scholarly journals A Method for Improving UWB Indoor Positioning

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Zhanjun Hao ◽  
Beibei Li ◽  
Xiaochao Dang
Keyword(s):  
Estimation Method ◽  
Pulse Amplitude ◽  
Relative Displacement ◽  
Indoor Positioning ◽  
Base Stations ◽  
Ultra Wideband ◽  
Signal Interference ◽  
Positioning Errors ◽  
Wireless Environment ◽  
The Time Domain

The existing positioning methods that use received signal strength indication (RSSI) and channel state information (CSI) may suffer from multipath and shadowing in a complex wireless environment, which can result in more positioning errors. This paper proposes a method for accurate multilabel positioning in the non-line-of-sight (NLOS) environment. First, the position is roughly estimated using the orthogonal variable spreading factor (OVSF-TH) algorithm, which can automatically match the signal interference. The ultra-wideband (UWB) spectral density and pulse amplitude in the time domain are used to determine the direction of the label and enhance estimation of the mobile label direction. Then, the location of the tag is obtained by triangulation, and a coordinate-based coordinate estimation method is proposed to calculate the relative displacement of multiple tags to determine the label position. Finally, by setting up a real experimental environment, the influence of the number of base stations on the accuracy and the performance of the localization method under different circumstances are analyzed. The theoretical analysis and experimental results show that the method is simple to deploy, inexpensive, and very accurate in terms of positioning, having a clearly effective indoor positioning accuracy. Compared with other existing positioning methods, this method can achieve more accurate positioning. Moreover, it has important theoretical and practical applicability because of the reliability and accuracy of indoor positioning in an NLOS environment.

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 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.

scholarly journals A high-accuracy indoor positioning system based on UWB

2018 ◽  
Vol 173 ◽  
pp. 01021
Author(s):  
Hanyu Liu ◽  
Yanhan Zeng ◽  
Ruguo Li ◽  
Huajie Huang
Keyword(s):  
Indoor Positioning ◽  
Mobile Node ◽  
High Accuracy ◽  
Base Stations ◽  
Ultra Wideband ◽  
Time Of Arrival ◽  
Positioning System ◽  
Wide Range ◽  
Fixed Base ◽  
Indoor Positioning System

In this paper, a high-accuracy indoor positioning system based on the ultra-wideband (UWB) technique is proposed. The proposed system uses a simple ranging process to obtain the distance between the mobile node and the fixed base stations. Besides, an improved time of arrival (ToA) algorithm with Kalman filtering is proposed to improve the positioning accuracy. Measurements have been performed in the real indoor 13m*7.6m environment with many obstacles and the root-mean-square error (RMSE) is less than 0.3m. The proposed system offers a wide range of application in robotics, industrial automation, post-sorting system and so on.

scholarly journals Timestamp Estimation in P802.15.4z Amendment

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5422
Author(s):  
Ioan Domuta ◽  
Tudor Petru Palade ◽  
Emanuel Puschita ◽  
Andra Pastrav
Keyword(s):  
Multipath Propagation ◽  
Pulse Amplitude ◽  
Impulse Radio ◽  
Ultra Wideband ◽  
Selective Fading ◽  
Frequency Selective Fading ◽  
Power Spectral ◽  
Localization Precision ◽  
Frequency Selective ◽  
The Time Domain

Due to the known issue that the ranging in the 802.15.4™-2015 standard is prone to external attacks, the enhanced impulse radio (EiR), a new amendment still under development, advances the secure ranging protocol by encryption of physical layer (PHY) timestamp sequence using the AES-128 encryption algorithm. This new amendment brings many changes and enhancements which affect the impulse-radio ultra-wideband (IR-UWB) ranging procedures. The timestamp detection is the base factor in the accuracy of range estimation and inherently in the localization precision. This paper analyses the key parts of PHY which have a great contribution in timestamp estimation precision, particularly: UWB pulse, channel sounding and timestamp estimation using ciphered sequence and frequency selective fading. Unlike EiR, where the UWB pulse is defined in the time domain, in this article, the UWB pulse is synthesized from the power spectral density mask, and it is shown that the use of the entire allocated spectrum results in a decrease in risetime, an increase in pulse amplitude, and an attenuation of lateral lobes. The paper proposes a random spreading of the scrambled timestamp sequence (STS), resulting in an improvement in timestamp estimation by the attenuation lateral lobes of the correlation. The timestamp estimation in the noisy channels with non-line-of-sight and multipath propagation is achieved by cross-correlation of the received STS with the locally generated replica of STS. The propagation in the UWB channel with frequency selective fading results in small errors in the timestamp detection.

scholarly journals Improving the Accuracy of Estimates of Indoor Distance Moved Using Deep Learning-Based Movement Status Recognition

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 346
Author(s):  
Zhenjie Ma ◽  
Wenjun Zhang ◽  
Ke Shi
Keyword(s):  
Deep Learning ◽  
Indoor Positioning ◽  
Ultra Wideband ◽  
Indoor Environments ◽  
Job Allocation ◽  
The Real ◽  
Positioning Errors ◽  
Wireless Signal ◽  
Movement Distance ◽  
Status Recognition

As a result of the development of wireless indoor positioning techniques such as WiFi, Bluetooth, and Ultra-wideband (UWB), the positioning traces of moving people or objects in indoor environments can be tracked and recorded, and the distances moved can be estimated from these data traces. These estimates are very useful in many applications such as workload statistics and optimized job allocation in the field of logistics. However, due to the uncertainties of the wireless signal and corresponding positioning errors, accurately estimating movement distance still faces challenges. To address this issue, this paper proposes a movement status recognition-based distance estimating method to improve the accuracy. We divide the positioning traces into segments and use an encoder–decoder deep learning-based model to determine the motion status of each segment. Then, the distances of these segments are calculated by different distance estimating methods based on their movement statuses. The experiments on the real positioning traces demonstrate the proposed method can precisely identify the movement status and significantly improve the distance estimating accuracy.

scholarly journals Analysis and Improvement of Indoor Positioning Accuracy for UWB Sensors

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5731
Author(s):  
Leehter Yao ◽  
Lei Yao ◽  
Yeong-Wei Wu
Keyword(s):  
Mathematical Model ◽  
Mathematical Models ◽  
Indoor Positioning ◽  
Mobile Node ◽  
Ultra Wideband ◽  
Positioning Accuracy ◽  
3 Dimensional ◽  
Positioning Errors ◽  
3D Positioning ◽  
2D And 3D

Ultra-wideband (UWB) sensors have been widely applied to indoor positioning. The indoor positioning of UWB sensors usually refers to the positioning of the mobile node that interacts with the anchors through radio for calculating the distance between the mobile node and each of the surrounding anchors. The positioning accuracy of the mobile node is affected by the installation positions of surrounding anchors. A mathematical model was proposed in this paper to respectively analyze the mobile node’s 2-dimensional (2D) and 3-dimensional (3D) positioning errors. The factors influencing the mobile node’s positioning errors were explored through the mathematical models. The best installation positions of surrounding anchors were obtained based on the mathematical models. The mobile node’s 2D and 3D positioning errors were reduced based on the anchor positions derived from the mathematical model. Both computer simulations and practical experiments were implemented to justify the results obtained in the mathematical models.

scholarly journals Integrated IMU with Faster R-CNN Aided Visual Measurements from IP Cameras for Indoor Positioning

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3134 ◽  
Author(s):  
Lin Zhang ◽  
Taoyun Zhou ◽  
Baowang Lian
Keyword(s):  
Indoor Positioning ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Inertial Measurement ◽  
Positioning Errors ◽  
Measurement Units ◽  
Stationary Obstacles ◽  
Indoor Scenarios ◽  
Visual Measurements

Considering the radio-based indoor positioning system pertaining to signal degradation due to the environmental factors, and rising popularity of IP (Internet Protocol) cameras in cities, a novel fusion of inertial measurement units (IMUs) with external IP cameras to determine the positions of moving users in indoor environments is presented. This approach uses a fine-tuned Faster R-CNN (Region Convolutional Neural Network) to detect users in images captured by cameras, and acquires visual measurements including ranges and angles of users with respect to the cameras based on the proposed monocular vision relatively measuring (MVRM) method. The results are determined by integrating the positions predicted by each user’s inertial measurement unit (IMU) and visual measurements using an EKF (Extended Kalman Filter). The results experimentally show that the ranging accuracy is affected by both the detected bounding box’s by Faster R-CNN height errors and diverse measuring distances, however, the heading accuracy is solely interfered with bounding box’s horizontal biases. The indoor obstacles including stationary obstacles and a pedestrian in our tests more significantly decrease the accuracy of ranging than that of heading, and the effect of a pedestrian on the heading errors is greater than stationary obstacles on that. We implemented a positioning test for a single user and an external camera in five indoor scenarios to evaluate the performance. The robust fused IMU/MVRM solution significantly decreases the positioning errors and shows better performance in dense multipath scenarios compared with the pure MVRM solution and ultra-wideband (UWB) solution.

scholarly journals High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo
Keyword(s):  
High Precision ◽  
Distance Estimation ◽  
Sampling Period ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Location Determination ◽  
Indoor Positioning System

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

scholarly journals Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 2
Author(s):  
Alwin Poulose ◽  
Dong Seog Han
Keyword(s):  
Deep Learning ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Learning Model ◽  
Signal Interference ◽  
Positioning System ◽  
Heat Maps ◽  
Positioning Systems ◽  
Localization Performance ◽  
Deep Learning Model

Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.

scholarly journals Wireless Body Area Networks: UWB Wearable Textile Antenna for Telemedicine and Mobile Health Systems

Micromachines ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 558 ◽  
Author(s):  
Ashok Yadav ◽  
Vinod Kumar Singh ◽  
Akash Kumar Bhoi ◽  
Gonçalo Marques ◽  
Begonya Garcia-Zapirain ◽  
...  
Keyword(s):  
Health Systems ◽  
Mobile Health ◽  
Human Body ◽  
Radiation Effects ◽  
Analytical Investigation ◽  
Ultra Wideband ◽  
Impedance Bandwidth ◽  
Textile Antenna ◽  
Mobile Health Systems ◽  
The Time Domain

A compact textile ultra-wideband (UWB) antenna with an electrical dimension of 0.24λo × 0.24λo × 0.009λo with microstrip line feed at lower edge and a frequency of operation of 2.96 GHz is proposed for UWB application. The analytical investigation using circuit theory concepts and the cavity model of the antenna is presented to validate the design. The main contribution of this paper is to propose a wearable antenna with wide impedance bandwidth of 118.68 % (2.96–11.6 GHz) applicable for UWB range of 3.1 to 10.6 GHz. The results present a maximum gain of 5.47 dBi at 7.3 GHz frequency. Moreover, this antenna exhibits Omni and quasi-Omni radiation patterns at various frequencies (4 GHz, 7 GHz and 10 GHz) for short-distance communication. The cutting notch and slot on the patch, and its effect on the antenna impedance to increase performance through current distribution is also presented. The time-domain characteristic of the proposed antenna is also discussed for the analysis of the pulse distortion phenomena. A constant group delay less than 1 ns is obtained over the entire operating impedance bandwidth (2.96–11.6 GHz) of the textile antenna in both situations, i.e., side by side and front to front. Linear phase consideration is also presented for both situations, as well as configurations of reception and transmission. An assessment of the effects of bending and humidity has been demonstrated by placing the antenna on the human body. The specific absorption rate (SAR) value was tested to show the radiation effect on the human body, and it was found that its impact on the human body SAR value is 1.68 W/kg, which indicates the safer limit to avoid radiation effects. Therefore, the proposed method is promising for telemedicine and mobile health systems.

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