Distributed Multi-Antenna Positioning for Automatic-Guided Vehicle

Radio-based positioning systems are typically utilized to provide high-precision position information for automatic-guided vehicles (AGVs). However, the presence of obstacles in harsh environments, as well as carried cargoes on the AGV, will degrade the localization performance, since they block the propagation of radio signals. In this paper, a distributed multi-antenna positioning system is proposed, where multiple synchronous antennas are equipped on corners of an AGV to improve the availability and accuracy of positioning. An estimator based on the Levenberg–Marquardt algorithm is introduced to solve the nonlinear pseudo-range equations. To obtain the global optimal solutions, we propose a coarse estimator that utilizes the displacement knowledge of the antennas to provide a rough initial guess. Simulation results show a better availability of our system compared with the single antenna positioning system. Decimeter accuracy can be obtained under a Gaussian measurement noise with a standard deviation of 0.2 m. The results also demonstrate that the proposed algorithm can achieve positioning accuracy close to the theoretical Cramer–Rao lower bound. Furthermore, given prior information of the yaw angle, the same level of accuracy can be obtained by the proposed algorithm without the coarse estimation step.

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

Electronics ◽

10.3390/electronics10010002 ◽

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.

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A Low-Cost Visible Light Positioning System for Indoor Positioning

Sensors ◽

10.3390/s20185145 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5145 ◽

Cited By ~ 1

Author(s):

Juan Torres ◽

Aitor Montes ◽

Sandra Mendoza ◽

Pedro Fernández ◽

Juan Betancourt ◽

...

Keyword(s):

Visible Light ◽

Electromagnetic Interference ◽

Urban Areas ◽

Low Cost ◽

Visible Light Communication ◽

Indoor Positioning ◽

Positioning System ◽

Time To Market ◽

Position Information ◽

Positioning Systems

Currently, a high percentage of the world’s population lives in urban areas, and this proportion will increase in the coming decades. In this context, indoor positioning systems (IPSs) have been a topic of great interest for researchers. On the other hand, Visible Light Communication (VLC) systems have advantages over RF technologies; for instance, they do not need satellite signals or the absence of electromagnetic interference to achieve positioning. Nowadays, in the context of Indoor Positioning (IPS), Visible Light Positioning (VLP) systems have become a strong alternative to RF-based systems, allowing the reduction in costs and time to market. This paper shows a low cost VLP solution for indoor systems. This includes multiple programmable beacons and a receiver which can be plugged to a smartphone running a specific app. The position information will be quickly and securely available through the interchange between the receiver and any configurable LED-beacon which is strategically disposed in an area. The implementation is simple, inexpensive, and no direct communication with any data server is required.

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UWB/Binocular VO Fusion Algorithm Based on Adaptive Kalman Filter

Sensors ◽

10.3390/s19184044 ◽

2019 ◽

Vol 19 (18) ◽

pp. 4044

Author(s):

Qingxi Zeng ◽

Dehui Liu ◽

Chade Lv

Keyword(s):

Kalman Filter ◽

Measurement Data ◽

Ultra Wideband ◽

Positioning System ◽

Adaptive Kalman Filter ◽

Fusion Algorithm ◽

Position Information ◽

Positioning Systems ◽

Visual Odometer ◽

Navigation Accuracy

Among the existing wireless indoor positioning systems, UWB (ultra-wideband) is one of the most promising solutions. However, the single UWB positioning system is affected by factors such as non-line of sight and multipath, and the navigation accuracy will decrease. In order to make up for the shortcomings of a single UWB positioning system, this paper proposes a scheme based on binocular VO (visual odometer) and UWB sensor fusion. In this paper, the original distance measurement data of UWB and the position information of binocular VO are merged by adaptive Kalman filter, and the structural design of the fusion system and the realization of the fusion algorithm are elaborated. The experimental results show that compared with a single positioning system, the proposed data fusion method can significantly improve the positioning accuracy.

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Ultra Short Baseline (USBL) Calibration for Positioning of Underwater Objects

Al-Fiziya: Journal of Materials Science, Geophysics, Instrumentation and Theoretical Physics ◽

10.15408/fiziya.v2i2.12524 ◽

2019 ◽

Vol 2 (2) ◽

pp. 73-85

Author(s):

Bagus Septyanto ◽

Dian Nurdiana ◽

Sitti Ahmiatri Saptari

Keyword(s):

Acoustic Wave ◽

Scale Factor ◽

Positioning System ◽

Radio Waves ◽

Short Baseline ◽

Error Angle ◽

Satellite Navigation System ◽

The Earth ◽

Underwater Positioning ◽

Radio Signals

In general, surface positioning using a global satellite navigation system (GNSS). Many satellites transmit radio signals to the surface of the earth and it was detected by receiver sensors into a function of position and time. Radio waves really bad when spreading in water. So, the underwater positioning uses acoustic wave. One type of underwater positioning is USBL. USBL is a positioning system based on measuring the distance and angle. Based on distance and angle, the position of the target in cartesian coordinates can be calculated. In practice, the effect of ship movement is one of the factors that determine the accuracy of the USBL system. Ship movements like a pitch, roll, and orientation that are not defined by the receiver could changes the position of the target in X, Y and Z coordinates. USBL calibration is performed to detect an error angle. USBL calibration is done by two methods. In USBL calibration Single Position obtained orientation correction value is 1.13 ̊ and a scale factor is 0.99025. For USBL Quadrant calibration, pitch correction values is -1.05, Roll -0.02 ̊, Orientation 6.82 ̊ and scale factor 0.9934 are obtained. The quadrant calibration results deccrease the level of error position to 0.276 - 0.289m at a depth of 89m and 0.432m - 0.644m at a depth of 76m

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

Sensors ◽

10.3390/s21113701 ◽

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.

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Consistency of the Empirical Distributions of Navigation Positioning System Errors with Theoretical Distributions—Comparative Analysis of the DGPS and EGNOS Systems in the Years 2006 and 2014

Sensors ◽

10.3390/s21010031 ◽

2020 ◽

Vol 21 (1) ◽

pp. 31

Author(s):

Mariusz Specht

Keyword(s):

Normal Distribution ◽

Root Mean Square ◽

Statistical Tests ◽

Position Error ◽

Positioning System ◽

Navigation Systems ◽

Mean Square ◽

Positioning Systems ◽

Position Errors ◽

System Errors

Positioning systems are used to determine position coordinates in navigation (air, land and marine). The accuracy of an object’s position is described by the position error and a statistical analysis can determine its measures, which usually include: Root Mean Square (RMS), twice the Distance Root Mean Square (2DRMS), Circular Error Probable (CEP) and Spherical Probable Error (SEP). It is commonly assumed in navigation that position errors are random and that their distribution are consistent with the normal distribution. This assumption is based on the popularity of the Gauss distribution in science, the simplicity of calculating RMS values for 68% and 95% probabilities, as well as the intuitive perception of randomness in the statistics which this distribution reflects. It should be noted, however, that the necessary conditions for a random variable to be normally distributed include the independence of measurements and identical conditions of their realisation, which is not the case in the iterative method of determining successive positions, the filtration of coordinates or the dependence of the position error on meteorological conditions. In the preface to this publication, examples are provided which indicate that position errors in some navigation systems may not be consistent with the normal distribution. The subsequent section describes basic statistical tests for assessing the fit between the empirical and theoretical distributions (Anderson-Darling, chi-square and Kolmogorov-Smirnov). Next, statistical tests of the position error distributions of very long Differential Global Positioning System (DGPS) and European Geostationary Navigation Overlay Service (EGNOS) campaigns from different years (2006 and 2014) were performed with the number of measurements per session being 900’000 fixes. In addition, the paper discusses selected statistical distributions that fit the empirical measurement results better than the normal distribution. Research has shown that normal distribution is not the optimal statistical distribution to describe position errors of navigation systems. The distributions that describe navigation positioning system errors more accurately include: beta, gamma, logistic and lognormal distributions.

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Sensing Framework for the Internet of Actors in the Value Co-Creation Process with a Beacon-Attachable Indoor Positioning System

Sensors ◽

10.3390/s21010083 ◽

2020 ◽

Vol 21 (1) ◽

pp. 83

Author(s):

Keiichi Zempo ◽

Taiga Arai ◽

Takuya Aoki ◽

Yukihiko Okada

Keyword(s):

Radio Wave ◽

Indoor Positioning ◽

Measurement Range ◽

Pulse Interval ◽

Retail Stores ◽

Positioning System ◽

Radio Waves ◽

Positioning Systems ◽

Environment Recognition ◽

Indoor Positioning System

To evaluate and improve the value of a service, it is important to measure not only the outcomes, but also the process of the service. Value co-creation (VCC) is not limited to outcomes, especially in interpersonal services based on interactions between actors. In this paper, a sensing framework for a VCC process in retail stores is proposed by improving an environment recognition based indoor positioning system with high positioning performance in a metal shelf environment. The conventional indoor positioning systems use radio waves; therefore, errors are caused by reflection, absorption, and interference from metal shelves. An improvement in positioning performance was achieved in the proposed method by using an IR (infrared) slit and IR light, which avoids such errors. The system was designed to recognize many and unspecified people based on the environment recognition method that the receivers had installed, in the service environment. In addition, sensor networking was also conducted by adding a function to transmit payload and identification simultaneously to the beacons that were attached to positioning objects. The effectiveness of the proposed method was verified by installing it not only in an experimental environment with ideal conditions, but posteriorly, the system was tested in real conditions, in a retail store. In our experimental setup, in a comparison with equal element numbers, positioning identification was possible within an error of 96.2 mm in a static environment in contrast to the radio wave based method where an average positioning error of approximately 648 mm was measured using the radio wave based method (Bluetooth low-energy fingerprinting technique). Moreover, when multiple beacons were used simultaneously in our system within the measurement range of one receiver, the appropriate setting of the pulse interval and jitter rate was implemented by simulation. Additionally, it was confirmed that, in a real scenario, it is possible to measure the changes in movement and positional relationships between people. This result shows the feasibility of measuring and evaluating the VCC process in retail stores, although it was difficult to measure the interaction between actors.

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Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

Applied Sciences ◽

10.3390/app9061048 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1048 ◽

Cited By ~ 8

Author(s):

Huy Tran ◽

Cheolkeun Ha

Keyword(s):

Machine Learning ◽

Visible Light ◽

Noise Reduction ◽

Indoor Positioning ◽

Computational Time ◽

Dual Function ◽

Positioning System ◽

Positioning Accuracy ◽

Positioning Systems ◽

Machine Learning Classification

Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.

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UWB Positioning Systsem Design: Selection of Modulation and Multiple Access Schemes

Journal of Navigation ◽

10.1017/s037346330700450x ◽

2007 ◽

Vol 61 (1) ◽

pp. 45-62 ◽

Cited By ~ 1

Author(s):

Hui Yu ◽

Enrique Aguado ◽

Gary Brodin ◽

John Cooper ◽

David Walsh ◽

...

Keyword(s):

Multiple Access ◽

High Performance ◽

Ultra Wideband ◽

Good Time ◽

Modulation Scheme ◽

Positioning System ◽

Indoor Environments ◽

Signal Design ◽

Positioning Systems ◽

Design Selection

In densely-populated cities or indoor environments, limited visibility to satellites and severe multipath effects significantly affect the accuracy and reliability of satellite-based positioning systems. To meet the needs of “seamless navigation” in these challenging environments an advanced terrestrial positioning system is under development. This system is based upon Ultra-Wideband (UWB) technology, which is a promising candidate for this application due to good time domain resolution and immunity to multipath. This paper presents a detailed analysis of two key aspects of the UWB signal design that will allow it to be used as the basis of such a high performance positioning system: the modulation scheme and the multiple access technique. These two aspects are evaluated in terms of spectral efficiency and synchronisation performance over multipath channels. Thus this paper identifies optimal modulation and multiple access techniques for a long range, high performance terrestrial positioning system using UWB.

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An Indoor Positioning System Using Multiple Methods and Tools

International Journal of Information Communication Technology and Applications ◽

10.17972/ijicta20184134 ◽

2018 ◽

Vol 4 (1) ◽

pp. 11-22

Author(s):

Michael Adeyeye Oshin ◽

Nobaene Sehloho

Keyword(s):

Wireless Mesh Network ◽

Ad Hoc ◽

Indoor Positioning ◽

Mesh Network ◽

Positioning System ◽

Tracking Systems ◽

Positioning Systems ◽

Wireless Mesh ◽

Positioning Method ◽

Indoor Positioning System

With many different studies showing a growing demand for the development of indoor positioning systems, numerous positioning and tracking methods and tools are available for which can be used for mobile devices. Therefore, an interest is more on development of indoor positioning and tracking systems that are accurate and effective. Presented and proposed in this work, is an indoor positioning system. As opposed to an Ad-hoc Positioning System (APS), it uses a Wireless Mesh Network (WMN). The system makes use of an already existing Wi-Fi infrastructure technology. Moreover, the approach tests the positioning of a node with its neighbours in a mesh network using multi-hopping functionality. The positioning measurements used were the ICMP echos, RSSI and RTS/CTS requests and responses. The positioning method used was the trilateral technique, in combination with the idea of the fingerprinting method. Through research and experimentation, this study developed a system which shows potential as a positioning system with an error of about 2 m to 3 m. The hybridisation of the method proves an enhancement in the system though improvements are still required.

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