Kalman Filter Data Fusion Research Articles

Research on the Innovation of Physical Education and Physical Education Teaching Based on Big Data Analysis

Abstract In this paper, Openpose is used to process the sports teaching video to get the coordinates data of the human body joint point positions in each frame of the video, and Kalman filter data fusion is used to establish the human skeleton model. According to the results of the division of the five major parts of the human body, after establishing the limb vectors of the human body’s torso, left arm, right arm, left leg, and right leg in three-dimensional spatial coordinates, the distances between the joints of the five human body skeletons based on the DTW posture matching algorithm were used to extract the characteristics of the sports error technical movements. From the demand of sports digital teaching, the design and implementation of sports basic movement teaching evaluation system based on the DTW posture matching algorithm, and the research and analysis of sports teaching under the background of big data. The results show that the IoU values of batting action localization in 6 segments of physical education teaching are 85.6%, 91.6%, 77.7%, 75.1%, 87.4% and 77.7%, respectively, and the average reach 82.5%, i.e., it shows that the research on action localization and recognition based on the DTW posture matching algorithm has a good performance. In the assessment of movement standardization in physical education, the maximum moment of stretching angle corresponds to the moment of hitting the ball, and its value reaches 3.79, i.e., it reflects that the evaluation system of physical education basic movement teaching can accurately determine whether the students’ movements are accurate or not, and make timely feedbacks to carry out the corrections of physical education movements. This study has the potential to enhance students’ interest and performance in sports and contribute to the advancement of digital sports teaching.

A Water Leakage Risk Assessment Model for Shield Tunnel Based on Kalman Filter Data Fusion Method

Multi-source information monitoring and data fusion analysis system has high practical value for the risk management of tunnel structure, especially in complex ground. The analysis of structure health will be getting more comprehensive and real-time with multisource monitoring technologies. Risk warning will be more accurate and timelier. Based on one shield tunnel project, this research analysed the water leakage risk of shield subsea tunnel in unfavourable geological conditions by using the method of geological information and structural monitoring data fusion. Firstly, Kalman filter method was used to denoise the monitoring data, and the effect of different monitoring data was compared; Then, the risk factor of water leakage was analysed, and the relationship between risk factors, geological information and monitoring data was established; Finally, the risk factors based on multi-source monitoring information were fused and analysed by using the improved D-S evidence theory method, and the final analysis results of water leakage risk were obtained. A multisource dynamic risk analysis model of tunnel water leakage was established. The results showed that the Kalman filter method has better noise reduction effect on the high-frequency monitoring data than that of low-frequency monitoring data, and the multi-source dynamic risk analysis model has a good prediction effect on the water leakage risk of tunnel lining in unfavourable ground condition.

A low-cost IMU/GPS position accuracy experimental study using extended kalman filter data fusion in real environments

In a three-dimensional environment, the navigation of a vehicle in airspace, terrestrial space, or maritime space presents complex aspects concerning the determination of its position, its orientation, and the stability of the processing of the asynchronous data coming from the various sensors during navigation. In this context, this paper presents an experimental analysis of the position accuracy estimated by a low-cost inertial measurement unit coupled, by the extended Kalman data fusion algorithm, with a system of absolute measurements of a positioning system received from a GPS which designates the global positioning system. The different scenarios of the experimental study carried out during this work concerned three tests in a real environment, such as the navigation in a course inside the city of Rabat/Morocco with a moderate speed, a section on the highway at a speed of 120 Km/h and a circular path around a roundabout. The experimental results proved that the low-cost sensors studied are a good candidate for civil navigation applications.

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.

Parameter identification of a nonlinear model: replicating the motion response of an autonomous underwater vehicle for dynamic environments

This study presents a system identification algorithm to determine the linear and nonlinear parameters of an autonomous underwater vehicle (AUV) motion response prediction mathematical model, utilising the recursive least squares optimisation method. The key objective of the model, which relies solely on propeller thrust, gyro measurements and parameters representing the vehicle hydrodynamic, hydrostatic and mass properties, is to calculate the linear velocities of the AUV in the x, y and z directions. Initially, a baseline mathematical model that represents the dynamics of a Gavia class AUV in a calm water environment was developed. Using a novel technique developed in this study, the parameters within the baseline model were calibrated to provide the motion response in different environmental conditions by conducting a calibration mission in the new environment. The accuracy of the velocity measurements from the calibrated model was substantially greater than those from the baseline model for the tested scenarios with a minimum velocity prediction improvement of 50%. The determined velocities will be used to aid the inertial navigation system (INS) position estimate using a Kalman filter data fusion algorithm when external aiding is unavailable. When an INS is not externally aided or constrained by a mathematical model such as that presented here, the positioning uncertainty can be more than 4% of the distance travelled (assuming a forward speed of 1.6 m s $$^{-1})$$ . The calibrated model is able to compute the position of the AUV within an uncertainty range of around 1.5% of the distance travelled, significantly improving the localisation accuracy.

A fault tolerant architecture for data fusion: A real application of Kalman filters for mobile robot localization

Multisensor perception has an important role in robotics and autonomous systems, providing inputs for critical functions including obstacle detection and localization. It is starting to appear in critical applications such as drones and ADASs (Advanced Driver Assistance Systems). However, this kind of complex system is difficult to validate comprehensively. In this paper we look at multisensor perception systems in relation to an alternative dependability method, namely fault tolerance. We propose an approach for tolerating faults in multisensor data fusion that is based on the more traditional method of duplication–comparison, and that offers detection and recovery services. We detail an example implementation using Kalman filter data fusion for mobile robot localization. We demonstrate its effectiveness in this case study using real data and fault injection.

Preliminary laboratory test on navigation accuracy of an autonomous robot for measuring air quality in livestock buildings

Air quality in many poultry buildings is less than desirable. However, the measurement of concentrations of airborne pollutants in livestock buildings is generally quite difficult. To counter this, the development of an autonomous robot that could collect key environmental data continuously in livestock buildings was initiated. This research presents a specific part of the larger study that focused on the preliminary laboratory test for evaluating the navigation precision of the robot being developed under the different ground surface conditions and different localization algorithm according internal sensors. The construction of the robot was such that each wheel of the robot was driven by an independent DC motor with four odometers fixed on each motor. The inertial measurement unit (IMU) was rigidly fixed on the robot vehicle platform. The research focused on using the internal sensors to calculate the robot position (x, y, θ) through three different methods. The first method relied only on odometer dead reckoning (ODR), the second method was the combination of odometer and gyroscope data dead reckoning (OGDR) and the last method was based on Kalman filter data fusion algorithm (KFDF). A series of tests were completed to generate the robot’s trajectory and analyse the localisation accuracy. These tests were conducted on different types of surfaces and path profiles. The results proved that the ODR calculation of the position of the robot is inaccurate due to the cumulative errors and the large deviation of the heading angle estimate. However, improved use of the gyroscope data of the IMU sensor improved the accuracy of the robot heading angle estimate. The KFDF calculation resulted in a better heading angle estimate than the ODR or OGDR calculations. The ground type was also found to be an influencing factor of localisation errors. Keywords: autonomous robot, air quality, navigation, Kalman filter data fusion, livestock building, robot localization DOI: 10.3965/j.ijabe.20160902.1189 Citation: Qi H X, Banhazi T M, Zhang Z G, Low T, Brookshaw I J. Preliminary laboratory test on navigation accuracy of an autonomous robot for measuring air quality in livestock buildings. Int J Agric & Biol Eng, 2016; 9(2): 29－39.

Based on Multi-Sensor Data Fusion Operating Machinery Velocity Monitoring System

Against the shortcomings such as high price and low precision , big error leading with land wheel exist shift phenomenon in traditional tachometry, raised that the idea of using multisensor data fusion handles operating machinery speed in the field, introduced a fusion progress system structure and applied the kalman filter data fusion algorithm.This system which is based on ATS665 and GPS and mcu MSP430F1121 can work in operating machinery speed measurement and in car navigation and positioning system .The above_mentioned determines the high reliability,high cost perfomance and using easily.

Interoperability versus Integration of Omega and GPS

This paper was presented at the 14th annual meeting of the International Omega Association, held at Long Beach, California in October 1989.The integration of Omega and GPS sensors into a single navigation system offers the advantages of good accuracy under almost all signal conditions, low capital investment, and certifiable worldwide navigation. The accuracy of the existing Omega network can be progressively improved as GPS satellite coverage is fully implemented. This is done by having the GPS system update the Omega navigator. Eventually, the same equipment can provide full GPS navigation accuracy but with Omega as a back-up.This paper proposes a method of further improving the overall accuracy and reliability of Omega-GPS navigation. The conceptsof Omega-GPS integration, interoperability, modes of operation, and Kalman filter data fusion are presented. Four interoperability modes of operation and their ability to improve navigation reliability are also discussed.