Disparity estimation for multi-scale multi-sensor fusion

The perception module of advanced driver assistance systems plays a vital role.Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer.This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme.A binocular stereo vision sensor composed of two cameras and a light deterction and ranging(LiDAR)sensor is used to jointly perceive the environment,and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map.This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors.Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.

Research on Transmission Line Tower Tilting and Foundation State Monitoring Technology Based onMulti-Sensor Cooperative Detection and Correction

The transmission line tower will be affected by bad weather and artificial subsidence caused by the foundation and other factors in the power transmission.The tower’s tilt and severe deformation will cause the building to collapse.Many small changes caused the tower’s collapse,but the early staff often could not intuitively notice the changes in the tower’s state.In the current tower online monitoring system,terminal equipment often needs to replace batteries frequently due to premature exhaustion of power.According to the need for real-time measurement of power line tower,this research designed a real-time monitoring device monitoring the transmission tower attitude tilting and foundation state based on the inertial sensor,the acceleration of 3 axis inertial sensor and angular velocity raw data to pole average filtering pre-processing,and then through the complementary filtering algorithm for comprehensive calculation of tilt angle,the system meets the demand for inclined online monitoring of power line poles and towers regarding measurement accuracy,with low cost and power consumption.The optimization multi-sensor cooperative detection and correction measured tilt angle result relative accuracy can reach 1.03%,which has specific promotion and application value since the system has the advantages of unattended and efficient calculation.

A multi-sensor-based distributed real-time measurement system for glacier deformation

Glacier disasters occur frequently in alpine regions around the world,but the current conventional geological disaster measurement technology cannot be directly used for glacier disaster measurement.Hence,in this study,a distributed multi-sensor measurement system for glacier deformation was established by integrating piezoelectric sensing,coded sensing,attitude sensing technology and wireless communication technology.The traditional Modbus protocol was optimized to solve the problem of data identification confusion of different acquisition nodes.Through indoor wireless transmission,adaptive performance analysis,error measurement experiment and landslide simulation experiment,the performance of the measurement system was analyzed and evaluated.Using unmanned aerial vehicle technology,the reliability and effectiveness of the measurement system were verified on the site of Galongla glacier in southeastern Tibet,China.The results show that the mean absolute percentage errors were only 1.13%and 2.09%for the displacement and temperature,respectively.The distributed glacier deformation real-time measurement system provides a new means for the assessment of the development process of glacier disasters and disaster prevention and mitigation.

Mission scheduling of multi-sensor collaborative observation for space surveillance network

With increased dependence on space assets,scheduling and tasking of the space surveillance network(SSN)are vitally important.The multi-sensor collaborative observation scheduling(MCOS)problem is a multi-constraint and high-conflict complex combinatorial optimization problem that is nondeterministic polynomial(NP)-hard.This research establishes a sub-time window constraint satisfaction problem(STWCSP)model with the objective of maximizing observation profit.Considering the significant effect of genetic algorithms(GA)on solving the problem of resource allocation,an evolution heuristic(EH)algorithm containing three strategies that focus on the MCOS problem is proposed.For each case,a task scheduling sequence is first obtained via an improved GA with penalty(GAPE)algorithm,and then a mission planning algorithm(heuristic rule)is used to determine the specific observation time.Compared to the model without sub-time windows and some other algorithms,a series of experiments illustrate the STWCSP model has better performance in terms of total profit.Experiments about strategy and parameter sensitivity validate its excellent performance in terms of EH algorithms.

Progress and Achievements of Multi-sensor Fusion Navigation in China during 2019—2023

Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and other aspects.However,in environments with limited satellite signals such as urban canyons,tunnels,and indoor spaces,it is difficult to provide accurate and reliable positioning services only by satellite navigation.Multi-source sensor integrated navigation can effectively overcome the limitations of single-sensor navigation through the fusion of different types of sensor data such as Inertial Measurement Unit(IMU),vision sensor,and LiDAR,and provide more accurate,stable and robust navigation information in complex environments.We summarizes the research status of multi-source sensor integrated navigation technology,and focuses on the representative innovations and applications of integrated navigation and positioning technology by major domestic scientific research institutions in China during 2019—2023.

Research on Optimal Preload Method of Controllable Rolling Bearing Based on Multisensor Fusion

Angular contact ball bearings have been widely used in machine tool spindles,and the bearing preload plays an important role in the performance of the spindle.In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties,a roller bearing preload test method based on the improved D-S evidence theorymulti-sensor fusion method was proposed.First,a novel controllable preload system is proposed and evaluated.Subsequently,multiple sensors are employed to collect data on the bearing parameters during preload application.Finally,a multisensor fusion algorithm is used to make predictions,and a neural network is used to optimize the fitting of the preload data.The limitations of conventional preload testing methods are identified,and the integration of complementary information frommultiple sensors is used to achieve accurate predictions,offering valuable insights into the optimal preload force.Experimental results demonstrate that the multi-sensor fusion approach outperforms traditional methods in accurately measuring the optimal preload for rolling bearings.

A Novel Multi-sensor Data Fusion Algorithm and Its Application to Diagnostics

To Meet the requirements of multi-sensor data fusion in diagnosis for complex equipment systems,a novel, fuzzy similarity-based data fusion algorithm is given. Based on fuzzy set theory, it calculates the fuzzy similarity among a certain sensor's measurement values and the multiple sensor's objective prediction values to determine the importance weigh of each sensor,and realizes the multi-sensor diagnosis parameter data fusion.According to the principle, its application software is also designed. The applied example proves that the algorithm can give priority to the high-stability and high -reliability sensors and it is laconic ,feasible and efficient to real-time circumstance measure and data processing in engine diagnosis.

Multi-sensor measurement and data fusion technology for manufacturing process monitoring:a literature review

Due to the rapid development of precision manufacturing technology,much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities in terms of measurement accuracy and information richness,thereby improving the efficiency and precision of manufacturing.In a multisensor system,each sensor independently measures certain parameters.Then,the system uses a relevant signalprocessing algorithm to combine all of the independent measurements into a comprehensive set of measurement results.The purpose of this paper is to describe multisensor measurement and data fusion technology and its applications in precision monitoring systems.The architecture of multisensor measurement systems is reviewed,and some implementations in manufacturing systems are presented.In addition to the multisensor measurement system,related data fusion methods and algorithms are summarized.Further perspectives on multisensor monitoring and data fusion technology are included at the end of this paper.

Design and construction of a multi-sensor position monitoring system applied to key components of synchrotron sources

High-accuracy position monitoring of key components is required for modern synchrotron sources,such as free-electron lasers and diffraction-limited storage rings.Although various position monitoring sensors have been adopted to monitor the displacement of key components in each direction in real time,these monitoring systems are usually based on their own coordinate system.Data from such systems are meaningful when evaluating and examining the data from each positioning monitoring system in a unified coordinate system.This paper presents the design and construction of a multi-sensor position monitoring system(MPMS).A hydrostatic levelling system,a wire position sensor(WPS) and a tiltmeter are fixed to a stainless steel plate that has been calibrated by a coordinate-measurement machine.Several plates form the MPMS.The system must compensate for the sag of the stretched wires so that the WPSs create a straight line.The method of the coordinate transformation from the sensor coordinate system to the MPMS coordinate system was thoroughly studied.An experimental MPMS that includes five plates was setup in a 20-m tunnel,and a validation study to verify fully the feasibility of the MPMS was performed.

Monitoring Land-Use Change in Nakuru (Kenya) Using Multi-Sensor Satellite Data

Recently land-use change has been the main concern for worldwide environment change and is being used by city and regional planners to design sustainable cities. Nakuru in the central Rift Valley of Kenya has undergone rapid urban growth in last decade. This paper focused on urban growth using multi-sensor satellite imageries and explored the potential benefits of combining data from optical sensors (Landsat, Worldview-2) with Radar sensor data from Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data for urban land-use mapping. Landsat has sufficient spectral bands allowing for better delineation of urban green and impervious surface, Worldview-2 has a higher spatial resolution and facilitates urban growth mapping while PALSAR has higher temporal resolution compared to other operational sensors and has the capability of penetrating clouds irrespective of weather conditions and time of day, a condition prevalent in Nakuru, because it lies in a tropical area. Several classical and modern classifiers namely maximum likelihood (ML) and support vector machine (SVM) were applied for image classification and their performance assessed. The land-use data of the years 1986, 2000 and 2010 were compiled and analyzed using post classification comparison (PCC). The value of combining multi-temporal Landsat imagery and PALSAR was explored and achieved in this research. Our research illustrated that SVM algorithm yielded better results compared to ML. The integration of Landsat and ALOS PALSAR gave good results compared to when ALOS PAL- SAR was classified alone. 19.70 km2 of land changed to urban land-use from non-urban land-use between the years 2000 to 2010 indicating rapid urban growth has taken place. Land-use information is useful for the comprehensive land-use planning and an integrated management of resources to ensure sustainability of land and to achieve social Eq- uity, economic efficiency and environmental sustainability.

A Study of Multi-sensor Data Fusion System Based on MAS for Nutrient Solution Measurement

For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system under greenhouse environment,sensors should have intelligent properties including self-calibration and self-compensation. Meanwhile,it is necessary for multiple sensors to cooperate and interact for enhancing reliability of multi-sensor system. Because of the properties of multi-agent system (MAS),it is an appropriate tool to study MSDF system.This paper proposed an architecture of MSDF system based on MAS for the multiple components measurement of nutrient solution.The sensor agent's structure and function modules are analyzed and described in detail,the formal definitions are given,too.The relations of the sensors are modeled to implement reliability diagnosis of the multi-sensor system,so that the reliability of nutrient control system is enhanced.This study offers an effective approach for the study of MSDF.

RESEARCH ON THE ACCURACY OF TRACKING LONG RANGE AIRPLANE BY MULTI-SENSOR

This paper mainly studies the influence of the relative position of target-sensors on the tracking accuracy of long range airplane. From theory analysis and simulation results, it is found that the tracking accuracy of long-range airplane can be improved greatly if the extant sensors are rationally placed and multi-sensor data fusion technique is used in the case of

Multi-sensor Data Fusion by Improved Hough Transformation

In this paper we present an evidence-gathering approach to slove the multi-sensor data fusion problem. It uses an improved Hough transformation method rather than the usual statistical or geometric approach to extract the directions and positions of the walls in a room and update the location (orientation and position)of a mobile robot. The simulation results show that the proposed method is of practical importance since it is very simple and easy to implement.

Inversion of Evaporation and Water Vapor Transport Using HY-2 Multi-Sensor Data

HY-2 satellite is the first marine dynamic environment satellite of China.In this study,global evaporation and water vapor transport of the global sea surface are calculated on the basis of HY-2 multi-sensor data from April 1 to 30,2014.The algorithm of evaporation and water vapor transport is discussed in detail,and results are compared with other reanalysis data.The sea surface temperature of HY-2 is in good agreement with the ARGO buoy data.Two clusters are shown in the scatter plot of HY-2 and OAFlux evaporation due to the uneven global distribution of evaporation.To improve the calculation accuracy,we compared the different parameterization schemes and adopted the method of calibrating HY-2 precipitation data by SSM/I and Global Precipitation Climatology Project(GPCP)data.In calculating the water vapor transport,the adjustment scheme is proposed to match the balance of the water cycle for data in the low latitudes.

Data Cleaning Based on Stacked Denoising Autoencoders and Multi-Sensor Collaborations

Wireless sensor networks are increasingly used in sensitive event monitoring.However,various abnormal data generated by sensors greatly decrease the accuracy of the event detection.Although many methods have been proposed to deal with the abnormal data,they generally detect and/or repair all abnormal data without further differentiate.Actually,besides the abnormal data caused by events,it is well known that sensor nodes prone to generate abnormal data due to factors such as sensor hardware drawbacks and random effects of external sources.Dealing with all abnormal data without differentiate will result in false detection or missed detection of the events.In this paper,we propose a data cleaning approach based on Stacked Denoising Autoencoders(SDAE)and multi-sensor collaborations.We detect all abnormal data by SDAE,then differentiate the abnormal data by multi-sensor collaborations.The abnormal data caused by events are unchanged,while the abnormal data caused by other factors are repaired.Real data based simulations show the efficiency of the proposed approach.

Design of Multi-Sensors Cooperation Control System for Intelligent Building

This paper presents a design of intelligent building control system based on multi-sensors. In order to achieve comfort, low carbon and energy conservation for buildings. The design uses STC89C52 chip, combined with monitoring data of Pyroelectric infrared microwave dual sensor, temperature sensors and light sensors to control the optical system, the temperature control system and the security system. It helps to achieve low carbon and energy conservation. In addition, the energy-saving effect of a medium-sized multimedia classroom was analyzed. We analyze the feasibility of the system. It is proved that the system is low cost, high yield and easy to operate, with strong engineering application value.

Obstacle avoidance technology of bionic quadruped robot based on multi-sensor information fusion

In order to improve the ability of a bionic quadruped robot to percept the location of obstacles in a complex and dynamic environment,the information fusion between an ultrasonic sensor and a binocular sensor was studied under the condition that the robot moves in the Walk gait on a structured road.Firstly,the distance information of obstacles from these two sensors was separately processed by the Kalman filter algorithm,which largely reduced the noise interference.After that,we obtained two groups of estimated distance values from the robot to the obstacle and a variance of the estimation value.Additionally,a fusion of the estimation values and the variances was achieved based on the STF fusion algorithm.Finally,a simulation was performed to show that the curve of a real value was tracked well by that of the estimation value,which attributes to the effectiveness of the Kalman filter algorithm.In contrast to statistics before fusion,the fusion variance of the estimation value was sharply decreased.The precision of the position information is 4.6cm,which meets the application requirements of the robot.

An Indoor Pedestrian Localization Algorithm Based on Multi-Sensor Information Fusion

For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sensor fusion. The pedestrian’s localization in indoor environment is described as dynamic system state estimation problem. The algorithm combines the smart mobile terminal with indoor localization, and filters the result of localization with the particle filter. In this paper, a dynamic interval particle filter algorithm based on pedestrian dead reckoning (PDR) information and RSSI localization information have been used to improve the filtering precision and the stability. Moreover, the localization results will be uploaded to the server in time, and the location fingerprint database will be built incrementally, which can adapt the dynamic changes of the indoor environment. Experimental results show that the algorithm based on multi-sensor improves the localization accuracy and robustness compared with the location algorithm based on Wi-Fi.

Multi-Sensor Image Fusion: A Survey of the State of the Art

Image fusion has been developing into an important area of research. In remote sensing, the use of the same image sensor in different working modes, or different image sensors, can provide reinforcing or complementary information. Therefore, it is highly valuable to fuse outputs from multiple sensors (or the same sensor in different working modes) to improve the overall performance of the remote images, which are very useful for human visual perception and image processing task. Accordingly, in this paper, we first provide a comprehensive survey of the state of the art of multi-sensor image fusion methods in terms of three aspects: pixel-level fusion, feature-level fusion and decision-level fusion. An overview of existing fusion strategies is then introduced, after which the existing fusion quality measures are summarized. Finally, this review analyzes the development trends in fusion algorithms that may attract researchers to further explore the research in this field.

Multi-Sensor Ensemble Classifier for Activity Recognition

This paper presents a multi-sensor ensemble classifier (MSEC) for physical activity (PA) pattern recognition of human subjects. The MSEC, developed for a wearable multi-sensor integrate measurement system (IMS),combines multiple classifiers based on different sensor feature sets to improve the accuracy of PA type identification.Experimental evaluation of 56 subjects has shown that the MSECis more effectivein assessing activities of varying intensitiesthan the traditional homogeneous classifiers. It is able to correctly recognize 6 PA types with an accuracy of 93.50%, which is 7% higher than the non-ensemble support vector machine method. Furthermore, the MSECis effective in reducing the subject-to-subject variabilityin activity recognition.