Multi-sensor System Research Articles

Multisensory System for Long-Term Activity Monitoring to Facilitate Aging-in-Place.

Demographic changes and an ageing population require more effective methods to confront the increased prevalence of chronic diseases which generate dependence in older adults as well as an important rise in social expenditure. The challenge is not only to increase life expectancy, but also to ensure that the older adults can fully enjoy that moment in their lives, living where they wish to (private home, nursing home, …). Physical activity (PA) is a representative parameter of a person's state of health, especially when we are getting older, because it plays an important role in the prevention of diseases, and that is the reason why it is promoted in older adults. One of the goals of this work is to assess the feasibility of objectively measuring the PA levels of older adults wherever they live. In addition, this work proposes long-term monitoring that helps to gather daily activity patterns. We fuse inertial measurements with other technologies (WiFi- and ultrasonic-based location) in order to provide not only PA, but also information about the place where the activities are carried out, including both room-level location and precise positioning (depending on the technology used). With this information, we would be able to generate information about the person's daily routines which can be very useful for the early detection of physical or cognitive impairment.

Shadow-Imaging-Based Triangulation Approach for Tool Deflection Measurement.

As incrementally formed sheets show large geometric deviations resulting from the deflection of the forming tool, an in-process measurement of the tool tip position is required. In order to cover a measuring volume of 2.0 m × 1.0 m × 0.2 m and to achieve measuring uncertainties of less than 50 µm, a multi-sensor system based on triangulation is realized. Each shadow imaging sensor in the multi-sensor system evaluates the direction vector to an LED attached to the tool, and the three-dimensional position of the LED is then determined from the combination of two sensors. Experimental results show that the angle of view from the sensor to the LED limits both the measurement range and the measurement uncertainty. The measurement uncertainty is dominated by systematic deviations, but these can be compensated, so that the measurement uncertainty required for measuring the tool tip position in the ISF is achieved.

Two-stage quality monitoring of a laser welding process using machine learning

Abstract In production, quality monitoring is essential to detect defective elements. State-of-the-art approaches are single-sensor systems (SSS) and multi-sensor systems (MSS). Yet, these approaches might not be suitable: Nowadays, one component may comprise several hundred meters of the weld seam, necessitating high-speed welding to produce enough components. To detect as many defects as possible in time, fast yet precise monitoring is required. However, information captured by SSS might not be sufficient and MSS suffer from long inference times. Therefore, we present a confidence-based cascaded system (CS). The key idea of the CS is that not all data are analyzed to obtain the quality weld, but only selected ones. As evidenced by our results, all CS outperform SSS in terms of accuracy and inference time. Further, compared to MSS, the CS has hardware advantages.

Fusion filtering for rectangular descriptor systems with stochastic bias and random observation delays under weighted try-once-discard protocol

This paper studies the fusion filtering problem for a class of multi-sensor rectangular descriptor systems with stochastic bias and random observation delays under weighted try-once-discard (WTOD) protocol. In order to reflect the stochastic behavior generated by disturbances, a dynamic equation is utilized to describe the stochastic bias. The random observation delays are modeled by Bernoulli random variables with individual occurrence probability. Furthermore, the WTOD protocol is used to lessen the network traffic burden. Firstly, by introducing matrix transformation and the singular value decomposition method, the rectangular descriptor systems are transformed into two non-descriptor subsystems with lower orders, where the augmentation technique is adopted to obtain new augmented systems. Secondly, the relevant local filter gains are obtained by minimizing the filtering error covariances based on the local filters (LFs) designed by new systems. Subsequently, the local filters and the fusion filters of the original descriptor systems are obtained by state transformation and covariance intersection (CI) fusion technique, respectively. The major contribution lies in that a novel fusion filtering algorithm avoiding the calculation of error cross-covariances is proposed for rectangular descriptor systems, where special effort is devoted to handling the impacts from the WTOD protocol, stochastic bias and random observation delays. Finally, the effectiveness of the fusion algorithm is demonstrated by a simulation example.

Nonlinear dimensionality reduction methods for potentiometric multisensor systems data analysis

AbstractElectrochemical multisensor systems were proven to be a very perspective research direction in modern analytical chemistry. The multisensor approach assumes an employment of cross‐sensitive chemical sensors in combination with multivariate data processing methods. Dimensionality reduction of the data obtained from multisensor systems is a very important step and it is mostly based on the traditional tools of chemometrics, such as Principal Component Analysis (PCA). In case of chemically complex samples, the response of multisensor systems may have a complex nonlinear nature and the use of linear modelling methods does not seem optimal. However, the potential of nonlinear dimensionality reduction methods in the processing of multisensor data has not yet been systematically studied. In this report we aim to fill this gap and assess the performance of various nonlinear dimensionality reduction tools: Isomap, Self‐Organizing Kohonen Maps, and Autoencoder. These methods were explored using three datasets from potentiometric multisensor systems obtained in various real applications. It was shown that nonlinear dimensionality reduction methods give the possibility to obtain additional and more detailed information about the analyzed objects/processes compared to PCA. However, calculation time for nonlinear dimensionality reduction methods essentially exceeds that for PCA, and it can be a limiting factor for application of such algorithms.

Automated Insulin Delivery System and People With Type 2 Diabetes: A Topic With Many Facets.

Optimizing glucose control is of interest also for patients with type 2 diabetes (T2D). While systems for automated insulin delivery are widely used for patients with type 1 diabetes, as documented by many publications, this is not the case with T2D. Because of the number of such patients, this will change drastically in the next years. Manufacturers can transfer many learnings from type 1 to type 2; however, specific clinical aspects have to be considered. This commentary will discuss these aspects and some of the current activities. Future automated insulin delivery (AID) systems will take data from multisensor systems into account to individualize the AID algorithm, supported by artificial intelligence. There is a high need to document the benefits of AID systems in this patient group.

Simultaneous localization and mapping of mobile robots with multi-sensor fusion

Abstract Simultaneous localization and map building of mobile robots is an important research direction in robotics, and multi-sensor fusion is one of the key technologies for mobile robots to achieve autonomous navigation. In this paper, firstly, based on the framework of the SLAM system, we analyze the extraction of FAST feature points and BRIEF feature descriptor matching and propose the CSD-ORB algorithm. Secondly, the FKCS-LD algorithm based on the fusion map key frame and center selection strategy is proposed. The suitable multi-sensor fusion SLAM system is selected to build the back-end optimization algorithm based on the bit-pose map. Finally, we complete the comparison experiments of the multi-sensor fusion SLAM system on standard data sets, robot hardware assembly and software environment construction, and actual field testing. The experimental data show that the trajectory calculation accuracy can be further improved by using local map tracking, i.e., the current frame and local keyframe to calculate the bit pose, in which the maximum error is 6 cm, the root mean square error is reduced to 3.6 cm, the maximum value of local optimization is 0.66034, the minimum value is 0.146232, the average is 0.32812, and the median is 0.33023. The experiment proves that the multi-sensor fusion SLAM system can achieve centimeter-level accuracy and has global consistency and real-time performance, and can effectively complete the task of simultaneous robot positioning and map building.

A multi-sensor indoor tracking system for autonomous marine model-scale vehicles

Attitude estimation is a popular topic in marine engineering and robotics; the position and orientation of a vehicle are required as feedback from several control algorithms to improve autonomous navigation capabilities, such as dynamic positioning, track keeping, and autodocking. Typically, position and heading angles are provided by the Global Positioning System and compass. Usually, during the development and testing, the experiments are performed in a controlled environment, such as an indoor test tank. However, Global Positioning System systems can be unreliable due to non-negligible model scale errors or the absence of line-of-sight with the satellites. This article presents an experimental tracking system setup suitable for indoor testing facilities. In particular, the paper presents a tracking system based on a GigE camera and ArUco markers detection and a LiDAR-based tracking system relying on unsupervised machine learning techniques. The MQTT broker-based publish/subscribe message-queuing protocol allows real-time data communication and sharing. The proposed system was developed, installed, and tested in the COMPASS laboratory (University of Genoa). The two tracking systems’ outcomes have been compared. Eventually, an accuracy analysis was performed by comparing the results to the ground truth in purpose-built experiments. The proposed approach can estimate the degrees of freedom of a self-propelled model-scale vessel in an indoor testing facility without requiring active or powered markers and share the information acquired with multiple entities in real-time at a high frame rate.

WMS: Wearables-Based Multisensor System for In-Home Fitness Guidance

Human activity recognition (HAR) is now a powerful in-home fitness assistive technology. This article presents a wearables-based multi-sensor system (WMS), which not only supports conventional functionalities such as motion evaluation based on multi-dimensional information about the users body (movement speed, angle, muscle states, etc.), but also provides advanced services, including assessing training fatigue and providing real-time, elastic, and professional training advice. The proposed WMS is experimentally validated by yielding 90.11% accuracy of motion evaluation with 36% and 23% improvement of fitness effect on bicep girth and muscular endurance, indicating its feasibility to prompt the development of HAR in the in-home fitness training domain.

Research on Multi-Sensor Data Fusion Positioning Method of Unmanned Ships Based on Threshold- and Hierarchical-Capacity Particle Filter

To improve the positioning accuracy of unmanned ships, a multi-sensor system including ZigBee, a Global Positioning System (GPS), and BeiDou Navigation Satellite System (BDS) is constructed, and an adaptive multi-sensor data fusion positioning method based on the threshold and hierarchical capacity particle filter (TCPF) is designed. First, the ZigBee-GPS/BDS multi-sensor measurement data is preprocessed to achieve a consistent space–time reference and transformed into the same coordinate system by projection. Then, the fault data is weighted and corrected through the consistency inspection of ZigBee-GPS/BDS multi-sensor positioning data, and the corresponding confidence factor is given according to the confidence distance of the positioning data; furthermore, the confidence factor is associated with stratified sampling. After that, the multi-sensor positioning data is filtered and denoised using a basic particle filter. Finally, a TCPF data fusion algorithm is designed, and the navigation positioning data of the unmanned ship is fused and filtered to obtain its positioning information. Numerical tests show that compared with other filtering algorithms, the mean square root error and standard deviation of the proposed TCPF algorithm decrease by an average of 25.0% and 28.0%, respectively, which verifies its high filtering accuracy and its advantages in suppressing particle degradation and avoiding sample scarcity. The experimental tests show that compared with other fusion algorithms, the proposed TCPF algorithm can not only realize the precise positioning during unmanned ship navigation, but also in the positioning and fault tolerance test, the average positioning error, root-mean-square error, and standard deviation of the former decrease by 36.0%, 38.0%, and 37.0%, respectively, and the corresponding performance indicators of the latter decrease by an average of 20.0%, 19.5%, and 17.5%, which verifies that it has the advantages of high data reliability and good filtering fault tolerance, and helps to improve the positioning accuracy of unmanned ships.

PENERAPAN TEKNOLOGI CERDAS PENYIRAMAN TANAMAN HIDROPONIK BERBASIS MIKROKONTROLER DAN MULTISENSOR PADA PEMBUDIDAYA TANAMAN HIDROPONIK KABUPATEN MAJENE

Smart technology has provided many benefits for all human activities, including hydroponic farming systems. The consistent challenge in the process of cultivating plants using hydroponic systems is the monitoring of partner farmers in managing plant nutrition. Smart technology, utilizing microcontrollers and multisensors, can enhance hydroponic management, particularly concerning the control of nutrient availability, including saturation, water pH, and water availability in the hydroponic system. The purpose of this activity is to address partner-related issues regarding their lack of knowledge and skills in managing businesses aided by smart technology such as microcontrollers and multisensors and to improve the economic viability of partner businesses by producing nutritious and high-quality vegetables. The method used in this community engagement activity involves initial observation at the partner's business location, theoretical instruction to enhance partner knowledge, and finally, training in the creation and operation of smart technology involving microcontrollers and multisensors in the partner's hydroponic area. Questionnaires were distributed to assess the initial conditions before the training and to measure the success of the training after its completion. The initial observation showed a lack of knowledge about smart technology using microcontrollers and multisensors. Based on this, theoretical training was conducted to improve partner knowledge about smart technology using microcontrollers and multisensors. The theoretical content included 1) technology and innovation in hydroponic plant nutrition, 2) an introduction to microcontrollers and sensors in agriculture/horticulture technology, and 3) a demonstration of monitoring physical parameters and nutrient content of hydroponic plants using the Wokwi simulator. Subsequently, a simulation of microcontroller and multisensory device use was carried out, resulting in an enhancement of partner skills in using microcontrollers and multisensors. The improvement in partner knowledge and skills after participating in this training is evident from the questionnaire results, and partner satisfaction with the conducted training is also evident. The conclusion of this community engagement activity is that partners have gained knowledge and skills after participating in the training on the application of smart technology for microcontroller-based irrigation of hydroponic plants with multisensor systems in hydroponic plant cultivation.

Multi-Sensors System and Deep Learning Models for Object Tracking.

Autonomous navigation relies on the crucial aspect of perceiving the environment to ensure the safe navigation of an autonomous platform, taking into consideration surrounding objects and their potential movements. Consequently, a fundamental requirement arises to accurately track and predict these objects' trajectories. Three deep recurrent network architectures were defined to achieve this, fine-tuning their weights to optimize the tracking process. The effectiveness of this proposed pipeline has been assessed, with diverse tracking scenarios demonstrated in both sub-urban and highway environments. The evaluations have yielded promising results, affirming the potential of this approach in enhancing autonomous navigation capabilities.

Monte Carlo variance propagation for the uncertainty modeling of a kinematic LiDAR-based multi-sensor system

Abstract Kinematic multi-sensor systems (MSS) are widely used for various applications, like mobile mapping or for autonomous systems. Depending on the application, insufficient knowledge of a system, like wrong assumptions about the accuracy of calibrations, might lead to inaccurate maps for mapping tasks or it might endanger humans in the context of autonomous driving. Uncertainty modeling can help to gain knowledge about the data captured by a system. Usually, uncertainty estimations for MSSs are done as backward modeling based on a comparison to reference datasets. In this paper, a forward modeling approach for the uncertainty modeling of a LiDAR-based kinematic MSS is chosen to estimate the uncertainty of an acquired point cloud. The MSS consists of a Leica Absolute Tracker and a platform with a 6-DoF sensor and Velodyne VLP-16 LiDAR. Results of multiple calibrations are used as the source for the uncertainty information for a Monte Carlo (MC) variance propagation of the point uncertainties. The deviations of the acquired point clouds in comparison to a ground truth can be decreased by an ensemble referencing process using the MC samples. Furthermore, the predicted uncertainties for the point clouds are well representing the actual deviations for reference panels closer to the system. Panels farther away indicate remaining distance depending effects.

A channel estimation method using denoising autoencoder for large-scale asymmetric backscatter systems

A novel channel estimation method based on deep learning algorithm is proposed for large-scale IoT networks. We consider asymmetric backscatter communication system to maintain low-power at sensor nodes. In order to obtain channel data, we design denoising autoencoder which consists of encoder with Feedforward Neural Network (FNN) and decoder with Convolutional Neural Network (CNN). Finally, the channel estimation error is minimized, while the pilots are optimized. Especially, we adopt beamforming technique that relies only on cascaded channel data to reduce complexity in multi-sensor system. It is shown that the accuracy is slightly degraded while the complexity is greatly reduced.

Multi-Sensor Fault Diagnosis for Misalignment and Unbalance Detection Using Machine Learning

Rotating machines frequently undergo various faults causing increased maintenance and operation costs. To minimize these costs, effective and intelligent methods are thus required. Different sensor modalities reflecting various faults should continuously be monitored and interpreted to enable these methods. In this work, two sensor modalities, Infrared Thermography (IRT), and vibration are used complementary to form a multi-sensor fault diagnosis system. This system is used to diagnose the three most occurring faults: misalignment, unbalance, and rotor disk eccentricity, as single, dual, and multi-faults in a rotating mechanical system. The high feature processing capabilities of a Deep Convolutional Neural Network (DCNN) and the high predictive capabilities of a Support Vector Machine (SVM) are combined along with the potential dimensionality reduction using Principal Component Analysis (PCA). The results show that the proposed method is robust and signifies its reliability towards the effective diagnosis of considered faults. Further, IRT-based fault diagnosis outperforms the vibration-based classification in all working conditions.

Anomaly Detection Against GPS Spoofing Attacks on Connected and Autonomous Vehicles Using Learning From Demonstration

GPS spoofing attacks pose great challenges to connected vehicle (CVs) safety applications and localization of autonomous vehicles (AVs). In this paper, we propose to utilize transportation and vehicle engineering domain knowledge to detect GPS spoofing attacks towards CVs and AVs. A novel detection method using learning from demonstration is developed, which can be implemented in both vehicles and at the transportation infrastructure. A computational-efficient driving model, which can be learned from historical trajectories of the vehicles, is constructed to predict normal driving behaviors. Then a statistical method is developed to measure the dissimilarities between the observed trajectory and the predicted normal trajectory for anomaly detection. We validate the proposed method using two threat models (i.e., attacks targeting the multi-sensor fusion system of AVs and attacks targeting the intersection movement assist application of CVs) on two real-world datasets (i.e., KAIST and Michigan roundabout dataset). Results show that the proposed model is able to detect almost all of the attacks in time with low false positive and false negative rates.

A Novel Method for Land Vehicle Positioning: Invariant Kalman Filters and Deep-Learning-Based Radar Speed Estimation

Autonomous and intelligent vehicles are multi-sensor systems operating in various environments and conditions. Due to their characteristics, inertial measurement units (IMUs) are typically the core component of such systems. However, these sensors rapidly accumulate errors due to biases and noise, degrading the positioning solution. Therefore, this paper presents a positioning solution that only uses three gyroscopes and one radar. The proposed method was tested using low-cost sensors in different scenarios, such as open-sky, urban and indoor areas. The key components of the method are the invariant Kalman filters and the use of deep neural networks to estimate the forward speed of the car using the radar readings. The method was tested on a custom dataset, and our integrated solution accurately estimates the vehicle's position, velocity, and orientation. We achieved, on average, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.45~\%$</tex-math></inline-formula> translational error in the tested scenarios, making the proposed method a robust alternative to current IMU-based positioning methods.

Enhancing the Capabilities of Electrochemical Sensor Arrays Using Nanomaterials and Nanostructured Films

In the last years electronic tongues (ET) based on electrochemical sensors coupled to a pattern recognition system, have been developed and applied to the analysis of a variety of foods [1]. Nowadays it is evident that improvements in the field involve the development of instruments dedicated to a specific application. This requires the search of sensors specifically designed to analyze an specific target, using the appropriate sensing materials and transduction methods adapted to a specific problem.Important advances in the field of electrochemical sensors are linked to the use of techniques and materials coming from the field of nanoscience. On one hand, incorporating nanomaterials in the sensing layers can enhance the sensitivity thanks to their excellent electrocatalytic properties. On the other hand, using nanostructured sensing layers can offer opportunities to increase the number of active sites at the surface and to develop structures similar to that of the cellular membranes that can host enzymes or other biologic receptors in a biomimetic environment. Here, the new strategies based on potentiometric and voltammetric sensors will be presented, paying special attention to the new developments based on nanobiosensors and the combinations of enzymes with nanomaterials.The developments of our group in the field of multisensor systems based on potentiometric sensors and biosensors applied to the analysis of milk are based on PVC membranes where different additives and plasticizers are added. The different compositions give rise to membranes with different porosity and hydrophilicity and hence an array of sensors with cross-sensitivity can be constructed. Such an array can discriminate main components of milk such as galactose, lactose, lactic acid or urea. The capability of the array is drastically enhanced when the membranes are modified with combinations of nanoparticles and enzymes specific to components present in milk such as galactose oxydase, lactose dehydrogenase or urease [2].Voltammetric sensors can be used to analyze liquid mixtures containing electroactive substances. The example shown here is the wine, which is an hydroalcoholic solution rich in antioxidants such as the polyphenols and sugars such as glucose.In arrays of voltammetric sensors, electrodes are chemically modified with electrocatalytic materials that increase the intensity of the signals while reducing the oxidation potential of the phenols. Classic materials such as phthalocyanines or conducting polymers are excellent electrocatalytic materials that facilitate the electron transfer between the phenols or sugars and the electrode. Metal nanoparticles and nanocarbons are also excellent electron mediators [3].Nanostructured Sensing layers prepared using de Layer by Layer (LbL) or the Langmuir-Blodgett (LB) technique can further increase the intensity of the signals due to the high surface-to-volume ratio. In addition, these techniques facilitate the preparation of films made of different materials with different functionalities. For instance, the combination of nanoparticles with phthalocyanines have synergistic effects that increase even further the intensity of the signals.Enzymes such as glucose oxydase or galactose oxydase (specific for the detection of sugars) or tyrosinase or laccase (specific for the detection of phenols) have been introduced in nanostructured films based on phospholipids that create a biomimetic environment enhancing the performance of the sensors.The biosensors described above have been combined to develop bioelectronic tongues (bio-ETs) that have been successfully used to discriminate and classify milk and wine samples. References Rodríguez-Méndez, M.L., Electronic noses and tongues in the food industry. Elsevier-Academic press, 2016 Perez-Gonzalez et al., Food Control, 145, 109425 (2023) Medina-Plaza et. Al. Biosens. Bioelectronics, 57, 276-286 (2015)

Impedance Spectroscopy: A New Tool for Dairy Analysis

In this work, an impedimetric bioelectronic tongue (bioET) formed by an array of interdigitated microelectrodes sensors was developed and applied in the discrimination of chemical components usually found in milk samples. Few studies of impedimetric bioETs applied in the dairy industry can be found in the bibliography due to the complexity of the characterization of milk matrixes. Therefore, the development and study of the capabilities of multisensory systems containing different advanced materials such as nanomaterials combined with biological molecules is a new field of research.In order to develop an array of sensors with enhanced sensitivity and selectivity, sensors were modified with silver nanoparticles (AgNPs) and enzymes including galactose oxidase (GaOx), glucose oxidase (GOx), beta-galactosidase (β-gal), urease (Ure) and lipase (Lip) were immobilized on the sensors surface. Silver nanoparticles have proved to be an excellent option to enhance the performance of biosensors due to their electrochemical properties, their high electrical conductivity, and their ability to amplify bio electrochemical signals.Atomic force microscopy (AFM) was used to study the distribution of AgNPs and enzymes in the sensor surface. Silver nanoparticles showed a homogeneous arrangement along the entire surface, moreover after the enzyme immobilisation a pronounced increase in the sensors surface roughness was observed ensuring the process of the biological molecule (Figure 1.A and Figure 1.B).The impedance analysis of standard solutions of interest in milk (glucose, galactose, lactose, urea and triglycerides) demonstrated that sensors with a higher concentration of silver nanoparticles (AgNPs) in their structure reported higher sensitivities towards compounds found in milk with a reduction in the impedance module, indicating that there has been an improvement in electronic transfer due to the interaction of the nanomaterial and the electrode surface an example of the sensor behaviour is shown in Figure 1.C. Moreover, sensors that combined the nanomaterial with an enzyme showed a greater ability to differentiate between increasing concentrations of the enzymes target molecules (Figure 1.D). Taking as an example the case of glucose the sensor modified with AgNPs and glucose oxidase achieved a lower impedance module compared with the other sensors thanks to its lower resistance to electronic transfer in the sensor due to the enzymatic activity enhanced by the silver nanoparticles.Once all the standard solutions had been analysed, the chemometric analysis of their response was carried out applying a principal components analysis (PCA) approach which showed that the bioET developed was able to discriminate between the standard solutions (glucose, galactose, lactose, urea and triglycerides), resulting in five clusters according to their nature with a 79% of the variance of the original data obtained by the first two components of the system. Sugars appear in the positive region of the first component divided into three groups (lactose, galactose and glucose). In the negative region of the first component appears urea and the suspension of triglycerides that can be differentiated over the second component.Lastly, a first approach was made to the analysis of milk samples with different nutritional characteristics such as their fat, lactose and urea content. The utilization of the bioelectronic tongue showed how the different contents of molecules of interest was directly related with the response of the impedimetric sensors due to changes in the interaction of the sensors surface and the milk matrix. As it was expected, higher concentrations of fat caused an increase in the impedance module due to their interference in the electronic transfer process. Furthermore, it was confirmed that the biosensors developed showed specific responses towards milk samples with higher values of sugar and proteins contents ensuring a high degree of cross-selectivity in the system.Figure 1: A) Characterization by AFM of the sensor modified with AgNPs. B) Characterization by AFM of the biosensor with AgNPs and GOx. C) Nyquist diagram of sensors with 20 (orange), 30 (black) and 40 μl (blue) of AgNPs in 0.1M glucose. D) Nyquist diagram of the response of a sensor with AgNPs and GOx in glucose 0.1M (red), 0.01M (green) and 0.001M (grey). Figure 1

Prediction based fusion estimator-controller for an event-triggered multi-sensor cyber-physical systems

This article focusses on development of centralized fusion estimator-controller for event-triggered multi-variable(MV) multi-sensors(MS) cyber–physical systems. The unified mathematical model is developed, which is able to take care of simultaneous occurrences of packet loss in S-E and C-A channels and event-triggered conditions. The model uses current measurements and state predictions to handle communication flaws and untriggered sensor measurements. The centralized fusion estimator is developed using orthogonal projections for augmented MV-MS system, offering a globally optimal solution. The proposed fusion estimator possesses linear minimum variance property and it is unbiased. Feedback control is designed and it ensures mean square stability. Zero estimate error concept is introduced to reduce the computation burden, network congestion and energy utilization. The novel residual-based cyber-attack detection algorithm is developed. The proposed method is validated for cruise control system and performance is compared with other popular works.