scholarly journals Factor Graph-Assisted Distributed Cooperative Positioning Algorithm in the GNSS System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3748 ◽  
Author(s):  
Chengkai Tang ◽  
Lingling Zhang ◽  
Yi Zhang ◽  
Houbing Song
Keyword(s):  
Smart Cities ◽  
Factor Graph ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Cooperative Positioning ◽  
Measured Range ◽  
Ranging Error ◽  
Positioning Algorithm ◽  
Range Error ◽  
Cooperative Nodes

The development of smart cities calls for improved accuracy in navigation and positioning services; due to the effects of satellite orbit error, ionospheric error, poor quality of navigation signals and so on, it is difficult for existing navigation technology to achieve further improvements in positioning accuracy. Distributed cooperative positioning technology can further improve the accuracy of navigation and positioning with existing GNSS (Global Navigation Satellite System) systems. However, the measured range error and the positioning error of the cooperative nodes exhibit larger reductions in positioning accuracy. In response to this question, this paper proposed a factor graph-aided distributed cooperative positioning algorithm. It establishes the confidence function of factor graphs theory with the ranging error and the positioning error of the coordinated nodes and then fuses the positioning information of the coordinated nodes by the confidence function. It can avoid the influence of positioning error and ranging error and improve the positioning accuracy of cooperative nodes. In the simulation part, the proposed algorithm is compared with a mainly coordinated positioning algorithm from four aspects: the measured range error, positioning error, convergence speed, and mutation error. The simulation results show that the proposed algorithm leads to a 30–60% improvement in positioning accuracy compared with other algorithms under the same measured range error and positioning error. The convergence rate and mutation error elimination times are only 1 / 5 to 1 / 3 of the other algorithms.

The Weighted Multilateral Positioning Algorithm Based on Time Reversal Ranging

2014 ◽  
Vol 644-650 ◽  
pp. 1464-1469
Author(s):  
Zheng Zhang ◽  
Xing Peng Tao ◽  
Lun Zeng ◽  
Chan Wang
Keyword(s):  
Time Reversal ◽  
Wireless Sensor ◽  
Positioning Error ◽  
Complex Environments ◽  
Complex Environment ◽  
General Applicability ◽  
Positioning Accuracy ◽  
Key Technology ◽  
Ranging Error ◽  
Positioning Algorithm

Indoor node positioning is a key technology in wireless sensor network but the general indoor nodes positioning algorithm is difficult to meet the precision positioning requirements due to the indoor complex environment such as multilateral positioning algorithm based on RSSI ranging. The weighted multilateral positioning algorithm is proposed based on time reversal ranging to solve the problem. We simulate within 50m*50m area, the experimental results show that the ranging error is less than 1%. The maximum positioning error is less than 0.6m. Compared with general positioning algorithm, it can improve the positioning accuracy greatly in complex environments and has general applicability.

scholarly journals Factor-graph-aided three-dimensional faster cooperative positioning algorithm

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chengkai Tang ◽  
Jiaqi Liu ◽  
Yi Zhang ◽  
Xingxing Zhu ◽  
Lingling Zhang
Keyword(s):  
Three Dimensional ◽  
Factor Graph ◽  
Cooperative Positioning ◽  
Positioning Algorithm

scholarly journals Factor Graph Weight Particles aided Distributed Underwater Cooperative Positioning Algorithm

2021 ◽  
Author(s):  
lingling zhang ◽  
baoguo yu ◽  
Chengkai Tang ◽  
yi zhang ◽  
Houbing Song
Keyword(s):  
Power Efficiency ◽  
High Efficiency ◽  
Low Complexity ◽  
Factor Graph ◽  
Network Nodes ◽  
Cooperative Positioning ◽  
Underwater Localization ◽  
Positioning Algorithm ◽  
Marine Exploration ◽  
Real Test

Abstract The growing scale of marine exploration requires high-resolution underwater localization, which necessitates cooperation among underwater network nodes, considering the channel complexity and power efficiency. In this paper, we proposed factor graph weight particles aided distributed underwater nodes cooperative positioning algorithm (WP-DUCP). It capitalized on the factor graph and sum-product algorithm to decompose the global optimization to the product of several local optimization functions. Combined with the Gaussian parameters to construct the weighted particles and to realize the belief transfer, it shows low complexity and high efficiency, suitable to the energy-restricted and communication distance-limited underwater networks. In terms of convergence, localization resolution, and computation complexity, we conducted the simulation and real-test with comparison to the existing co-localization methods. The results verified a higher resolution of the proposed method with no extra computation burden.

scholarly journals An Advanced Cooperative Positioning Algorithm Based on Improved Factor Graph and Sum-Product Theory for Multiple AUVs

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 67006-67017 ◽  
Author(s):  
Shiwei Fan ◽  
Ya Zhang ◽  
Chunyang Yu ◽  
Minghong Zhu ◽  
Fei Yu
Keyword(s):  
Factor Graph ◽  
Cooperative Positioning ◽  
Positioning Algorithm

scholarly journals Application of LSTM Network to Improve Indoor Positioning Accuracy

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5824
Author(s):  
Dongqi Gao ◽  
Xiangye Zeng ◽  
Jingyi Wang ◽  
Yanmang Su
Keyword(s):  
Short Term Memory ◽  
Indoor Positioning ◽  
Base Stations ◽  
Ultra Wideband ◽  
Positioning Accuracy ◽  
Last Mile ◽  
Ranging Error ◽  
Positioning Method ◽  
Lstm Network ◽  
Positioning Algorithm

Various indoor positioning methods have been developed to solve the “last mile on Earth”. Ultra-wideband positioning technology stands out among all indoor positioning methods due to its unique communication mechanism and has a broad application prospect. Under non-line-of-sight (NLOS) conditions, the accuracy of this positioning method is greatly affected. Unlike traditional inspection and rejection of NLOS signals, all base stations are involved in positioning to improve positioning accuracy. In this paper, a Long Short-Term Memory (LSTM) network is used while maximizing the use of positioning equipment. The LSTM network is applied to process the raw Channel Impulse Response (CIR) to calculate the ranging error, and combined with the improved positioning algorithm to improve the positioning accuracy. It has been verified that the accuracy of the predicted ranging error is up to centimeter level. Using this prediction for the positioning algorithm, the average positioning accuracy improved by about 62%.

scholarly journals An Improved Indoor Positioning Method Based on Nearest Neighbor Interpolation

2021 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Yonghao Zhao
Keyword(s):  
Nearest Neighbor ◽  
Indoor Positioning ◽  
Location Information ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Indoor Location ◽  
Sampling Points ◽  
Sampling Cost ◽  
Positioning Method ◽  
Positioning Algorithm

Nowadays, people’s demand for indoor location information is more and more, which continuously promotes the development of indoor positioning technology. In the field of indoor positioning, fingerprint based indoor positioning algorithm still accounts for a large proportion. However, the operation of this method in the offline stage is too cumbersome and time-consuming, which makes its disadvantages obvious, and requires a lot of manpower and time to sample and maintain. Therefore, in view of this phenomenon, an improved algorithm based on nearest neighbor interpolation is designed in this paper, which reduces the measurement of actual sampling points when establishing fingerprint map. At the same time, some simulation points are added to expand fingerprint map, so as to ensure that the positioning error will not become larger or even better. Experimental results show that this method can further improve the positioning accuracy while saving the sampling cost.

scholarly journals Improved RSSI Positioning Algorithm for Coal Mine Underground Locomotive

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bin Ge ◽  
Kai Wang ◽  
Jianghong Han ◽  
Bao Zhao
Keyword(s):  
Coal Mine ◽  
Gaussian Model ◽  
Correction Method ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Positioning Errors ◽  
Weighted Model ◽  
The Difference ◽  
Positioning Algorithm ◽  
Received Signal Strength Indication

Aiming at the large positioning errors of traditional coal mine underground locomotive, an improved received signal strength indication (RSSI) positioning algorithm for coal mine underground locomotive was proposed. The RSSI value fluctuates heavily due to the poor environment of coal mine underground. The nodes with larger RSSI value corrected by Gaussian-weighted model were selected as beacon nodes. In order to reduce the positioning error further, the estimated positions of the locomotives were corrected by the weighted distance correction method. The difference between actual position and estimated position of beacon node was regarded as the positioning error and was given a corresponding weight. The results of simulation show that the positioning accuracy of Gaussian-weighted model is better than statistical average model and Gaussian model and it has a high positioning accuracy after correcting positioning error correction. In the 10 m of communication range, positioning error can be maintained at 0.5 m.

scholarly journals An Improved Long-Period Precise Time-Relative Positioning Method Based on RTS Data

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 53
Author(s):  
Yangwei Lu ◽  
Shengyue Ji ◽  
Rui Tu ◽  
Duojie Weng ◽  
Xiaochun Lu ◽  
...  
Keyword(s):  
High Precision ◽  
Reference Station ◽  
Positioning Error ◽  
Relative Positioning ◽  
Precision Positioning ◽  
Positioning Accuracy ◽  
Long Period ◽  
Short Period ◽  
Positioning Method ◽  
Positioning Algorithm

The high precision positioning can be easily achieved by using real-time kinematic (RTK) and precise point positioning (PPP) or their augmented techniques, such as network RTK (NRTK) and PPP-RTK, even if they also have their own shortfalls. A reference station and datalink are required for RTK or NRTK. Though the PPP technique can provide high accuracy position data, it needs an initialisation time of 10–30 min. The time-relative positioning method estimates the difference between positions at two epochs by means of a single receiver, which can overcome these issues within short period to some degree. The positioning error significantly increases for long-period precise positioning as consequence of the variation of various errors in GNSS (Global Navigation Satellite System) measurements over time. Furthermore, the accuracy of traditional time-relative positioning is very sensitive to the initial positioning error. In order to overcome these issues, an improved time-relative positioning algorithm is proposed in this paper. The improved time-relative positioning method employs PPP model to estimate the parameters of current epoch including position vector, float ionosphere-free (IF) ambiguities, so that these estimated float IF ambiguities are used as a constraint of the base epoch. Thus, the position of the base epoch can be estimated by means of a robust Kalman filter, so that the position of the current epoch with reference to the base epoch can be obtained by differencing the position vectors between the base epoch and the current one. The numerical results obtained during static and dynamic tests show that the proposed positioning algorithm can achieve a positioning accuracy of a few centimetres in one hour. As expected, the positioning accuracy is highly improved by combining GPS, BeiDou and Galileo as a consequence of a higher amount of used satellites and a more uniform geometrical distribution of the satellites themselves. Furthermore, the positioning accuracy achieved by using the positioning algorithm here described is not affected by the initial positioning error, because there is no approximation similar to that of the traditional time-relative positioning. The improved time-relative positioning method can be used to provide long-period high precision positioning by using a single dual-frequency (L1/L2) satellite receiver.

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