scholarly journals Hybrid Urban Canyon Pedestrian Navigation Scheme Combined PDR, GNSS and Beacon Based on Smartphone

2019 ◽  
Vol 11 (18) ◽  
pp. 2174 ◽  
Author(s):  
Junhua Ye ◽  
Yaxin Li ◽  
Huan Luo ◽  
Jingxian Wang ◽  
Wu Chen ◽  
...  
Keyword(s):  
Smart Phone ◽  
Tall Buildings ◽  
Urban Canyon ◽  
Positioning Error ◽  
Fusion Algorithm ◽  
Long Distance ◽  
Filter Model ◽  
Fusion Procedure ◽  
Pedestrian Navigation ◽  
Navigation Scheme

This study presents a comprehensive urban canyon pedestrian navigation scheme. This scheme combines smart phone internal MEMS sensors, GNSS and beacon observations together. Heading estimation is generally a key issue of the PDR algorithm. We design an orientation fusion algorithm to improve smart phone heading using MEMS measurements. Static and kinematic tests are performed, superiority of the improved heading algorithm is verified. We also present different heading processing solutions for comparison and analysis. Heading bias increases with time due to error accumulation and model inaccuracy. Thus, we develop a related heading calibration method based on beacons. This method can help correct smart phone headings continuously to decrease cumulative error. In addition to PDR, we also use GNSS and beacon measurements to integrate a fusion location. In the fusion procedure, we design related algorithms to adjust or limit the use of these different type observations to constrain large jumps in our Kalman filter model, thereby making the solution stable. Navigation experiments are performed in the streets of Mong Kok and Wanchai, which are typically the most crowded areas of Hong Kong, with narrow streets and many pedestrians, vehicles and tall buildings. The first experiment uses the strategy PDR + GNSS + beacon, in east–west orientation street, in which 10 m positioning error is improved from 30 % (smart phone internal GNSS) to 80 % and in south–north orientation street, in which 15 m positioning error is improved from 20 % (smart phone internal GNSS) to 80 % . The second experiment performs two long-distance tests without any beacons, in which the fusion scheme also has significant improvement, that is, 10 m positioning error is improved from 38 % to 60 % .

A Multi-Sensor Fusion Algorithm for Pedestrian Navigation Using Factor Graphs

2021 ◽  
Author(s):  
Langping An ◽  
Xianfei Pan ◽  
Ze Chen ◽  
Mang Wang ◽  
Zheming Tu ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Factor Graphs ◽  
Fusion Algorithm ◽  
Pedestrian Navigation

scholarly journals Performance Enhancement of Pedestrian Navigation Systems Based on Low-Cost Foot-Mounted MEMS-IMU/Ultrasonic Sensor

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 364 ◽  
Author(s):  
Ming Xia ◽  
Chundi Xiu ◽  
Dongkai Yang ◽  
Li Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Ultrasonic Sensor ◽  
Measurement Unit ◽  
Positioning Error ◽  
Process Noise ◽  
Pedestrian Navigation ◽  
Mems Imu

The pedestrian navigation system (PNS) based on inertial navigation system-extended Kalman filter-zero velocity update (INS-EKF-ZUPT or IEZ) is widely used in complex environments without external infrastructure owing to its characteristics of autonomy and continuity. IEZ, however, suffers from performance degradation caused by the dynamic change of process noise statistics and heading estimation errors. The main goal of this study is to effectively improve the accuracy and robustness of pedestrian localization based on the integration of the low-cost foot-mounted microelectromechanical system inertial measurement unit (MEMS-IMU) and ultrasonic sensor. The proposed solution has two main components: (1) the fuzzy inference system (FIS) is exploited to generate the adaptive factor for extended Kalman filter (EKF) after addressing the mismatch between statistical sample covariance of innovation and the theoretical one, and the fuzzy adaptive EKF (FAEKF) based on the MEMS-IMU/ultrasonic sensor for pedestrians was proposed. Accordingly, the adaptive factor is applied to correct process noise covariance that accurately reflects previous state estimations. (2) A straight motion heading update (SMHU) algorithm is developed to detect whether a straight walk happens and to revise errors in heading if the ultrasonic sensor detects the distance between the foot and reflection point of the wall. The experimental results show that horizontal positioning error is less than 2% of the total travelled distance (TTD) in different environments, which is the same order of positioning error compared with other works using high-end MEMS-IMU. It is concluded that the proposed approach can achieve high performance for PNS in terms of accuracy and robustness.

Research on WPS/PDR/MM Integrated Algorithm for Pedestrian Navigation and Positioning

2013 ◽  
Vol 437 ◽  
pp. 870-875 ◽  
Author(s):  
Zhong Liang Deng ◽  
Fei Peng Xie ◽  
Yan Pei Yu ◽  
Xiao Hong Zhao ◽  
Zhuang Yuan
Keyword(s):  
User Experience ◽  
Dead Reckoning ◽  
Positioning System ◽  
Map Matching ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Integrated Method ◽  
Pedestrian Navigation ◽  
Wireless Positioning ◽  
Pedestrian Dead Reckoning

In order to solve the discontinuity of navigation and positioning in indoor signal coverage blind areas, and false region judgment caused by positioning error, an integrated method combining Wireless Positioning System (WPS), Pedestrian Dead Reckoning (PDR) and Map Matching (MM) is presented in this paper. By using the combination of Kalman filtered WPS and PDR information, inertial information and geographic information, pedestrian position could be evaluated. Through experiment, this method effectively increased positioning accuracy of the system as well as greatly improved the user experience.

scholarly journals An Ultra-Short Baseline Positioning Model Based on Rotating Array & Reusing Elements and Its Error Analysis

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4373 ◽  
Author(s):  
Jinwu Tong ◽  
Xiaosu Xu ◽  
Lanhua Hou ◽  
Yao Li ◽  
Jian Wang ◽  
...  
Keyword(s):  
Depth Information ◽  
Base Line ◽  
Positioning System ◽  
Positioning Error ◽  
Long Distance ◽  
Positioning Accuracy ◽  
Underwater Acoustic ◽  
Positioning Systems ◽  
Model Based ◽  
Underwater Positioning

The USBL (Ultra-Short Base Line) positioning system is widely used in underwater acoustic positioning systems due to its small size and ease of use. The traditional USBL positioning system is based on ‘slant range and azimuth’. The positioning error is an increasing function with the increase in distance and the positioning accuracy depends on the ranging accuracy of the underwater target. This method is not suitable for long-distance underwater positioning operations. This paper proposes a USBL positioning calculation model based on depth information for ‘rotating array and reusing elements’. This method does not need to measure the distance between the USBL acoustic array and target, so it can completely eliminate the influence of long-distance ranging errors in USBL positioning. The theoretical analysis and simulation experiments show that the new USBL positioning model based on ‘rotating array and reusing elements’ can completely eliminate the influence of the wavelength error and spacing error of underwater acoustic signals on the positioning accuracy of USBL. The positioning accuracy can be improved by approximately 90%, and the horizontal positioning error within a positioning distance of 1000 m is less than 1.2 m. The positioning method has high precision performance in the long distance, and provides a new idea for the engineering design of a USBL underwater positioning system.

scholarly journals Recovery and guidance technology of collision network based on multi-source information fusion

2021 ◽  
Vol 336 ◽  
pp. 04003
Author(s):  
Kangyi Li ◽  
Xinhua Wang ◽  
Zhengqing Liu
Keyword(s):  
Visual Information ◽  
Least Square Method ◽  
Recovery Process ◽  
Least Square ◽  
Integrated Navigation ◽  
Source Information ◽  
Long Distance ◽  
Position Information ◽  
Sensor Navigation ◽  
Navigation Scheme

To solve the problem of low precision of pose estimation and poor anti-jamming in the recovery process of small Shipborne UAV, a multi-sensor navigation scheme was proposed, based on vision / IMU / GPS fusion. Firstly, an integrated navigation scheme of multi-sensor fusion in collision recovery guidance was proposed. GPS is used for long-distance guidance. Vision / IMU / GPS fusion guidance is used in approach phase. GPS and visual information are fused based on extended Kalman filter to obtaion position information. IMU and visual information are fused based on least square method to obtain the optimal attitude information. According to this method, the optimal pose information is obtained to improve the guidance accuracy and the success rate of net collision recovery.The simulation results show that the guidance deviation is reduced by 30% compared with the unfused data.

Seismic hazard in Buenos Aires, Argentina: A preliminary study on the effects of long-distance earthquakes on tall buildings

2018 ◽  
Vol 47 (11) ◽  
pp. 2333-2339
Author(s):  
Raúl D. Bertero ◽  
Sebastián Vaquero ◽  
Juan M. Mussat ◽  
Agustín Bertero
Keyword(s):  
Seismic Hazard ◽  
Buenos Aires ◽  
Tall Buildings ◽  
Long Distance ◽  
Preliminary Study

scholarly journals Robust Self-Contained Pedestrian Navigation by Fusing the IMU and Compass Measurements via UFIR Filtering

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Meng Hou ◽  
Yuan Xu ◽  
Xiao Liu
Keyword(s):  
Inertial Measurement Unit ◽  
Stance Phase ◽  
Finite Impulse Response ◽  
Measurement Unit ◽  
Test Results ◽  
Robust Performance ◽  
Pedestrian Navigation ◽  
Noise Statistics ◽  
Navigation Scheme ◽  
Real Test

In order to overcome the poor observability of yaw measurement for foot-mounted inertial measurement unit (IMU), an integrated IMU+Compass scheme for self-contained pedestrian navigation is presented. In this mode, the compass measurement is used to provide the accurate yaw to improve the accuracy of the attitude transformation matrix for the foot-mounted IMU solution. And then, when the person is in a stance phase during walk, a unbiased finite impulse response (UFIR) filter based on the self-contained pedestrian navigation scheme is investigated, which just needs the state vector size MU and the filtering horizon size NU, while ignoring the noise statistics compared with the Kalman filter (KF). Finally, a real test has been done to verify the performance of the proposed self-contained pedestrian navigation using the IMU and compass measurements via UFIR filter. The test results show that the proposed filter has robust performance compared with the KF.

scholarly journals CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL

2016 ◽  
Vol XLI-B2 ◽  
pp. 257-262
Author(s):  
X. Yang ◽  
L. Tang
Keyword(s):  
Low Cost ◽  
Tall Buildings ◽  
Geographic Information ◽  
Consensus Algorithm ◽  
Gps Tracking ◽  
Gps Data ◽  
Data Sets ◽  
Filter Model ◽  
Trace Data ◽  
Data Source

GPS traces collected via crowdsourcing way are low-cost and informative and being as a kind of new big data source for urban geographic information extraction. However, the precision of crowdsourcing traces in urban area is very low because of low-end GPS data devices and urban canyons with tall buildings, thus making it difficult to mine high-precision geographic information such as lane-level road information. In this paper, we propose an efficient partition-and-filter model to filter trajectories, which includes trajectory partitioning and trajectory filtering. For the partition part, the partition with position and angle constrain algorithm is used to partition a trajectory into a set of sub-trajectories based on distance and angle constrains. Then, the trajectory filtering with expected accuracy method is used to filter the sub-trajectories according to the similarity between GPS tracking points and GPS baselines constructed by random sample consensus algorithm. Experimental results demonstrate that the proposed partition-and-filtering model can effectively filter the high quality GPS data from various crowdsourcing trace data sets with the expected accuracy.

IMAGE FUSION ALGORITHM BASED ON NONSUBSAMPLED CONTOURLET TRANSFORM AND ESTIMATION THEORY

2009 ◽  
Vol 06 (02) ◽  
pp. 109-116
Author(s):  
GAO-PENG ZHAO ◽  
YU-MING BO
Keyword(s):  
Image Fusion ◽  
Low Frequency ◽  
Gaussian Mixture ◽  
Estimation Theory ◽  
Contourlet Transform ◽  
Model Parameters ◽  
Nonsubsampled Contourlet Transform ◽  
Fusion Algorithm ◽  
Fusion Procedure ◽  
Fused Image

Aimed at the fusion of infrared and visual images, and their application demands, a new image fusion method was proposed based on the nonsubsampled contourlet transform and estimation theory. Firstly, the nonsubsampled contourlet transform was employed to decompose the source images into the low frequency subband coefficient and bandpass directional subband coefficients. Then, for the bandpass directional subband coefficients, the detail coefficients were modeled by the Gaussian mixture distributions and the EM algorithm was used in conjunction with the model to develop an iterative fusion procedure to estimate the model parameters and to produce the fused coefficients; for the fusion of the approximate subband coefficients, the rule was employed based on the energy of the pixel neighboring region. Finally, the fused image was obtained by applying the inverse nonsubsampled contourlet transform. The experimental results showed that the fusion scheme is effective and the fused image is better than that of using the wavelet transform and the contourlet transform.

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