively form an intelligent network achieving high performance with significant features of scalability, adaptability, robustness, persistence and reliability. Such networks of robots can be the enabling technology for a wide range of applications in the maritime domain. However, they also introduce new challenges for underwater distributed sensing, data processing and analysis, autonomy and communications. The main thrust of this study is to review the underwater surveillance scenario within a framework of four research areas: (i) underwater robotics, (ii) acoustic signal processing, (iii) tracking and distributed information fusion, and (iv) underwater communications networks. Progress in each of these areas as well as future challenges is presented.

An Adaptive Fusion Strategy for Distributed Information Estimation Over Cooperative Multi-Agent Networks

In this paper, we study the problem of distributed information estimation that is closely relevant to some network-based applications, such as distributed surveillance, cooperative localization, and optimization. We consider a problem where an application area containing multiple information sources of interest is divided into a series of subregions in which only one information source exists. The information is presented as a signal variable, which has finite states associated with certain probabilities. The probability distribution of information states of all the subregions constitutes a global information picture for the whole area. Agents with limited measurement and communication ranges are assumed to monitor the area, and cooperatively create a local estimate of the global information. To efficiently approximate the actual global information using individual agents’ own estimates, we propose an adaptive distributed information fusion strategy and use it to enhance the local Bayesian rule-based updating procedure. Specifically, this adaptive fusion strategy is induced by iteratively minimizing a Jensen–Shannon divergence-based objective function. A constrained optimization model is also presented to derive minimum Jensen–Shannon divergence weights at each agent for fusing local neighbors’ individual estimates. Theoretical analysis and numerical results are supplemented to show the convergence performance and effectiveness of the proposed solution.

A delay-aware schedule method for distributed information fusion with elastic and inelastic traffic

Information fusion is an efficient way to detect the specified events and extract useful information, especially in the context of big data. As a large-scale data-gathering system, Internet of Things (IoT) has the traffic with the mixed timing characteristics. The real-time observations with various delay constraints and the non-real-time observations are needed in information fusion. In order to guarantee the performance of Distributed Information Fusion (DIF), the paper focuses on the communication mechanism from the perspective of real-time delivery of sensing data. An online scheduling algorithm and its distributed implementation, named Delay-Guaranteed CSMA, are proposed. Both the timing constraints and the historical transmission statistics of sensors are taking into consideration. The simulation results have shown that the proposed policy achieves good delay-guaranteed satisfaction. The goal of real-time data delivery for distributed information fusion is achieved.

Distributed Cooperative Search with Collision Avoidance for a Team of Unmanned Aerial Vehicles Using Gradient Optimization

AbstractThis paper addresses the cooperative search problem for a team of unmanned aerial vehicles (UAVs) with limited field-of-view (FOV) and available overload constraints. First, the models of environment, UAV, image sensor, and communication are established. Second, a modified distributed information fusion strategy based on the Bayesian rule is proposed, which can make all the individual probability maps converge to the same one and reflect the true existence or nonexistence of targets within each cell. Third, a distributed gradient-based optimization method for path planning involved in the cooperative search is proposed and the available overload constraint of the UAV can be taken into account. Fourth, the aforementioned distributed gradient-based optimization method is improved by involving collision avoidance, establishing reasonable safety distance constraints, and using the Lagrange multiplier. Finally, the correctness and effectiveness of the proposed method are validated and demonstrated via ...

Distributed Information Fusion in Multistatic Sensor Networks for Underwater Surveillance

Surveillance in antisubmarine warfare has traditionally been carried out by means of submarines or frigates with towed arrays. These techniques are manpower intensive. Alternative approaches have recently been suggested using distributed stationary and mobile sensors, such as autonomous underwater vehicles (AUVs). In contrast with the use of standard assets, these small, low-power, and mobile devices have limited processing and wireless communication capabilities. However, when deployed in a spatially separated network, these sensors can form an intelligent network achieving high performance with significant features of scalability, robustness, and reliability. The distributed information FUSION (DIFFUSION) strategy, in which the local information is shared among sensors, is one of the key aspects of this intelligent network. In this paper, we propose two DIFFUSION schemes, in which the information shared among sensors consists of: 1) contacts, generated by the local detection stage and 2) tracks, generated by the local tracking stage. In the first DIFFUSION scheme, contacts are combined at each nodes using the optimal Bayesian tracking based on the random finite set formulation. In the second DIFFUSION scheme, tracks are combined using the track-to-track association/fusion procedure, then a sequential decision based on the association events is exploited. A full validation of the DIFFUSION schemes is conducted by the NATO Science and Technology Organization-Center for maritime research and experimentation during the sea trials Exercise Proud Manta 2012-2013 using real data. Performance metrics of DIFFUSION and of local tracking/detection strategies are also evaluated in terms of time-on-target (ToT) and false alarm rate (FAR). We demonstrate the benefit of using DIFFUSION against the local noncooperative strategies. In particular DIFFUSION improves the level of TOT (FAR) with respect to the local tracking/detection strategies. In particular, the TOT is increased over 90%-95% while the FAR is reduced of two order of magnitude. The problem of communication failures, data not available from the collaborative AUV during certain periods of time, is also investigated. The robustness of DIFFUSION with respect to these communication failures is demonstrated, and the related performance results are reported here. In particular, with 75% of communication failures the ToT is over 90%-95% with a relatively small increase of the FAR with respect to the case of perfect communication.

Towards distributed intelligent sensor and information fusion

Recent industrial applications are implemented in a modular way, resulting in flexibility during the whole life cycle, i.e., setup, operation, and maintenance. This applies especially to larger applications like logistic, production, and printing processes. Their modular character is resulting from the constantly increasing complexity of such installations, which makes their supervision for securing reliable operation a difficult task: the data of hundreds (if not thousands) of signal sources must be acquired, communicated, and evaluated for system diagnosis. In this contribution we summarize the challenges arising in such applications and show that distributed sensor and information fusion for modular self-diagnosis tackles these challenges. Here, we propose an innovative distributed architecture encompassing intelligent sensor nodes, self-configuring real-time communication networks, and a suitable sensor and information fusion system for condition monitoring. New challenges arise in the context of distributed information fusion systems, which are identified and to which an outlook on future solutions is provided. A number of these solutions have already been discovered, implemented, and are evaluated in the context of a demonstrator, which resembles a real-world printing application.

Multisensor Information Fusion State Estimator for Descriptor and Non Descriptor Systems

Multisensor distributed information fusion state estimator is presented in this paper. The algorithm deals with descriptor and non descriptor discrete time time-invariant stochastic linear system which is described by state space model. Under the assumption of the observability of the system, the state of the system is a linear combination of input white noise, observation white noise and observation signal. Further non-recursive state estimators algorithm is presented, which can be computed by the white noise estimators and measurement predictor. In order to improve the accuracy of the state estimator, this paper presents information fusion algorithm including matrix weighted, diagonal matrices weighted, scalar weighted and covariance intersection fusion, in the sense of linear minimum variance. The formula of optimal weighting coefficients is given. The algorithm analyzes the relationship between the accuracy and the computation of the four fusion algorithm. A simulation example for non descriptor system with 3 sensors shows its correctness and effectiveness.

Multisensor Distributed Information Fusion White Noise Wiener Deconvolution Estimator

Multisensor distributed information fusion white noise wiener deconvolution estimator is presented in this paper. The algorithm is using the modern time series analysis method and white noise estimator under the linear minimum variance optimal fusion criterion. Gevers-Wouters (G-W) algorithm are also used in this paper. This paper presents information fusion algorithm including scalar weighted and covariance intersection fusion. The algorithm analyzes the relationship between the accuracy and the computation of the two fusion algorithm. The formula of optimal weighting coefficients is given. Compared with the single sensor case, the accuracy of the fused filter is greatly improved. It can be applied to signal processing in oil seismic exploration, communication and other fields. A simulation example for information fusion BernoulliGaussian white noise deconvolution filter shows its correctness and effectiveness.

Multisensor Information Fusion State Estimator for Systems with Random Sensor Errors

In this paper, a multisensor distributed information fusion state estimator for discrete time stochastic linear systems with random sensor errors is presented. Based on state-space model, the white noise estimator and the observation predictor are applied in this algorithm. Modern time series analysis method and Gevers-Wouters(G-W) algorithm are also used in this paper. The algorithm can deal with the filtering, smoothing and prediction problems via a unified method. In order to improve the estimation accuracy, the multisensor distributed information fusion method is adopted, which calculates the weighting parameters with the forms of matrix, diagonal matrices and scalars respectively, in the sense of linear minimum variance. Among those three kinds of fusion methods, the method weighted by matrix has the highest accuracy but more computation, while the one weighted by scalar has the lowest accuracy but less computation. A simulation example for a typical tracking system with 3sensor shows the correctness, validity and no obvious difference among three kinds of the fusion algorithms.

Based on Neural Network Information Fusion System Architecture Evolution Research in the Theory of Evolution

Under the environment of the human nervous system is multi-sensor information fusion architecture for the most flexible. Papers through on people cranial nerve Yuan of evolution course and information fusion system Fusion unit of evolution process of compared research, proposed has a distributed information fusion system of evolution system structure, the system structure is information fusion system structure evolution of a senior form, it can according to network system different of mission, and external environment and network system of State automatically formed currently by knows of most effective of information fusion system structure, more effective to reached fusion purposes.

Task-oriented distributed data fusion in autonomous wireless sensor networks

The evolution of sensor node capabilities makes distributed data fusion possible in autonomous wireless sensor networks (WSNs) for various purposes. We propose a framework of task-oriented distributed data fusion, and investigate the assignments of heterogeneous sensors on nodes in the network, so that system performance can adapt the dynamics of tasks and the topology of self-organised networks. This work provides an approach to improving the fusion performance based on partial information from WSNs. Such a task-oriented autonomous wireless sensor network can be a part of the infrastructure for cloud computing through the Internet. A hierarchy of linguistic decision trees is used to map the distributed information fusion. The performance evaluation is done from five aspects, quality of estimates, computing scalability, real-time performance, data flow, and energy consumption. Four classic decision-making problems in the UCI machine learning repository are used as the virtual measures from WSNs to demonstrate the merits of the proposed system compared with the central fusion models.

Information Fusion Predictive Control Algorithm for Time-Varying Systems with Unknown Stochastic System Bias

This paper puts forward on a fast distributed information fusion predictive control algorithm for the time-varying system with unknown stochastic system bias. It is based on the distributed fusion estimation algorithms and state-space model. The optimal information fusion rule for this algorithm is weighted by matrices, diagonal matrices and scalars. It can avoid the complicated Diophantine equation, thus obviously reduces the amount of calculation. Via the distributed information fusion algorithm, the comparison of algorithm in this paper with the local sensor, this algorithm improves stability and accuracy for the timevarying system with unknown stochastic system bias. By testing through the three-sensor target tracking control system simulation, this algorithm shows its effectiveness and correctness, and the results of simulation also show no significant difference in error between the three kinds of distributed fusion algorithm. With reduction of calculation using the scalar weighting fusion predictor, the information fusion estimation algorithm presented in this paper also improves the calculation speed and accuracy.

Distributed Information Fusion Structure Based on Data Fusion Tree

A distributed information fusion structure based on data fusion tree is built to realize precise localization and efficient navigation for the mobile robot. The multi-class, multi-level information from robot and environment is fused using different algorithms in different levels, and make the robot have a deeper understanding to the whole environment. Experiments demonstrate that the new model proposed in the paper can improve the positioning precision of robot greatly, and the search efficiency and success rate are also better than traditional mode.

The Self-Tuning Distributed Information Fusion Kalman Filter for ARMA Signals

For the multisensor Autoregressive Moving Average (ARMA) signals with unknown model parameters and noise variances, using the Recursive Instrumental Variable (RIV) algorithm, the correlation method and the Gevers-Wouters algorithm with dead band, the fused estimators of unknown model parameters and noise variances can be obtained. Then substituting them into optimal fusion signal filter weighted by scalars, a self-tuning distributed fusion Kalman filter is presented. Using the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning fused Kalman signal filter converges to the optimal fused Kalman signal filter, so that it has asymptotic optimality. A simulation example shows its effectiveness.

Distributed information fusion models for regional public health surveillance

Biosurveillance systems designed and deployed in the United States and abroad to allow public health authorities to monitor the health of their communities have significant design limitations despite their wide usage. One limitation is the lack of algorithmic solutions to combine local data sources for regional situation awareness. The objective of the current study is to develop and demonstrate the value of automated information fusion methods applied to the distributed neighboring public health sites. A prototype system consisting of distributed Bayesian models was designed to enable informed regional and local cognitive decision support response. The Intelligent Decision Support Network (IDSN) is composed of Bayesian Information Fusion Models (BIFMs) that target a particular syndrome or disease type. Using local data from county health departments in Northern Virginia for the time period between August 2005 and May 2007, we estimated the probability of a gastrointestinal (GI) outbreak in two ways: First, based on data from the local hospitals only; and second, based on the relative probability of outbreak by combining local hospital data and probabilities of GI events from the neighboring counties’ BIFMs. Preliminary findings showed that the network of distributed models detected events that would be undetected without multi-jurisdictional data.

Analytical and Computational Evaluation of Scalable Distributed Fusion Algorithms

The theoretical fundamentals of distributed information fusion have been developed over the past two decades and are now fairly well established. However, practical applications of these theoretical results to dynamic sensor networks have remained a challenge. There has been a great deal of work in developing distributed fusion algorithms applicable to a network centric architecture. In general, in a distributed system such as ad hoc sensor networks, the communication architecture is not fixed. In those cases, the distributed fusion approaches based on pedigree information may not scale because of limited communication bandwidth. In this paper, we focus on scalable fusion algorithms and conduct analytical performance evaluation to compare their performance. The goal is to understand the performance of these algorithms under different operating conditions. Specifically, we evaluate the performance of channel filter fusion, naive fusion, Chernoff fusion, Shannon fusion, and Bhattacharyya fusion algorithms. We also compare their performance to optimal centralized fusion under a specific communication pattern. The results show that the channel filter fusion, representing a first order approximation to the information graph fusion, is the only consistent fusion algorithm.