A novel hybrid fusion algorithm to bridge the period of GPS outages using low-cost INS

2014 ◽  
Vol 41 (5) ◽  
pp. 2166-2173 ◽  
Author(s):  
Deepak Bhatt ◽  
Priyanka Aggarwal ◽  
Vijay Devabhaktuni ◽  
Prabir Bhattacharya
Keyword(s):  
Low Cost ◽  
Fusion Algorithm ◽  
Hybrid Fusion

scholarly journals Development and Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2480
Author(s):  
Isidoro Ruiz-García ◽  
Ismael Navarro-Marchal ◽  
Javier Ocaña-Wilhelmi ◽  
Alberto J. Palma ◽  
Pablo J. Gómez-López ◽  
...  
Keyword(s):  
Data Fusion ◽  
Reference System ◽  
Inertial Sensor ◽  
Low Cost ◽  
Sampling Rate ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Improve Technique ◽  
Worst Case ◽  
Sensor Data Fusion

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

Sensor Data Fusion Algorithm for Indoor Environment Mapping Using Low-Cost Sensors

2013 ◽  
Vol 24 (3) ◽  
pp. 199-211 ◽  
Author(s):  
Luciano Buonocore ◽  
Cairo Lúcio Nascimento Júnior ◽  
Areolino de Almeida Neto
Keyword(s):  
Data Fusion ◽  
Indoor Environment ◽  
Low Cost ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Sensor Data Fusion

scholarly journals STATISTIC TESTS AIDED MULTI-SOURCE DEM FUSION

2016 ◽  
Vol XLI-B6 ◽  
pp. 227-233
Author(s):  
C. Y. Fu ◽  
J. R. Tsay
Keyword(s):  
Land Surface ◽  
Type I Error ◽  
Low Cost ◽  
Type I ◽  
Fusion Algorithm ◽  
Type I Errors ◽  
Production Methods ◽  
Grid Points ◽  
The Cost ◽  
Proper Setting

Since the land surface has been changing naturally or manually, DEMs have to be updated continually to satisfy applications using the latest DEM at present. However, the cost of wide-area DEM production is too high. DEMs, which cover the same area but have different quality, grid sizes, generation time or production methods, are called as multi-source DEMs. It provides a solution to fuse multi-source DEMs for low cost DEM updating. The coverage of DEM has to be classified according to slope and visibility in advance, because the precisions of DEM grid points in different areas with different slopes and visibilities are not the same. Next, difference DEM (dDEM) is computed by subtracting two DEMs. It is assumed that dDEM, which only contains random error, obeys normal distribution. Therefore, student test is implemented for blunder detection and three kinds of rejected grid points are generated. First kind of rejected grid points is blunder points and has to be eliminated. Another one is the ones in change areas, where the latest data are regarded as their fusion result. Moreover, the DEM grid points of type I error are correct data and have to be reserved for fusion. The experiment result shows that using DEMs with terrain classification can obtain better blunder detection result. A proper setting of significant levels (α) can detect real blunders without creating too many type I errors. Weighting averaging is chosen as DEM fusion algorithm. The priori precisions estimated by our national DEM production guideline are applied to define weights. Fisher’s test is implemented to prove that the priori precisions correspond to the RMSEs of blunder detection result.

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

2021 ◽  
Vol 297 ◽  
pp. 01040
Author(s):  
Aziz El Fatimi ◽  
Adnane Addaim ◽  
Zouhair Guennoun
Keyword(s):  
Experimental Study ◽  
Data Fusion ◽  
Low Cost ◽  
Three Dimensional ◽  
Measurement Unit ◽  
Circular Path ◽  
Positioning System ◽  
Fusion Algorithm ◽  
Position Accuracy ◽  
The Stability

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.

scholarly journals A Robust GNSS/PDR Integration Scheme with GRU-Based Zero-Velocity Detection for Mass-Pedestrians

2022 ◽  
Vol 14 (2) ◽  
pp. 300
Author(s):  
Dongpeng Xie ◽  
Jinguang Jiang ◽  
Jiaji Wu ◽  
Peihui Yan ◽  
Yanan Tang ◽  
...  
Keyword(s):  
Low Cost ◽  
Single Point ◽  
Dead Reckoning ◽  
Satellite System ◽  
Integration Scheme ◽  
Fusion Algorithm ◽  
Zero Velocity ◽  
Single Antenna ◽  
Other Information ◽  
Global Navigation Satellite

Aiming at the problem of high-precision positioning of mass-pedestrians with low-cost sensors, a robust single-antenna Global Navigation Satellite System (GNSS)/Pedestrian Dead Reckoning (PDR) integration scheme is proposed with Gate Recurrent Unit (GRU)-based zero-velocity detector. Based on the foot-mounted pedestrian navigation system, the error state extended Kalman filter (EKF) framework is used to fuse GNSS position, zero-velocity state, barometer elevation, and other information. The main algorithms include improved carrier phase smoothing pseudo-range GNSS single-point positioning, GRU-based zero-velocity detection, and adaptive fusion algorithm of GNSS and PDR. Finally, the scheme was tested. The root mean square error (RMSE) of the horizontal error in the open and complex environments is lower than 1 m and 1.5 m respectively. In the indoor elevation experiment where the elevation difference of upstairs and downstairs exceeds 25 m, the elevation error is lower than 1 m. This result can provide technical reference for the accurate and continuous acquisition of public pedestrian location information.

scholarly journals Attitude Determination Method by Fusing Single Antenna GPS and Low Cost MEMS Sensors Using Intelligent Kalman Filter Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Lei Wang ◽  
Bo Song ◽  
Xueshuai Han ◽  
Yongping Hao
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Fuzzy Controller ◽  
Low Cost ◽  
Transition Process ◽  
Measurement Unit ◽  
Accurate Estimation ◽  
Mems Sensors ◽  
Fusion Algorithm ◽  
Single Antenna

For meeting the demands of cost and size for micronavigation system, a combined attitude determination approach with sensor fusion algorithm and intelligent Kalman filter (IKF) on low cost Micro-Electro-Mechanical System (MEMS) gyroscope, accelerometer, and magnetometer and single antenna Global Positioning System (GPS) is proposed. The effective calibration method is performed to compensate the effect of errors in low cost MEMS Inertial Measurement Unit (IMU). The different control strategies fusing the MEMS multisensors are designed. The yaw angle fusing gyroscope, accelerometer, and magnetometer algorithm is estimated accurately under GPS failure and unavailable sideslip situations. For resolving robust control and characters of the uncertain noise statistics influence, the high gain scale of IKF is adjusted by fuzzy controller in the transition process and steady state to achieve faster convergence and accurate estimation. The experiments comparing different MEMS sensors and fusion algorithms are implemented to verify the validity of the proposed approach.

Sign in / Sign up

Export Citation Format

Share Document