A combined approach of Doppler and carrier-based hyperbolic positioning with a multi-channel GPS-pseudolite for indoor localization of robots

et al. 2020

Applied Sciences

In recent years, many new technologies have been used in indoor positioning. In 2016, IEEE 802.11-2016 created a Wi-Fi fine timing measurement (FTM) protocol, making Wi-Fi ranging more robust and accurate, and providing meter-level positioning accuracy. However, the accuracy of positioning methods based on the new ranging technology is influenced by non-line-of-sight (NLOS) errors. To enhance the accuracy, a positioning method with LOS (line-of-sight)/NLOS identification is proposed in this paper. A Gaussian model has been established to identify NLOS signals. After identifying and discarding NLOS signals, the least square (LS) algorithm is used to calculate the location. The results of the numerical experiments indicate that our algorithm can identify and discard NLOS signals with a precision of 83.01% and a recall of 74.97%. Moreover, compared with the traditional algorithms, by all ranging results, the proposed method features more accurate and stable results for indoor positioning.

Section: Single Point Positioningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

et al. 2020

Applied Sciences

“…Another multi-channel pseudolite called the GNSS repeater has been proposed by Niwa [11]- [14], which realizes indoor positioning using carrier phase difference method with the base and rover stations. A combined approach of Doppler and carrier-based hyperbolic positioning with a multi-channel GPS-pseudolite for indoor localization has also been proposed by Fujii et al [23], an state equation of three dimensional(3-D) receiver's position and orientation, and ambiguity is established, an observation equation for Doppler positioning is used to estimate the offset of three dimensional(3-D) position and orientation, a nonlinear observation equation for carrier phase difference between pseudolites is used to estimate ambiguity, therefore, it needs to overcome such problems as ambiguity resolution, non-linear observation equation, positioning stability and real-time, etc.. The most famous indoor pseudolites system is produced by Locata Corporation [2], [15], [16], which consists of a network (LocataNet) of time-synchronised transceivers (LocataLites), and it has the potential to allow point positioning with sub-cm precision (using carrier phase) for a mobile unit.…”

Section: Introductionmentioning

confidence: 99%

A New Array Pseudolites Technology for High Precision Indoor Positioning

Gan

et al. 2019

IEEE Access

Global Satellite Navigation System (GNSS) can't provide normal location service in the indoor environment because the signal can be blocked by buildings. The pseudolites system can supplement GNSS, especially in the indoor environments. A new indoor array pseudolites system is introduced, its multi-channel transmitter has an identical clock source. The high-precision Doppler positioning method based on Z-fixed Known Point Initialization (Z-KPI) is developed without ambiguity resolution. Some static and dynamic tests are conducted to evaluate the positioning accuracy of this new method. In the static testing, the average Doppler measurement error is 1×10-5 m/s, which lays the foundation of the high precision velocity measurement, and the average positioning error is 0.02 m in X axis, 0.004 m in Y axis, and the average velocity error is 3.28×10 −5 m/s in X axis, 1.94×10 −5 m/s in Y axis. In the dynamic test, the maximum positioning error is 0.39 m in X axis, 0.34 m in Y axis. Finally, the reason for adopting the Z-fixed KPI algorithm is explained by the horizontal dilution of precision (HDOP), meanwhile, the influence of the known initialization point error on indoor positioning accuracy is analyzed, when a deviation from −1.5 m to 0.9 m is added to the Z-axis of the KPI, the maximum positioning error is 0.311 m in X axis, 0.08 m in Y axis; when the horizontal deviation tolerance of the KPI is set from-0.5 m to 0.5 m, the maximum horizontal positioning error is less than 0.5 m.

“…Nowadays, there are a large number of applications for ILBS in market. And researchers have carried out a large number of studies on indoor positioning technologies and developed corresponding indoor positioning systems, such as the infrared [ 1 ], ultrasonic [ 2 ] or sound [ 3 ], radio frequency identity [ 4 , 5 ] (RFID), ZigBee [ 6 ], Wi-Fi [ 7 , 8 ], Bluetooth [ 9 ], microelectromechanical systems (MEMS) sensors [ 10 ], ultra-wideband [ 11 ] (UWB), geomagnetic field [ 12 , 13 ], visible light [ 14 ], computer vision [ 15 ], pseudolites [ 16 ] (PL), etc. However, due to the complexity of the indoor environment, it is usually difficult to provide a satisfactory level of accuracy in most applications.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Weighted KNN Positioning Method Based on Omnidirectional Fingerprint Database and Twice Affinity Propagation Clustering

et al. 2018

Sensors

The human body has a great influence on Wi-Fi signal power. A fixed K value leads to localization errors for the K-nearest neighbor (KNN) algorithm. To address these problems, we present an adaptive weighted KNN positioning method based on an omnidirectional fingerprint database (ODFD) and twice affinity propagation clustering. Firstly, an OFPD is proposed to alleviate body’s sheltering impact on signal, which includes position, orientation and the sequence of mean received signal strength (RSS) at each reference point (RP). Secondly, affinity propagation clustering (APC) algorithm is introduced on the offline stage based on the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed for estimating user’s position during online stage. K initial RPs can be obtained by KNN, then they are clustered by APC algorithm based on their position-domain distances. The most probable sub-cluster is reserved by the comparison of RPs’ number and signal-domain distance between sub-cluster center and the online RSS readings. The weighted average coordinates in the remaining sub-cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 m, the root mean square error of 1.5 m. Experimental results show that our proposed method outperforms traditional fingerprinting methods.