Multi-sensor Network Research Articles

Distributed adaptive moving horizon estimation for multi-sensor networks subject to quantization effects

This paper investigates the distributed state estimation problem for multi-sensor networks with quantized measurements. Within the Bayesian framework, a distributed adaptive moving horizon estimation algorithm is developed. Unlike the existing methods regarding quantized errors roughly as bounded uncertainties, the posterior distributions of the errors are demanded to be derived. To overcome the difficulty of evaluating the posterior distributions for series of the states and quantized errors jointly, the variational Bayesian methodology is adopted to approximate the true distributions. Based on the fixed-point iteration method, the update rules are analytically derived, with the convergence criterion provided. Furthermore, by incorporating the average consensus algorithm into the prediction process, all sensors can achieve consensus on their estimates in a distributed manner. Finally, a numerical example of target tracking under logarithmic and uniform quantization effects is given to illustrate the validity of the proposed algorithm.

Rolling bearings fault diagnosis based on two-stage signal fusion and deep multi-scale multi-sensor network

In order to realize high-precision diagnosis of bearings faults in a multi-sensor detection environment, a fault diagnosis method based on two-stage signal fusion and deep multi-scale multi-sensor networks is proposed. Firstly, the signals are decomposed and fused using weighted empirical wavelet transform to enhance weak features and reduce noise. Secondly, an improved random weighting algorithm is proposed to perform a second weighted fusion of the signals to reduce the total mean square error. The fused signals are input into the deep multi-scale residual network, the feature information of different convolutional layers is extracted through dilated convolution, and the features are fused using pyramid theory. Finally, the bearings states are classified according to the fusion features. Experiment results show the effectiveness and superiority of this method.

Performance evaluation of a nuclear facility monitoring system using multi-sensor network and artificial intelligence algorithm

As the use of nuclear and radiation technologies increases, the importance of radiation safety and monitoring increases. In this study, we develop a nuclear facility monitoring system (NFMS) for rapid response to radiation accidents in nuclear material storage facilities; (1) multi-sensor network based on NaI(Tl) detector and FPGA-DAQ system and (2) an artificial neural network (ANN) algorithm for tracking radiation source location. Energy resolution and sensitivity of the detectors were evaluated to accurately track the location of radioactive materials and to identify nuclides using the multi-sensor network. To evaluate the localization accuracy of NFMS, a test storage facility was built and experiments were performed. Localization accuracy was obtained by analyzing the measured counts for each detector using an artificial intelligence (AI) based ANN algorithm, confirming an accuracy of over 99 %. The developed NFMS is expected to contribute to the safe management of radioactive materials in nuclear facilities.

Sequential track fusion in multi-sensor networks of unscented Kalman filters: A case of slip estimation in planetary mobile robots

Accurate wheel slip estimation facilitates Wheeled Mobile Robots (WMRs) with improved localization and traversability monitoring which are crucial to their autonomy in challenging planetary environments. Although distributed track-level fusion offers better computational efficiency, often sensor-level fusion is adopted in slip estimators of planetary rovers. Extending upon our earlier work, we develop a novel explicit track-to-track fusion algorithm for multi-sensor networks of unscented Kalman filters that has immediate application to slip ratio estimation in WMRs. The algorithm demonstrates better consistency compared to the existing fusion techniques since it implements a sub-optimal fusion rule to sequentially combine local tracks. It also saves computational power due to the recursive propagation of cross-covariance matrices based on the statistical linearization technique, instead of performing online optimizations. We prove that this recursion only requires the information of the first and last tracks in a sequence if all local tracks are unbiased. We rigorously study various key properties of the developed fusion algorithm and show its superior level of confidence when compared to the two prominent fusion methods of sequential and batch covariance intersection. The slip ratio estimation in a six-wheel WMR is considered as a case study, where the steerable wheel sets act as a network of sensors. The proposed slip estimator works based on the rigid body kinematics and readings of purely proprioceptive sensors, i.e., an inertial measurement unit and encoders. The slip estimator’s performance is evaluated in a high-fidelity software-in-the-loop simulation environment connecting MATLAB and CM Lab’s Vortex Studio software. In a comparison study that considers four rival strategies, we show the superiority of the proposed fusion method offering a balance between consistency, accuracy, and speed in real-time slip estimations.

Distributed Joint Detection, Tracking, and Classification via Labeled Multi-Bernoulli Filtering.

In this article, we propose a novel approach to distributed joint detection, tracking, and classification (D-JDTC) of multiple targets by means of a multisensor network. The proposed approach relies on labeled multi-Bernoulli (LMB) random finite set modeling of the multisensor state, and consists of two main tasks, that is, local filtering in each individual node and data fusion among multiple nodes. For local filtering, the LMB filter is extended to JDTC by augmenting the target state to incorporate class and mode information. Further, the well-known generalized covariance intersection and recently developed minimum information loss fusion paradigms are exploited for data fusion among sensors. The effectiveness of the resulting algorithm, called D-JDTC-LMB, is assessed via simulation experiments.

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.

Precipitation Vertical Structure Characterization: A Feature-Based Approach

Abstract The three-dimensional (3D) structure of precipitation systems is highly dependent on hydrometeor formation processes and microphysics. This study aims to characterize distinct vertical profiles of precipitation regimes by relying on the availability of a high-quality, spatially dense radar network and its capability to observe the 3D structure of the storms. A deep-learning-based framework, coupled with unsupervised clustering methods, is developed to identify types of precipitation structures irrespective of their physical properties. A 6-month period of 3D reflectivity profiles from the Multi-Radar Multi-Sensor (MRMS) network is used to identify different regimes and investigate their properties with respect to the underlying environmental conditions. Dominant features retrieved from radar reflectivity profiles using convolutional neural-network-based autoencoders are employed to identify similar-looking vertical structures using coupled k-means and agglomerative clustering algorithms. The k-means method identifies distinct groups, while the agglomerative clustering visualizes intercluster relationships. The framework identifies 18 clusters that can be broadly combined into five groups of varied echo-top heights. The 18 clusters demonstrate variability with respect to structural features and precipitation rate/type, implying that profiles in each group belong to a physically different precipitation regime. An independent analysis of the regime properties is conducted by matching the MRMS reflectivity profiles with environmental parameters derived from the High-Resolution Rapid Refresh model forecasts. The distribution of the environmental variables confirms cluster-specific feature properties, confirming the physics-based regime separation across the clusters and their dependence on the vertical structure. The identified precipitation regimes can assist in developing physics-guided retrievals and studying precipitation regimes. Significance Statement This study proposes a systematic model to identify precipitation profiles of distinct vertical structures and evaluate their dependence on environmental conditions. The model was developed using ground-based radar observations; however, there is potential to extend this model to reflectivity profiles from both ground- and satellite-based sensors. In addition, the identified precipitation regime clusters could be a proxy for the vertical structure of precipitation systems and assist in determining the structural variability within traditional precipitation type classification (e.g., convective versus stratiform). Moreover, identifying the precipitation regimes could also be used to improve satellite-based precipitation retrievals. Finally, a better understanding of precipitation structure would also help improve the initialization of climate models.

Optimal Distributed Filter for State and Unknown Input Estimations in Multi-Sensor Networks

This brief is aimed at estimating the state and unknown input of a linear system in the multi-sensor network. First, a distributed filter consisting of the unknown input estimator and the state estimator is proposed, in which the unknown input estimator only uses its own information while the state estimator uses its in-neighbors’ information. Second, the optimal gain matrices are obtained by employing the weighted least squares method and minimizing the performance function, which allow to realize the optimal unbiased estimation of the proposed filter. Third, a sufficient condition about the given filter’s convergence is derived. Finally, a simulation is given for validating the given methodologies.

Multi-Target Tracking AA Fusion Method for Asynchronous Multi-Sensor Networks.

Aiming at the problem of asynchronous multi-target tracking, this paper studies the AA fusion optimization problem of multi-sensor networks. Firstly, each sensor node runs a PHD filter, and the measurement information obtained from different sensor nodes in the fusion interval is flood communicated into composite measurement information. The Gaussian component representing the same target is associated with a subset by distance correlation. Then, the Bayesian Cramér-Rao Lower Bound of the asynchronous multi-target-tracking error, including radar node selection, is derived by combining the composite measurement information representing the same target. On this basis, a multi-sensor-network-optimization model for asynchronous multi-target tracking is established. That is, to minimize the asynchronous multi-target-tracking error as the optimization objective, the adaptive optimization design of the selection method of the sensor nodes in the sensor network is carried out, and the sequential quadratic programming (SQP) algorithm is used to select the most suitable sensor nodes for the AA fusion of the Gaussian components representing the same target. The simulation results show that compared with the existing algorithms, the proposed algorithm can effectively improve the asynchronous multi-target-tracking accuracy of multi-sensor networks.

Random Number Generation Based on Heterogeneous Entropy Sources Fusion in Multi-Sensor Networks.

The key system serves as a vital foundation for ensuring the security of information systems. In the presence of a large scale of heterogeneous sensors, the use of low-quality keys directly impacts the security of data and user privacy within the sensor network. Therefore, the demand for high-quality keys cannot be underestimated. Random numbers play a fundamental role in the key system, guaranteeing that generated keys possess randomness and unpredictability. To address the issue of random number requirements in multi-sensor network security, this paper introduces a new design approach based on the fusion of chaotic circuits and environmental awareness for the entropy pool. By analyzing potential random source events in the sensor network, a high-quality entropy pool construction is devised. This construction utilizes chaotic circuits and sensor device awareness technology to extract genuinely random events from nature, forming a heterogeneous fusion of a high-quality entropy pool scheme. Comparatively, this proposed scheme outperforms traditional random entropy pool design methods, as it can meet the quantity demands of random entropy sources and significantly enhance the quality of entropy sources, ensuring a robust security foundation for multi-sensor networks.

On the use of matrix profiles and optimal transport theory for multivariate time series anomaly detection within structural health monitoring

In order for a practical application of structural health monitoring to be considered successful, not only is the detection of anomalies important but so is the tracking of various other behaviors, which may be induced by changing operational conditions or environmental variations. Rather than being problems of anomaly detection, these are known as problems of changing contexts or changing semantics. In this paper, we propose a novel, and efficient method which is able to perform both tasks: anomaly detection, and semantic analysis, simultaneously, and which is also able to scale to a multi-sensor network array. The proposed methodology works to combine a recent development from the field of time series data mining known as matrix profiling, as well as ideas from a popular area of mathematics known as optimal transport theory. In particular, matrix profiles will be used to generate curves of semantic segmentation, over multivariate time series data. From here, it will be demonstrated that by finding the unbalanced optimal transport barycenter one can successfully pool together information from a sensor array network in such a way that the presence of false positive transition points is reduced significantly when compared to the baseline mean. Moreover, this will be demonstrated to occur in a hierarchical fashion by employing a variational mode decomposition across each sensor in the network array. Overall, the proposed methodology: (i) Can work in the small data context without requiring a large amount of data for model training and validation, (ii) Applies readily to unsupervised and semi-supervised learning settings, (iii) Can extend to the online learning setting, (iv) Is adaptable and robust for sensor array network problems, (v) Does not require specifying any threshold, and (vi) Is highly interpretable. Finally, the proposed methodology will be validated on three different levels of investigation: (i) A numerical dataset of an aerospace model based on a two-dimensional plate model, (ii) An experimental dataset of a building from Los Alamos National Laboratories, and finally, (iii) A field investigation on a cable-stayed bridge located in Sydney, Australia.

Networked Sensing for Radiation Detection, Localization, and Tracking

The detection, identification, and localization of illicit radiological and nuclear material continue to be key components of nuclear non-proliferation and nuclear security efforts around the world. Networks of radiation detectors deployed at strategic locations in urban environments have the potential to provide continuous radiological/nuclear (R/N) surveillance and provide high probabilities of intercepting threat sources. The integration of contextual information from sensors such as video, Lidar, and meteorological sensors can provide significantly enhanced situational awareness, and improved detection and localization performance through the fusion of the radiological and contextual data. In this work, we present details of our work to establish a city-scale multi-sensor network testbed for intelligent, adaptive R/N detection in urban environments, and develop new techniques that enable city-scale source detection, localization, and tracking.

Online distributed detection of sensor networks with delayed information

Distributed decision on sensor network is affected by the bandwidth, network congestion, and computation capability of sensors, which cause the communication delays and feedback delays during the signal processing process. In this paper, the effects of state constraints, directed graph, communication delays and feedback delays on the online distributed parameter estimation problem for multi-sensor networks are considered comprehensively. To this end, we first propose an Online Decentralized Gradient Descent Algorithm (ODGDA) to overcome the effects of these factors and give the regret upper bounds for convex loss function and strongly convex loss function, respectively. Further, considering that the loss function may not be fully disclosed in the complex environment, we extend ODGDA to the scenario of bandit feedback and propose an Online Decentralized Bandit Feedback Algorithm (ODBFA), which is updated using historical state information stored by sensors. The analysis shows that, compared with the ODGDA which can directly use the gradient information for state update, the ODBFA utilizing two-point bandit feedback information does not lead to regret degradation in the order sense. Finally, the effectiveness of the proposed algorithm is verified by the multi-sensor network.

Optimal Distributed Finite-Time Fusion Method for Multi-Sensor Networks under Dynamic Communication Weight.

Aiming at the problem of distributed state estimation in sensor networks, a novel optimal distributed finite-time fusion filtering method based on dynamic communication weights has been developed. To tackle the fusion errors caused by incomplete node information in distributed sensor networks, the concept of limited iterations of global information aggregation was introduced, namely, fast finite-time convergence techniques. Firstly, a local filtering algorithm architecture was constructed to achieve fusion error convergence within a limited number of iterations. The maximum number of iterations was derived to be the diameter of the communication topology graph in the sensor network. Based on this, the matrix weight fusion was used to combine the local filtering results, thereby achieving optimal estimation in terms of minimum variance. Next, by introducing the generalized information quality (GIQ) calculation method and associating it with the local fusion result bias, the relative communication weights were obtained and embedded in the fusion algorithm. Finally, the effectiveness and feasibility of the proposed algorithm were validated through numerical simulations and experimental tests.

Distributed optimization of multi-integrator agent systems with mixed neighbor interactions

In this paper, we consider a constrained distributed optimization problem for multi-integrator agent systems with mixed neighbor interactions, where the neighbors of the agents can be classified as cooperative or noncooperative by introducing a cooperation-competition network into the modeling of the communication graph. The agent’s goal is to optimize the total objective function of the cooperators. Since agents can only communicate with their neighbors, they are unable to obtain complete information about cooperators. The distributed optimization algorithm is proposed to classify cooperative subnetworks by introducing additional auxiliary variables, and the outputs of all agents exponentially converge to the optimal solution of the optimization problem under the condition of satisfying the global equality constraint of the subnetwork. The Lyapunov stability theory is used to analyze the algorithm’s convergence. Simulation results show the effectiveness of the proposed algorithm in two examples of economic dispatch in smart grids and optimal area coverage in multi-sensor networks.

IoT-Enabled System for Detection, Monitoring, and Tracking of Nuclear Materials

A low-cost embedded system for high-energy radiation detection applications was developed for national security proposes, mainly to detect nuclear material and send the detection event to the cloud in real time with tracking capabilities. The proof of concept was built with state-of-the-art electronics such as an adequate Si-based photodetector, a trans-impedance amplifier, an ARM Cortex M4 microcontroller with sufficient ADC capture capabilities, an ESP8266 Internet of Things (IoT) module, an optimized Message Queuing Telemetry Transport (MQTT) protocol, a MySQL data base, and a Python handler program. The system is able to detect alfa particles and send the nuclear detection events to the CloudMQTT servers. Moreover, the detection message records the date and time of the ionization event for the tracking application, and due to a particular MQTT-optimized protocol the message is sent with low latency. Furthermore, the designed system was validated with a standard radiation instrumentation preamplifier 109A system from ORTEC company, and more than one node was demonstrated with an internet connection employing a 20,000 bits/s CloudMQTT plan. Therefore, the design can be escalated to produce a robust big data multisensor network.

Multi-sensor network tracking research utilizing searchable encryption algorithm in the cloud computing environment.

Presently, the focus of target detection is shifting towards the integration of information acquired from multiple sensors. When faced with a vast amount of data from various sensors, ensuring data security during transmission and storage in the cloud becomes a primary concern. Data files can be encrypted and stored in the cloud. When using data, the required data files can be returned through ciphertext retrieval, and then searchable encryption technology can be developed. However, the existing searchable encryption algorithms mainly ignore the data explosion problem in a cloud computing environment. The issue of authorised access under cloud computing has yet to be solved uniformly, resulting in a waste of computing power by data users when processing more and more data. Furthermore, to save computing resources, ECS (encrypted cloud storage) may only return a fragment of results in response to a search query, lacking a practical and universal verification mechanism. Therefore, this article proposes a lightweight, fine-grained searchable encryption scheme tailored to the cloud edge computing environment. We generate ciphertext and search trap gates for terminal devices based on bilinear pairs and introduce access policies to restrict ciphertext search permissions, which improves the efficiency of ciphertext generation and retrieval. This scheme allows for encryption and trapdoor calculation generation on auxiliary terminal devices, with complex calculations carried out on edge devices. The resulting method ensures secure data access, fast search in multi-sensor network tracking, and accelerates computing speed while maintaining data security. Ultimately, experimental comparisons and analyses demonstrate that the proposed method improves data retrieval efficiency by approximately 62%, reduces the storage overhead of the public key, ciphertext index, and verifiable searchable ciphertext by half, and effectively mitigates delays in data transmission and computation processes.

Application of multi-sensor network and artificial intelligence in health monitoring of medical geriatric care

Application of multi-sensor network and artificial intelligence in health monitoring of medical geriatric care

MIM-Graph: A multi-sensor network approach for fault diagnosis of HSR Bogie bearings at the IoT edge via mutual information maximization

The Internet of Things (IoT) is crucial in developing next-generation high-speed railways (HSRs). HSR IoT enables intelligent diagnosis of trains using multi-sensor data, which is critical for maintaining high speeds and ensuring passenger safety. Graph neural network (GNN)-based methods have gained popularity in HSR IoT research due to the ability to represent the sensor network as intuitive graphs. However, labeling monitoring data in the HSR scenario takes time and effort. To address this challenge, we propose a semi-supervised graph-level representation learning approach called MIM-Graph, which uses mutual information maximization to learn from a large amount of unlabeled data. First, the multi-sensor data is converted into association graphs based on their spatial topology. The unsupervised encoder is trained using global–local mutual maximization. The teacher–student framework transfers knowledge from the unsupervised encoder learned to the supervised encoder, which is trained using a small amount of labeled data. As a result, the supervised encoder learns distinguishable representations for intelligent diagnosis of HSR. We evaluate the proposed method using CWRU dataset and data from HSR Bogie test platform, and the experimental results demonstrate the effectiveness and superiority of MIM-Graph.

Double-event-triggered cooperative maximum correntropy estimation over wireless sensor networks

This paper is concerned with the distributed state estimation problem for a class of multi-sensor network systems with bandwidth constraints, measurement loss, and shot noises. To tackle these issues, the double-event-triggered cooperative maximum correntropy estimation algorithm is proposed following the proposed double-event-triggered communication mechanism and maximum correntropy criterion. With such an algorithm, measurement errors between adjacent events in the first-event trigger are applied to determine whether the sensor node needs to transmit data, and then current innovation samples in the second-event trigger are to detect measurement anomalies. Compared to existing approaches, the proposed algorithm can significantly reduce communication costs and improve sensors’ service life, accuracy and robustness. A simulation case-study concerning moving target tracking with multiple unmanned aerial vehicles (multi-UAVs) is worked out to demonstrate the effectiveness of the considered algorithm.