Seamless Navigation Methodology optimized for Indoor/Outdoor Detection Based on WIFI

Huang, Heze; Zeng, Qinghua; Chen, Ruizhi; Meng, Qian; Wang, Jingxian; Zeng, Shijie

doi:10.1109/upinlbs.2018.8559940

Cited by 12 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ESKF method, which used an error-state representation, has several benefits compared to a nominal state representation [ 21 ]. First of all, the orientation error-state is represented as a 3D vector, which makes the state parameters equal to the degrees of freedom.…”

Section: Data Fusion Methods and Optimizationmentioning

confidence: 99%

“…Reference [ 17 ] focuses on increasing the interference robustness of WiFi fingerprints, and the least square method is used to reduce location error in [ 18 ]. Reference [ 21 ] optimizes the AdaBoost algorithm to increase the accuracy of indoor/outdoor detection. The WiFi signature map and continuous perceptual model are established in [ 22 ] to realize indoor mobile robot navigation, and the localization error is a mean of 1.2 m. The series of references indicate that the WiFi-based positioning algorithm can meet the requirements of indoor positioning for mobile robots.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Constrained ESKF for UAV Positioning in Indoor Corridor Environment Based on IMU and WiFi

Zhang

2022

Sensors

View full text Add to dashboard Cite

The indoor autonomous navigation of unmanned aerial vehicles (UAVs) is the current research hotspot. Unlike the outdoor broad environment, the indoor environment is unknown and complicated. Global Navigation Satellite System (GNSS) signals are easily blocked and reflected because of complex indoor spatial features, which make it impossible to achieve positioning and navigation indoors relying on GNSS. This article proposes a set of indoor corridor environment positioning methods based on the integration of WiFi and IMU. The zone partition-based Weighted K Nearest Neighbors (WKNN) algorithm is used to achieve higher WiFi-based positioning accuracy. On the basis of the Error-State Kalman Filter (ESKF) algorithm, WiFi-based and IMU-based methods are fused together and realize higher positioning accuracy. The probability-based optimization method is used for further accuracy improvement. After data fusion, the positioning accuracy increased by 51.09% compared to the IMU-based algorithm and by 66.16% compared to the WiFi-based algorithm. After optimization, the positioning accuracy increased by 20.9% compared to the ESKF-based data fusion algorithm. All of the above results prove that methods based on WiFi and IMU (low-cost sensors) are very capable of obtaining high indoor positioning accuracy.

show abstract

Section: Data Fusion Methods and Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Constrained ESKF for UAV Positioning in Indoor Corridor Environment Based on IMU and WiFi

Zhang

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…However, these technologies have the same disadvantages as those of INSs: without absolute position of external sensor constraints, the positioning performance will be degraded, and the accuracy will be significantly decreased. On the other hand, radio frequency (RF)-based techniques such as wireless fidelity (Wi-Fi) [13,14], Bluetooth Low Energy (BLE) [15], and UWB [16] have also been extensively investigated. Wi-Fi and BLE technologies currently mainly use fingerprint recognition for positioning, and their characteristics and accuracy cannot meet the positioning requirements of AVs.…”

Section: Related Workmentioning

confidence: 99%

“…In the construction of the TC UWB/INS/map observation model, the UWB observation function is expanded using the Taylor formula at the approximate position x 0 = r e I NS to obtain the TC UWB/INS/map observation equation as follows: (14) where d I NS and d I NS denote the UWB observation and the INS-predicted range, respectively; ε u represents the noise sequence. Consequently, the discrete UWB/INS/map measurement model can be expressed as follows:…”

Section: Measurement Model For the Tc Uwb/ins/map Integrated Systemmentioning

confidence: 99%

A Seamless Navigation System and Applications for Autonomous Vehicles Using a Tightly Coupled GNSS/UWB/INS/Map Integration Scheme

Wang

Xin

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Seamless positioning systems for complex environments have been a popular focus of research on positioning safety for autonomous vehicles (AVs). In particular, the seamless high-precision positioning of AVs indoors and outdoors still poses considerable challenges and requires continuous, reliable, and high-precision positioning information to guarantee the safety of driving. To obtain effective positioning information, multiconstellation global navigation satellite system (multi-GNSS) real-time kinematics (RTK) and an inertial navigation system (INS) have been widely integrated into AVs. However, integrated multi-GNSS and INS applications cannot provide effective and seamless positioning results for AVs in indoor and outdoor environments due to limited satellite availability, multipath effects, frequent signal blockages, and the lack of GNSS signals indoors. In this contribution, multi-GNSS-tightly coupled (TC) RTK/INS technology is developed to solve the positioning problem for a challenging urban outdoor environment. In addition, ultrawideband (UWB)/INS technology is developed to provide accurate and continuous positioning results in indoor environments, and INS and map information are used to identify and eliminate UWB non-line-of-sight (NLOS) errors. Finally, an improved adaptive robust extended Kalman filter (AREKF) algorithm based on a TC integrated single-frequency multi-GNSS-TC RTK/UWB/INS/map system is studied to provide continuous, reliable, high-precision positioning information to AVs in indoor and outdoor environments. Experimental results show that the proposed scheme is capable of seamlessly guaranteeing the positioning accuracy of AVs in complex indoor and outdoor environments involving many measurement outliers and environmental interference effects.

show abstract

“…Early Wi-Fi positioning is mainly applied in Global Navigation Satellite System (GNSS) denied environments, such as indoor environments. It could be integrated with GNSS [18][19][20] in GNSS failure scenarios, such as satellite visibility degradation, multipath propagation, tree and building occlusion, malicious interference, etc. With the intensive and extensive deployment of wireless access points in cities, Wi-Fi technology will become both a feasible and economical method for target localization, vehicles classification [21], traffic parameter measurement, etc.…”

Section: Introductionmentioning

confidence: 99%

A Review of Wi-Fi-Based Traffic Detection Technology in the Field of Intelligent Transportation Systems

Lin

Lyu

et al. 2022

Buildings

View full text Add to dashboard Cite

With the increasing innovation and development of Wi-Fi technology, its penetration in the various fields of industry and academia is becoming more and more profound. As the core infrastructure of traffic data collection in the field of Intelligent Transportation Systems (ITS), Wi-Fi-based traffic detectors have great potential for use in traffic target positioning, perception, and pattern recognition due to their low-cost and extensive infrastructure deployment. This paper conducts a comprehensive review of three major Wi-Fi-based traffic detection applications in the field of ITS: target positioning, traffic parameter extraction, and travel mode identification. Among these, target positioning is one of the most widespread applications of Wi-Fi technology, which is also the basis for two other research aspects. Moreover, Wi-Fi-based positioning can be divided into two categories: ranging-based positioning and range-free one; in the field of transportation, it can also be categorized into pedestrian positioning and vehicle positioning based on travel mode. To further demonstrate the effectiveness of Wi-Fi-based ITS applications in practice, this study compares with the various Wi-Fi-involved models and algorithms around the world, as well as provides some ideas and inspiration along with this direction.

show abstract

Seamless Navigation Methodology optimized for Indoor/Outdoor Detection Based on WIFI

Cited by 12 publications

References 5 publications

Constrained ESKF for UAV Positioning in Indoor Corridor Environment Based on IMU and WiFi

Constrained ESKF for UAV Positioning in Indoor Corridor Environment Based on IMU and WiFi

A Seamless Navigation System and Applications for Autonomous Vehicles Using a Tightly Coupled GNSS/UWB/INS/Map Integration Scheme

A Review of Wi-Fi-Based Traffic Detection Technology in the Field of Intelligent Transportation Systems

Contact Info

Product

Resources

About