A Weighted Quadrangle Positioning Algorithm Using Automatic Distance Correction for Optimizing Indoor Positioning Accuracy

Currently, there is an urgent need for indoor positioning technology. Considering the complexity of indoor environment, this paper proposes a new positioning algorithm (N-CHAN) via the analysis of the error of arrival time positioning (TOA) and the channels of S-V model. It overcomes an obvious shortcoming that the accuracy of traditional CHAN algorithm effected by no-line-of-sight (NLOS). Finally, though MATLAB software simulation, we prove that N-CHAN’s superior performance in NLOS in the S-V channel model, which has a positioning accuracy of centimeter-level and can effectively eliminate the influence of NLOS error on positioning accuracy. Moreover, the N-CHAN can effectively improve the positioning accuracy of the system, especially in the conditions of larger NLOS error.

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Research on Indoor Positioning Algorithm of Single Reader Based on Gated Recurrent Unit

Artificial Intelligence Evolution ◽

10.37256/aie.212021663 ◽

2021 ◽

pp. 1-10

Author(s):

Jintao Tang ◽

Lvqing Yang ◽

Jiangsheng Zhao ◽

Yishu Qiu ◽

Yihui Deng

Keyword(s):

Internet Of Things ◽

Radio Frequency Identification ◽

Indoor Positioning ◽

The Internet ◽

Positioning Accuracy ◽

Frequency Identification ◽

The Cost ◽

Positioning Algorithm ◽

Gated Recurrent Unit ◽

The Internet Of Things

With the development of the Internet of Things and Radio Frequency Identification (RFID), indoor positioning technology as an important part of positioning technology, has been attracting much attention in recent years. In order to solve the problems of low precision, high cost and signal collision between readers, a new indoor positioning algorithm based on a single RFID reader combined with a Double-order Gated Recurrent Unit (GRU) are proposed in this paper. Firstly, the reader is moved along the specified direction to collect the sequential tag data. Then, the tag’s coordinate is taken as the target value to train models and compare them with existing algorithms. Finally, the best Gated Recurrent Unit positioning model is used to estimate the position of the tags. Experiment results show that the proposed algorithm can effectively improve positioning accuracy, reduce the number of readers, cut down the cost and eliminate the collisions of reader signals.

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Mobile Robot Indoor Positioning System Based on K-ELM

Journal of Sensors ◽

10.1155/2019/7547648 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Haixia Wang ◽

Junliang Li ◽

Wei Cui ◽

Xiao Lu ◽

Zhiguo Zhang ◽

...

Keyword(s):

Mobile Robot ◽

Indoor Localization ◽

Indoor Positioning ◽

Positioning System ◽

Positioning Accuracy ◽

Positioning Method ◽

Kernel Extreme Learning Machine ◽

Learning Machine ◽

Positioning Algorithm ◽

Indoor Positioning System

Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices.

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

Sensors ◽

10.3390/s21082769 ◽

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.

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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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Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs

Sensors ◽

10.3390/s19224871 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4871 ◽

Cited By ~ 2

Author(s):

Jing Chen ◽

Shixin Wang ◽

Mingsan Ouyang ◽

Yuting Xuan ◽

Kuan-Ching Li

Keyword(s):

Distance Distribution ◽

Distribution Model ◽

Localization Algorithm ◽

Target Node ◽

Positioning Accuracy ◽

Position Information ◽

Deviation Coefficient ◽

Positioning Algorithm ◽

Distance Deviation ◽

Distance Correction

Node position information is critical in wireless sensor networks (WSN). However, existing positioning algorithms commonly have the issue of low positioning accuracy due to noise interferences in communication. Hence, proposed in this paper is an iterative positioning algorithm based on distance correction to improve the positioning accuracy of target nodes in WSNs, with contributions including (1) a log-distance distribution model of received signal strength indication (RSSI) ranging which is built and from which is derived a noise impact factor based on the model, (2) the initial position coordinates of the target node obtained using a triangle centroid localization algorithm, via which the distance deviation coefficient under the influence of noise is calculated, and (3) the ratio of the distance measured by the log-distance distribution model to the median distance deviation coefficient which is taken as the new distance between the target node and the anchor node. Based on the new distance, the triangular centroid positioning algorithm is applied to calculate the coordinates of the target node, after which the iterative positioning model is constructed and the distance deviation coefficient updated repeatedly to update the positioning result until the criteria of iterations are reached. Experiment results show that the proposed iterative positioning algorithm is promising and effectively improves positioning accuracy.

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An Enhanced Indoor Positioning Technique Based on a Novel Received Signal Strength Indicator Distance Prediction and Correction Model

Sensors ◽

10.3390/s21030719 ◽

2021 ◽

Vol 21 (3) ◽

pp. 719

Author(s):

Mohammed Nagah Amr ◽

Hussein M. ELAttar ◽

Mohamed H. Abd El Azeem ◽

Hesham El Badawy

Keyword(s):

Signal Strength ◽

Indoor Positioning ◽

Low Energy ◽

Indoor Environments ◽

Positioning Accuracy ◽

Correction Model ◽

Positioning Technique ◽

Unknown Node ◽

Positioning Algorithm ◽

Distance Prediction

Indoor positioning has become a very promising research topic due to the growing demand for accurate node location information for indoor environments. Nonetheless, current positioning algorithms typically present the issue of inaccurate positioning due to communication noise and interferences. In addition, most of the indoor positioning techniques require additional hardware equipment and complex algorithms to achieve high positioning accuracy. This leads to higher energy consumption and communication cost. Therefore, this paper proposes an enhanced indoor positioning technique based on a novel received signal strength indication (RSSI) distance prediction and correction model to improve the positioning accuracy of target nodes in indoor environments, with contributions including a new distance correction formula based on RSSI log-distance model, a correction factor (Beta) with a correction exponent (Sigma) for each distance between unknown node and beacon (anchor nodes) which are driven from the correction formula, and by utilizing the previous factors in the unknown node, enhanced centroid positioning algorithm is applied to calculate the final node positioning coordinates. Moreover, in this study, we used Bluetooth Low Energy (BLE) beacons to meet the principle of low energy consumption. The experimental results of the proposed enhanced centroid positioning algorithm have a significantly lower average localization error (ALE) than the currently existing algorithms. Also, the proposed technique achieves higher positioning stability than conventional methods. The proposed technique was experimentally tested for different received RSSI samples’ number to verify its feasibility in real-time. The proposed technique’s positioning accuracy is promoted by 80.97% and 67.51% at the office room and the corridor, respectively, compared with the conventional RSSI trilateration positioning technique. The proposed technique also improves localization stability by 1.64 and 2.3-fold at the office room and the corridor, respectively, compared to the traditional RSSI localization method. Finally, the proposed correction model is totally possible in real-time when the RSSI sample number is 50 or more.

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An Indoor Positioning Algorithm for Wearable Device Using Deep Learning Regression Prediction Model in IoT Applications

Mathematical Problems in Engineering ◽

10.1155/2020/8842784 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7

Author(s):

Aichuan Li ◽

Shujuan Yi

Keyword(s):

Deep Learning ◽

Signal Strength ◽

Indoor Positioning ◽

Wearable Device ◽

Strength Characteristics ◽

Average Error ◽

Positioning Accuracy ◽

Fingerprint Database ◽

Positioning Algorithm ◽

Automatic Coding

To solve the problem of low positioning accuracy and ease environmental impact of wearable devices in the Internet of things, a wearable device indoor positioning algorithm based on deep learning was proposed. Firstly, a basic model of deep learning composed of an input layer, hidden layer, and output layer is proposed to realize the continuous prediction and positioning with higher accuracy. Secondly, the automatic stacking encoder is trained with signal strength data, and features are extracted from a large number of signal strength samples with noise to build the location fingerprint database. Finally, the stacking automatic coding machine is used to obtain the signal strength characteristics of the points to be measured, which are matched with the signal strength characteristics in the fingerprint database, and the location of the points to be measured is estimated by the nearest neighbor algorithm. The experimental results show that the indoor positioning algorithm based on the stacking automatic coding machine has higher positioning accuracy, and the average error of points on the complete path can reach within 3 m in 93% cases.

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