The Weighted Multilateral Positioning Algorithm Based on Time Reversal Ranging

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.

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

Sensors ◽

10.3390/s18113748 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3748 ◽

Cited By ~ 11

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.

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Research on Wireless Sensor Network Positioning Based on Genetic Algorithm

Wireless Communications and Mobile Computing ◽

10.1155/2021/3996401 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Jing Zhang ◽

Yajing Hu ◽

Hongliang Li

Keyword(s):

Genetic Algorithm ◽

Wireless Sensor ◽

Harsh Environment ◽

Positioning Error ◽

Network Construction ◽

Maximum Coverage ◽

Ranging Error ◽

Anchor Nodes ◽

Communication Constraint ◽

Positioning Algorithm

For smart city wireless sensing network construction needs, a network positioning algorithm based on genetic algorithm is proposed. The genetic algorithm uses a real number encoding, and the positioning model is constructed by analyzing the communication constraint between unknown nodes and a small amount of anchor nodes and constructs the positioning model, and the model is solved. The results show that when the ranging error is 50%, the positioning error is only increased by approximately 15% compared to the nonranging error. In a more harsh environment, if the ranging error is equal to the node wireless range, the ranging error is 100%, and the positioning error and the positioning ratio are not significantly changed. The scheme obtained by this algorithm can be well approarded with an ideal limit. In the case where the sensor node is given, the algorithm can obtain the maximum coverage.

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Exploration of Wireless Sensor Network Based on RSSI Positioning Algorithm

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v14i11.9519 ◽

2018 ◽

Vol 14 (11) ◽

pp. 133

Author(s):

Shuan Liu

Keyword(s):

Signal Strength ◽

Wireless Sensor ◽

Positioning Error ◽

Network Resources ◽

Simulation Experiments ◽

Wormhole Attack ◽

Positioning Method ◽

Physical Information ◽

Positioning Algorithm ◽

Node Energy

Based on the security of the receiving signal strength indicator positioning algorithm, the RSSI positioning algorithm in the environment of witch attack, wormhole attack and replication attack has largely failed.Although existing security positioning algorithms can effectively prevent attacks from occurring, the massive consumption of network resources can’t be ignored.Therefore, a tolerable security positioning method is proposed for each of the three attacks in order to improve the security of positioning.According to the node's physical information, the attack node is detected.Through simulation experiments, compared with the traditional indoor security positioning method, the proposed algorithm can significantly reduce the intervention of witch attack, wormhole attack and replication attack on positioning error.While achieving the goal of combating attacks, it reduces the computational complexity, decreases node energy consumption, and extends the network life cycle.

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Application of LSTM Network to Improve Indoor Positioning Accuracy

Sensors ◽

10.3390/s20205824 ◽

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%.

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A Novel Wireless Sensor Node Positioning Algorithm Based on Ant Colony Optimization Algorithm and Neural Network

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v14i10.9301 ◽

2018 ◽

Vol 14 (10) ◽

pp. 40

Author(s):

Beichen Chen

Keyword(s):

Neural Network ◽

Ant Colony Optimization ◽

Cluster Structure ◽

Ant Colony ◽

Wireless Sensor ◽

Wireless Sensor Node ◽

Ant Colony Optimization Algorithm ◽

Positioning Accuracy ◽

Bee Colony ◽

Positioning Algorithm

This paper aims to enhance the positioning accuracy of wireless sensor network (WSN) nodes. For this purpose, a WSN node positioning algorithm was proposed based on artificial bee colony (ABC) algorithm and the neural network (NN). First, the parameters between three anchor nodes and the target node were measured. Then, the ABC and NN were introduced to simulate and predict the ranging error, and the weight was determined according to the results. In the proposed algorithm, the cluster structure was effectively combined with the NN model. The weight of backpropagation NN was optimized by the ant colony optimization (ACO) algorithm. Then, the ACO-optimized NN was used to fuse the data collected by WSN nodes. The simulation results show that the proposed algorithm can improve the positioning accuracy of WSN nodes and reduce the time of the search. The research findings shed new light on the positioning of WSN nodes.

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Map-Aided Indoor Positioning Algorithm with Complex Deployed BLE Beacons

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10080526 ◽

2021 ◽

Vol 10 (8) ◽

pp. 526

Author(s):

Wuping Liu ◽

Wei Guo ◽

Xinyan Zhu

Keyword(s):

Communication Technology ◽

Indoor Positioning ◽

Low Energy ◽

Complex Environments ◽

Positioning Accuracy ◽

Parking Lot ◽

Multiple Cell ◽

Positioning Algorithm ◽

The Relationship ◽

Selection Of

As communication technology and smartphones develop, many indoor positioning applications based on Bluetooth low energy (BLE) beacons have emerged. However, in a complex BLE network, it can be challenging to select the optimal reference beacon, and accurate positioning becomes difficult. Fortunately, if the BLE network is displayed on a map, we can intuitively grasp the structure and density of the beacons in each area, which is important information for accurate positioning. Therefore, in this study we developed a map-aided indoor positioning algorithm to model the relationship between beacons in the positioning area in a parking lot. Specifically, the algorithm split all beacons into multiple cell areas to find the optimal reference beacon in that area. Then, the optimal reference beacon is used to find the preferred reference beacons among the real-time beacons. Finally, the positioning results were calculated and evaluated according to the preferred beacons. According to the results, our method can optimize the selection of reference beacons in different areas. The average positioning accuracy was 2.09 m and the results can be scored accurately. The results verify that our algorithm can effectively use map information to guide the selection of reference beacons in complex environments.

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An Improved Indoor Positioning Method Based on Nearest Neighbor Interpolation

Network and Communication Technologies ◽

10.5539/nct.v6n1p1 ◽

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.

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Improved RSSI Positioning Algorithm for Coal Mine Underground Locomotive

Journal of Electrical and Computer Engineering ◽

10.1155/2015/918962 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 4

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.

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Underground Operator Monitoring Platform Based on Ultra-Wide Band WSN

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v14i10.9307 ◽

2018 ◽

Vol 14 (10) ◽

pp. 219 ◽

Cited By ~ 4

Author(s):

Weicai Xie ◽

Xiaofeng Li ◽

Xi Long

Keyword(s):

Wide Band ◽

Wireless Sensor ◽

Ultra Wide Band ◽

Font Size ◽

Time Difference Of Arrival ◽

Positioning Accuracy ◽

Research Findings ◽

Property Losses ◽

Positioning Algorithm ◽

Monitoring Platform

This paper aims to accurately locate underground personnel in coal mines. For this purpose, an underground personnel positioning platform was established on the wireless sensor network (WSN). Specifically, the ultra-wide band (UWB) and the time difference of arrival (TDOA) positioning algorithm were introduced briefly, in view of the underground operation environment. Then, the underground operator monitoring platform was developed based on UWB-WSN and compared it with different positioning techniques through experiments. The results show that the proposed platform achieved a high positioning accuracy and satisfied the needs of real-time monitoring of underground personnel. The research findings shed new light on the mitigation of personnel and property losses in coal mine accidents.

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

Sensors ◽

10.3390/s21010053 ◽

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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