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.

The Method of the Global Positioning Based on RSSI

2012 ◽  
Vol 198-199 ◽  
pp. 1438-1441
Author(s):  
Ming Ming Tian ◽  
Zong Jiu Zhu
Keyword(s):  
Coal Mine ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Positioning Error ◽  
Underground Coal Mine ◽  
Global Positioning ◽  
Positioning Algorithm ◽  
Received Signal Strength Indication ◽  
Vertical Positioning ◽  
Communication Radius

A novel Global Positioning Algorithm for Workers in Underground Coal Mine Based on RSSI (Received Signal Strength Indication) is proposed and implemented. The innovation of the algorithm is the RSSI optimization and GIS auxiliary positioning. The RSSI optimization can reduces the error caused when the blind code is too closed to the reference node, and GIS auxiliary positioning is to solve the problem that the number of the reference codes in the communication radius of blind code is less than three. Experimental Results Indicate that Global positioning algorithm has the average vertical positioning error less than 4m, the horizontal error less than 1/2 roadway width, and the positioning cycle less than 1s, with better positioning and tracking performance, and expands the coverage of the network of underground positioning.

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.

scholarly journals Geo-Location Algorithm for Building Targets in Oblique Remote Sensing Images Based on Deep Learning and Height Estimation

2020 ◽  
Vol 12 (15) ◽  
pp. 2427
Author(s):  
Yiming Cai ◽  
Yalin Ding ◽  
Hongwen Zhang ◽  
Jihong Xiu ◽  
Zhiming Liu
Keyword(s):  
Remote Sensing ◽  
Simulation Analysis ◽  
Recognition Algorithm ◽  
Positioning Error ◽  
Remote Sensing Images ◽  
Target Height ◽  
Building Recognition ◽  
Aerial Remote Sensing ◽  
The Difference ◽  
Positioning Algorithm

To improve the accuracy of the geographic positioning of a single aerial remote sensing image, the height information of a building in the image must be considered. Oblique remote sensing images are essentially two-dimensional images and produce a large positioning error if a traditional positioning algorithm is used to locate the building directly. To address this problem, this study uses a convolutional neural network to automatically detect the location of buildings in remote sensing images. Moreover, it optimizes an automatic building recognition algorithm for oblique aerial remote sensing images based on You Only Look Once V4 (YOLO V4). This study also proposes a positioning algorithm for the building target, which uses the imaging angle to estimate the height of a building, and combines the spatial coordinate transformation matrix to calculate high-accuracy geo-location of target buildings. Simulation analysis shows that the traditional positioning algorithm inevitably leads to large errors in the positioning of building targets. When the target height is 50 m and the imaging angle is 70°, the positioning error is 114.89 m. Flight tests show that the algorithm established in this study can improve the positioning accuracy of building targets by approximately 20%–50% depending on the difference in target height.

scholarly journals Intelligent Calibration of a Heavy-Duty Mechanical Arm in Coal Mine

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1186
Author(s):  
Yunhong Jia ◽  
Xiaodong Zhang ◽  
Zhenchong Wang ◽  
Wei Wang
Keyword(s):  
Extreme Learning Machine ◽  
Coal Mine ◽  
Absolute Error ◽  
Calibration Method ◽  
Heavy Duty ◽  
Maximum Absolute Error ◽  
Positioning Accuracy ◽  
Positioning Errors ◽  
Roof Bolter ◽  
Learning Machine

Accurate positioning of an airborne heavy-duty mechanical arm in coal mine, such as a roof bolter, is important for the efficiency and safety of coal mining. Its positioning accuracy is affected not only by geometric errors but also by nongeometric errors such as link and joint compliance. In this paper, a novel calibration method based on error limited genetic algorithm (ELGA) and regularized extreme learning machine (RELM) is proposed to improve the positioning accuracy of a roof bolter. To achieve the improvement, the ELGA is firstly implemented to identify the geometric parameters of the roof bolter’s kinematics model. Then, the residual positioning errors caused by nongeometric facts are compensated with the regularized extreme learning machine (RELM) network. Experiments were carried out to validate the proposed calibration method. The experimental results show that the root mean square error (RMSE) and the mean absolute error (MAE) between the actual mast end position and the nominal mast end position are reduced by more than 78.23%. It also shows the maximum absolute error (MAXE) between the actual mast end position and the nominal mast end position is reduced by more than 58.72% in the three directions of Cartesian coordinate system.

Positioning Method without Current Navigation Data

2014 ◽  
Vol 556-562 ◽  
pp. 3234-3237
Author(s):  
Ji Zhong Li ◽  
Rui Wang
Keyword(s):  
Objective Function ◽  
Mobile Station ◽  
Positioning Error ◽  
Fuzzy Algorithm ◽  
Reference Time ◽  
Navigation Data ◽  
Clock Error ◽  
Positioning Errors ◽  
Positioning Method ◽  
Positioning Algorithm

Positioning errors were found through the analysis to AGPS positioning algorithm with mobile station (MS) clock error. These errors are engendered in the process of calculating the pseudo-range. Using fuzzy algorithm, the large positioning error of several hundred kilometers was eliminated. After this method, the objective function is quadratically related to the deviation of reference time. The objective function curve were fitted with three reference time values and determined the lowest point of curve to amend the reference time. This method removed residual positioning error.

scholarly journals An Enhanced Indoor Positioning Algorithm Based on Fingerprint Using Fine-Grained CSI and RSSI Measurements of IEEE 802.11n WLAN

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2769
Author(s):  
Jingjing Wang ◽  
Joongoo Park
Keyword(s):  
Nearest Neighbor ◽  
Low Cost ◽  
Gaussian Function ◽  
Low Complexity ◽  
Correction Method ◽  
Indoor Positioning ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Indoor Location ◽  
Positioning Algorithm

Received signal strength indication (RSSI) obtained by Medium Access Control (MAC) layer is widely used in range-based and fingerprint location systems due to its low cost and low complexity. However, RSS is affected by noise signals and multi-path, and its positioning performance is not stable. In recent years, many commercial WiFi devices support the acquisition of physical layer channel state information (CSI). CSI is an index that can characterize the signal characteristics with more fine granularity than RSS. Compared with RSS, CSI can avoid the effects of multi-path and noise by analyzing the characteristics of multi-channel sub-carriers. To improve the indoor location accuracy and algorithm efficiency, this paper proposes a hybrid fingerprint location technology based on RSS and CSI. In the off-line phase, to overcome the problems of low positioning accuracy and fingerprint drift caused by signal instability, a methodology based on the Kalman filter and a Gaussian function is proposed to preprocess the RSSI value and CSI amplitude value, and the improved CSI phase is incorporated after the linear transformation. The mutation and noisy data are then effectively eliminated, and the accurate and smoother outputs of the RSSI and CSI values can be achieved. Then, the accurate hybrid fingerprint database is established after dimensionality reduction of the obtained high-dimensional data values. The weighted k-nearest neighbor (WKNN) algorithm is applied to reduce the complexity of the algorithm during the online positioning stage, and the accurate indoor positioning algorithm is accomplished. Experimental results show that the proposed algorithm exhibits good performance on anti-noise ability, fusion positioning accuracy, and real-time filtering. Compared with CSI-MIMO, FIFS, and RSSI-based methods, the proposed fusion correction method has higher positioning accuracy and smaller positioning error.

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 Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4515 ◽  
Author(s):  
Markus Bullmann ◽  
Toni Fetzer ◽  
Frank Ebner ◽  
Markus Ebner ◽  
Frank Deinzer ◽  
...  
Keyword(s):  
Estimation Method ◽  
Indoor Positioning ◽  
Propagation Time ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Channel Bandwidth ◽  
Timing Measurement ◽  
The Difference ◽  
Error Behavior ◽  
Received Signal Strength Indication

With the addition of the Fine Timing Measurement (FTM) protocol in IEEE 802.11-2016, a promising sensor for smartphone-based indoor positioning systems was introduced. FTM enables a Wi-Fi device to estimate the distance to a second device based on the propagation time of the signal. Recently, FTM has gotten more attention from the scientific community as more compatible devices become available. Due to the claimed robustness and accuracy, FTM is a promising addition to the often used Received Signal Strength Indication (RSSI). In this work, we evaluate FTM on the 2.4 GHz band with 20 MHz channel bandwidth in the context of realistic indoor positioning scenarios. For this purpose, we deploy a least-squares estimation method, a probabilistic positioning approach and a simplistic particle filter implementation. Each method is evaluated using FTM and RSSI separately to show the difference of the techniques. Our results show that, although FTM achieves smaller positioning errors compared to RSSI, its error behavior is similar to RSSI. Furthermore, we demonstrate that an empirically optimized correction value for FTM is required to account for the environment. This correction value can reduce the positioning error significantly.

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 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.

Sign in / Sign up

Export Citation Format

Share Document