Multi-sensor Data Fusion Algorithm Research Articles

Multi-sensor data fusion algorithm based on the improved weighting factor

Aiming at the decrease of the accuracy of fusion data caused by the abnormal value and noise interference in the multi-sensor observations, this paper proposes a multi-sensor data fusion algorithm based on improved weighting factors. Firstly, the Dixon criterion is used to eliminate outliers in observations to avoid data containing gross errors. Then the Kalman filter algorithm is used to effectively reduce the noise impact caused by various reasons and provides the optimal data for weighted data fusion. Finally, an improved weighted fusion algorithm is used to comprehensively consider the nature of the sensor and the influence of various factors in the measurement process to obtain the best fusion data. The simulation analysis of the soil humidity in the greenhouse shows that the error of the multi-sensor data fusion algorithm based on the improved weighting factor is maintained at 0.04%-0.18%. Compared with the adaptive weighted fusion algorithm, the error of this algorithm is reduced by 0.12%, which verifies the algorithm’s effectiveness.

Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network

Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32%.

Multi-body sensor data fusion to evaluate the hippotherapy for motor ability improvement in children with cerebral palsy

Hippotherapy is a new rehabilitation therapy for children with cerebral palsy (CP). Although it has been proved to be effective in clinical research, a quantitative evaluation of such results is still lacking in previous studies. In this research, one method for evaluating the effectiveness of hippotherapy based on body sensor network (BSN) is proposed. The method adopts distributed magnetic, angular rate, and gravity (MARG) sensors to evaluate the gross motor function of CP children by multi-sensor data fusion algorithm, the comparison results with the golden standard optical capture system demonstrate the robustness of sensor fusion algorithm. Moreover, via tracking one-year’s hippotherapy projects, a pilot study was conducted for measuring and evaluating the motor coordination function and gait parameters of CP children. Finally, the rehabilitation of gross motor function for the chosen subjects under different treatment periods was evaluated through kinematic analysis. The results of our method show that some children’s symptoms, such as limb stiffness, poor joint range of motion, scissors gait, knee flexion gait, have been relieved. Thus, it provides an empirical basis for hippotherapy in the rehabilitation of motor function corresponding to CP children.

Retracted] Sports Injury Risk Assessment Based on Blockchain and Internet of Things

With the increase of people’s exercise in today’s society, how to exercise scientifically and healthily has attracted much attention. Therefore, sports injury risk assessment and monitoring system has attracted more and more attention in real‐time, flexibility, intelligence, and other aspects. To solve the above problems, this paper proposes a sports injury risk assessment based on blockchain and Internet of Things. By introducing computational power weight, a computational power balance D‐H algorithm based on Internet of Things blockchain network architecture is proposed. It can provide a secure and trusted interactive environment for the Internet of Things. On the basis of blockchain and Internet of Things, a multisensor data fusion algorithm is proposed to be applied to the analysis and evaluation of sports injury. A variety of physiological parameters of human motion state are collected through multisensor, the collected physiological parameters are processed by data fusion, and finally, sports injury risk assessment is carried out. The built system takes the embedded esp8266wifi module as the hardware processing core and uses body temperature sensor, blood pressure sensor, EMG sensor, and pulse sensor to form wearable devices. By wearing wearable devices, four human physiological parameters such as body temperature, blood pressure, electromyography, and pulse can be collected. In the process of decision level fusion, different weights are set for the focal elements causing information conflict, and the optimized D‐S evidence theory algorithm is used. Thus, according to the data detected by multisensor, the injury risk of user motion state is evaluated.

Human Falling Detection Algorithm Based on Multisensor Data Fusion with SVM

Falling is a common phenomenon in the life of the elderly, and it is also one of the 10 main causes of serious health injuries and death of the elderly. In order to prevent falling of the elderly, a real-time fall prediction system is installed on the wearable intelligent device, which can timely trigger the alarm and reduce the accidental injury caused by falls. At present, most algorithms based on single-sensor data cannot accurately describe the fall state, while the fall detection algorithm based on multisensor data integration can improve the sensitivity and specificity of prediction. In this study, we design a fall detection system based on multisensor data fusion and analyze the four stages of falls using the data of 100 volunteers simulating falls and daily activities. In this paper, data fusion method is used to extract three characteristic parameters representing human body acceleration and posture change, and the effectiveness of the multisensor data fusion algorithm is verified. The sensitivity is 96.67%, and the specificity is 97%. It is found that the recognition rate is the highest when the training set contains the largest number of samples in the training set. Therefore, after training the model based on a large amount of effective data, its recognition ability can be improved, and the prevention of fall possibility will gradually increase. In order to compare the applicability of random forest and support vector machine (SVM) in the development of wearable intelligent devices, two fall posture recognition models were established, respectively, and the training time and recognition time of the models are compared. The results show that SVM is more suitable for the development of wearable intelligent devices.

Practical Moving Target Detection in Maritime Environments Using Fuzzy Multi-sensor Data Fusion

As autonomous ships become the future trend for maritime transportation, it is of importance to develop intelligent autonomous navigation systems to ensure the navigation safety of ships. Among the three core components (sensing, planning and control modules) of the system, an accurate detection of target ships’ navigation information is critical. Within a typical maritime environment, the existence of sensor noises as well as the influences generated by varying environment conditions largely limit the reliability of using a single sensor for environment awareness. It is therefore vital to use multiple sensors together with a multi-sensor data fusion technology to improve the detection performance. In this paper, a fuzzy logic-based multi-sensor data fusion algorithm for moving target ships detection has been proposed and designed using both AIS and radar information. A two-stage fuzzy logic association method has been particularly developed and integrated with Kalman filtering to achieve a computationally efficient performance. The effectiveness of the proposed algorithm has been tested and validated in simulations where multiple target ships are transiting with complex movements.

Geological disaster monitoring and early warning system based on big data analysis

The existing geological disaster monitoring and early warning system has the problems of large monitoring errors and low accuracy. This paper designs a geological disaster monitoring and early warning system based on big data analysis. The geological disaster monitoring network module monitors the geological deformation and disaster inducing factors in real time through sensors and transmits them to the large-capacity data remote wireless transmission network module in the multi-dimensional heterogeneous monitoring data integration module through the Beidou satellite communication technology. This module uses a multi-sensor data fusion algorithm based on the D-S evidence theory and fuzzy mathematics to realize the integration of geological multi-dimensional heterogeneous monitoring data. Then, it uses cloud computing-based geological abnormal data mining method to mine abnormal geological information and transmit it to the early warning information release module. Geological disaster warning information will be released in the form of text messages and emails. Through verification, the system has high accuracy in monitoring and early warning of different types of geological disasters and provides a basis for timely and scientific deployment of geological disasters.

A New Approach of Minimizing Midas Touch Problem for a Tracer-Free Tongue-Controlled Assistive Technology

Tongue-controlled assistive technologies (ATs) have a great potential to improve the quality of life for people with severe disabilities. Current tongue-controlled ATs, however, require either a tracer to be attached on the tongue or a complicated gesture/mechanism to avoid issuing commands unintendedly during routine intraoral events (e.g., tongue resting, swallowing, and speaking), which poses a high entry barrier to potential users. In this study, we propose a tracer-free tongue-controlled AT using four optical distance sensors and one ambient light sensor with a multi-sensor data fusion algorithm to distinguish the intended tongue contacts from the unintended ones. Eleven healthy human subjects participated in the study to evaluate the performance using the proposed technology in masking unintendedly-issued commands and navigating a mouse cursor on a computer. The proposed multi-sensor data fusion algorithm could mask 100%, 86.99%, and 99.50% of unintendedly-issued commands during tongue resting, swallowing, and speaking, respectively. The efficacy of the tongue commands in mouse navigation was evaluated by command-issuing task and maze-navigation task. The participants could issue tongue commands in a random order with 97.16±0.94% of accuracy. The completion time and the sum of deviation of the maze-navigation task were 31.36±2.74 s and 9.38±0.85 pixel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The subjects' performance on computer-access tasks, using the proposed tracer-free tongue-controlled technology, is at the level of the performance measured with existing tracer-based tongue-controlled technologies, while securing robustness in masking commands, unintendedly issued during routine intraoral events. Results of this preliminary study suggest that the tracer-free implementation is feasible option for the tongue-controlled ATs.

Brain-Computer Interface-Based Humanoid Control: A Review.

A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for those who have severe motor disabilities. Traditional BCI systems have been dependent only on brain signals recorded using Electroencephalography (EEG) and have used a rule-based translation algorithm to generate control commands. However, the recent use of multi-sensor data fusion and machine learning-based translation algorithms has improved the accuracy of such systems. This paper discusses various BCI applications such as tele-presence, grasping of objects, navigation, etc. that use multi-sensor fusion and machine learning to control a humanoid robot to perform a desired task. The paper also includes a review of the methods and system design used in the discussed applications.

From Trash to Cash: How Blockchain and Multi-Sensor-Driven Artificial Intelligence Can Transform Circular Economy of Plastic Waste?

Virgin polymers based on petrochemical feedstock are mainly preferred by most plastic goods manufacturers instead of recycled plastic feedstock. Major reason for this is the lack of reliable information about the quality, suitability, and availability of recycled plastics, which is partly due to lack of proper segregation techniques. In this paper, we present our ongoing efforts to segregate plastics based on its types and improve the reliability of information about recycled plastics using the first-of-its-kind blockchain smart contracts powered by multi-sensor data-fusion algorithms using artificial intelligence. We have demonstrated how different data-fusion modes can be employed to retrieve various physico-chemical parameters of plastic waste for accurate segregation. We have discussed how these smart tools help in efficiently segregating commingled plastics and can be reliably used in the circular economy of plastic. Using these tools, segregators, recyclers, and manufacturers can reliably share data, plan the supply chain, execute purchase orders, and hence, finally increase the use of recycled plastic feedstock.

A Comparative Assessment of Multisensor Data Merging and Fusion Algorithms for High-Resolution Surface Reflectance Data

The improvement of the spatial and temporal resolution of reflectance data products has been challenging due to the diversity of data sources and availability of many data merging and fusion algorithms. In the algorithmic domain, methods for data merging and fusion may include, but are not limited to, the modified quantile–quantile adjustment (MQQA), the Bayesian maximum entropy (BME), and the spatial and temporal adaptive reflectance fusion model (STARFM). This article presents a synergistic integration of the data merging and fusion algorithms of MQQA and BME in dealing with heterogeneous and nonstationary surface reflectance data at both the top of atmosphere (TOA) and land surface for a comparative study. Emphasis has been placed on the distinctive performance between BME and MQQA–BME algorithms in the spatial domain and the MQQA–BME and STARFM in the temporal domain at both TOA and land surface levels. The results indicate that the BME and MQQA–BME outperform the MQQA in terms of the spatial coverage at both TOA and land surface levels. Moreover, the MQQA–BME algorithm shows a higher prediction accuracy than STARFM at the blue band over the temporal domain at both TOA and land surface levels. The results of this comparison will greatly empower the MQQA–BME to be used for urban air quality monitoring and related epidemiological assessment in the future, once finer aerosol optical depth predictions via integrated data merging and fusion can be made possible.

UAV Positioning Based on Multi-Sensor Fusion

Real-time and stable positioning data is essential for the UAV to perform various tasks. The traditional multi-sensor data fusion algorithm needs to know the measurement noise of sensor data, and even if there are corresponding adaptive methods to estimate the noise, most methods cannot deal with time-varying noise. In addition, traditional fusion algorithms usually are complicated, causing a large amount of calculation. In this paper, a multi-sliding window classification adaptive unscented Kalman filter (MWCAUKF) method with timestamp sort updating was proposed, which can improve the accuracy and stability of positioning. This method consists of three phases. First, according to the timestamp of sensor data, the multi-sensor data are added with fusion filtering in order. Then it estimates the measurement noise of multiple sensors through multiple sliding Windows. Finally, the sensor data classification method is adopted to deal with the filter instability caused by time-varying noise. Both theoretical analysis and experimental results show that this method has a low computational cost, high accuracy, and good stability.

A Method Based on Multi-Sensor Data Fusion for UAV Safety Distance Diagnosis

With the increasing application of unmanned aerial vehicles (UAVs) to the inspection of high-voltage overhead transmission lines, the study of the safety distance between drones and wires has received extensive attention. The determination of the safety distance between the UAV and the transmission line is of great significance to improve the reliability of the inspection operation and ensure the safe and stable operation of the power grid and inspection equipment. Since there is no quantitative data support for the safety distance of overhead transmission lines in UAV patrol, it is impossible to provide accurate navigation information for UAV safe obstacle avoidance. This paper proposes a mathematical model based on a multi-sensor data fusion algorithm. The safety distance of the line drone is diagnosed. In these tasks, firstly, the physical model of the UAV in the complex electromagnetic field is established to determine the influence law of the UAV on the electric field distortion and analyze the maximum electric and magnetic field strength that the UAV can withstand. Then, based on the main factors affecting the UAV such as the maximum wind speed, inspection speed, positioning error, and the size of the drone, the adaptive weighted fusion algorithm is used to perform first-level data fusion on the homogeneous sensor data. Then, based on the improved evidence, the theory performs secondary fusion on the combined heterogeneous sensor data. According to the final processing result and the type of proposition set, we diagnose the current safety status of the drone to achieve an adaptive adjustment of the safety distance threshold. Lastly, actual measurement data is used to verify the mathematical model. The experimental results show that the mathematical model can accurately identify the safety status of the drone and adaptively adjust the safety distance according to the diagnosis result and surrounding environment information.

Fault diagnosis of rotating machinery equipped with multiple sensors using space-time fragments

The vibration signals captured by multiple sensors can be fused and provide rich information to distinguish faults of rotating machinery. However, previous studies mostly regard multiple signals as individual signals and ignore the coupling relationship between signals resulting in a loss of information. To overcome the above problem, this paper proposes a new multi-sensor data fusion algorithm for identifying faults. First, space-time fragments are constructed to combine multiple signals together considering the space and time relationship between signals. Second, histograms of multi-channel shaft orbit based on space-time fragments are extracted to describe faults. Third, K-nearest neighbor is selected as the classification method. The experiments are carried out on a rig of rotating machinery supported by active magnetic bearings and demonstrate the effectiveness of our proposed algorithm.

An Integrated Adaptive Kalman Filter for High-Speed UAVs

In order to solve the problems of filtering divergence and low accuracy in Kalman filter (KF) applications in a high-speed unmanned aerial vehicle (UAV), this paper proposed a new method of integrated robust adaptive Kalman filter: strong adaptive Kalman filter (SAKF). The simulation of two high-dynamic conditions and a practical experiment were designed to verify the new multi-sensor data fusion algorithm. Then the performance of the Sage–Husa adaptive Kalman filter (SHAKF), strong tracking filter (STF), H∞ filter and SAKF were compared. The results of the simulation and practical experiments show that the SAKF can automatically select its filtering process under different conditions, according to an anomaly criterion. SAKF combines the advantages of SHAKF, H∞ filter and STF, and has the characteristics of high accuracy, robustness and good tracking skill. The research has proved that SAKF is more appropriate in high-speed UAV navigation than single filter algorithms.

A novel edge-enabled SLAM solution using projected depth image information

Environmental mapping is the key step for mobile robots to perform tasks independently and perfectly. In recent years, visual SLAM, laser-based SLAM and simultaneous localization and mapping (SLAM) have aroused the interest of many people. Unfortunately, those technologies are not widely used, limited by the computational complexity, data processing and very low and predictable latency. This paper had mainly completed the following work and edge-enabled computing-based edge computing (Shi and Dustdar in Computer 49(5):78–81, 2016) is used as a solution to accelerate calculation. First of all, this research design works with inertial unit mobile robotic navigation systems, and all sensors are connected in edge layers in the framework of edge computing and explore the accelerometer, electronic compass, and gyroscope data. The accelerometer data are integrated using the Kalman filter data fusion algorithm to filter the random drift error caused by the gyroscope and the electronic compass. The state of the machine is determined by calculation of the corresponding attitude angle and position information. Second, a low-cost distance sensor is used to detect the depth and upload to the other fog node for computation. Next, the 3D point coordinate information is projected onto the two-dimensional coordinate extraction feature point to establish the feature map. Third, the extended Kalman filter SLAM is used to achieve simultaneous positioning and mapping. Finally, the method is validated in the experiment, proving that the method is feasible. The main improvement in this article is as follows: First, the multi-sensor data fusion algorithm is used to reduce the positioning error. Second, we use low-cost distance sensors to measure the depth of the model environment and reduce the cost. Third, we would take advantage of translating the three-dimensional depth information into a flat two-dimensional projection information to reduce the calculation of load and computing time. Fourth, our computation is distributed in different layers and focuses on edge-enabled platform to decrease the latency and redundancy.

Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics.

Aiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink node, and data preprocessing based on cubic exponential smoothing is performed at the gateway to eliminate abnormal data and noise data. In fuzzy theory, the range of membership functions is determined, according to this feature, the data fusion algorithm based on exponential trust degree is used to fuse the smooth data to avoid the absolute degree of mutual trust between data. In this paper, we have improved the crossover and mutation operations in the standard genetic algorithm, the variation is separated from the intersection, the chaotic sequence is used to determine the intersection, and the weakest single-point intersection is implemented to improve the convergence accuracy of the algorithm, weaken and avoid jitter problems during optimization. The chaotic sequence is used to mutate multiple genes in the chromosome to avoid premature algorithm maturity. Finally, the improved genetic algorithm is used to optimize the fusion estimation value. The experimental results show that the cubic exponential smoothing can significantly reduce the data fluctuation and improve the stability of the system. Compared with the commonly used data fusion algorithms such as arithmetic average method and adaptive weighting method, the data fusion algorithm based on trust degree and improved genetics has higher fusion precision. At the same time, the execution time of the algorithm is greatly reduced.

Using Wearable Sensors to Capture Posture of the Human Lumbar Spine in Competitive Swimming

Motion capture based on wearable inertial sensors is a promising technique for swimmers' training. To apply motion capture techniques properly, a swimming motion evaluation method based on inertial motion capture technology is proposed. Our proposed method uses a multisensor data fusion algorithm for swimmers’ attitude estimation, and the swimming posture is reconstructed in combination with a human biomechanical model. Furthermore, a comparative experiment between our proposed motion capture system and the NDI motion tracking system shows that our system performs reliably and accurately, and the estimation errors are well controlled. In addition, the accuracy of the orientation estimation algorithm ranges from $\text{1.65}^{\circ }$ to $\text{3.66}^{\circ }$ . The system can capture swimmers’ lumbar spine in four competitive swimming styles. A kinematic analysis of lumbar spine movement indicates that the patterns of swimmers’ lumbar spine movement can be used to evaluate training performance and provide quantitative data for swimmers.

A new IMMU-based data glove for hand motion capture with optimized sensor layout

The number of people with hand disabilities caused by stroke is increasing every year. Developing a low-cost and easy-to-use data glove to capture the human hand motion can be used to assess the patient’s hand ability in home environment. While a majority of existing hand motion capture methods are too complex to be used for patients in residential settings. This paper proposes a new sensor layout strategy using the inertial and magnetic measurement units and designs a multi-sensor Kalman data fusion algorithm. The sensor layout strategy is optimized according to the inverse kinematics and the developed hand model, and the number of sensors can be significantly reduced from 12 in conventional systems to 6 in our system with the hand motion being completely and accurately reconstructed. Hand motion capture experiments were conducted on a healthy subject using the developed data glove. The hand motion can be restored completely and the hand gesture can be recognized with an accuracy of 85%. The results of a continuous hand movement indicate an average error under 15% compared with the common glove with full sensors. This new set with optimized sensor layout is promising for lower-cost and residential medical applications.

Asynchronous Multisonar Data Integration Approach Used to Detect Sparse Targets

Due to the advantage of the distributed multisensor detection system which makes sonar detect further and more accurate estimation of the target state, distributed multisensor data fusion algorithms are widely applied to the sonar detection system. However, on the one hand, the most asynchronous algorithms focus on how to convert asynchronous data fusion into synchronous data fusion. On the other hand, sonar detection system suffers from more serious asynchronous data problems (such as more serious random delay and packet loss defaults) than radar and other fields. Therefore, the traditional asynchronous fusion method has some limitations. When the targets are sparse, this paper proposed a novel asynchronous multisonar data integration approach, in which the Gaussian mixture probability hypothesis density (GMPHD) filter is used to filter clutter for local sonar sensor. Then, the Gaussian mixture model (GMM) algorithm is used to model asynchronous data over a period of time. Finally, all local sonar detection data are integrated into a surveillance region image to help to detect the target. Several simulation tests and a sea test are presented in this paper to test this approach performance.