scholarly journals Calculation of Weighted Geometric Dilution of Precision

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Chien-Sheng Chen ◽  
Yi-Jen Chiu ◽  
Chin-Tan Lee ◽  
Jium-Ming Lin
Keyword(s):  
Communication Systems ◽  
Approximation Error ◽  
Matrix Inversion ◽  
Location Estimation ◽  
Inversion Method ◽  
Error Statistics ◽  
Positioning Systems ◽  
Dilution Of Precision ◽  
Measurement Units ◽  
Geometric Dilution Of Precision

To achieve high accuracy in wireless positioning systems, both accurate measurements and good geometric relationship between the mobile device and the measurement units are required. Geometric dilution of precision (GDOP) is widely used as a criterion for selecting measurement units, since it represents the geometric effect on the relationship between measurement error and positioning determination error. In the calculation of GDOP value, the maximum volume method does not necessarily guarantee the selection of the optimal four measurement units with minimum GDOP. The conventional matrix inversion method for GDOP calculation demands a large amount of operation and causes high power consumption. To select the subset of the most appropriate location measurement units which give the minimum positioning error, we need to consider not only the GDOP effect but also the error statistics property. In this paper, we employ the weighted GDOP (WGDOP), instead of GDOP, to select measurement units so as to improve the accuracy of location. The handheld global positioning system (GPS) devices and mobile phones with GPS chips can merely provide limited calculation ability and power capacity. Therefore, it is very imperative to obtain WGDOP accurately and efficiently. This paper proposed two formations of WGDOP with less computation when four measurements are available for location purposes. The proposed formulae can reduce the computational complexity required for computing the matrix inversion. The simpler WGDOP formulae for both the 2D and the 3D location estimation, without inverting a matrix, can be applied not only to GPS but also to wireless sensor networks (WSN) and cellular communication systems. Furthermore, the proposed formulae are able to provide precise solution of WGDOP calculation without incurring any approximation error.

Dilution of Position Calculation for Accuracy Improvement in Wireless Location Systems

2011 ◽  
Vol 204-210 ◽  
pp. 1036-1040
Author(s):  
Yung Chuan Lin ◽  
Chien Sheng Chen ◽  
He Nian Shou ◽  
Chi Tien Sun
Keyword(s):  
Communication Systems ◽  
Selection Criterion ◽  
Matrix Inversion ◽  
Base Stations ◽  
Inversion Method ◽  
Wireless Location ◽  
The Matrix ◽  
Measurement Units ◽  
Geometric Dilution Of Precision ◽  
Volume Method

Geometric dilution of precision (GDOP) represents the geometric effect on the relationship between measurement error and positioning determination error. In the calculation of GDOP value, the maximum volume method does not guarantee the optimal selection of the four measurement units. The conventional method for calculating GDOP is to use matrix inversion to all subsets. In this paper, we employ GDOP using the matrix inversion method to select appropriate base stations (BSs) in cellular communication systems. The proposed BS selection criterion performs better than the random subsets of four or five BSs chosen from all seven BSs. The performances of MS location strongly depend on the relative position of the MS and BSs. Therefore, it is very important to select a subset with the most appropriate BSs rapidly and reasonably before positioning.

scholarly journals Neural Network for WGDOP Approximation and Mobile Location

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Chien-Sheng Chen ◽  
Jium-Ming Lin ◽  
Chin-Tan Lee
Keyword(s):  
Neural Network ◽  
Communication Systems ◽  
Learning Algorithm ◽  
Location Estimation ◽  
Estimation Accuracy ◽  
Neural Network Learning ◽  
Mobile Location ◽  
Positioning Systems ◽  
Geometric Dilution Of Precision ◽  
Location Methods

This paper considers location methods that are applicable in global positioning systems (GPS), wireless sensor networks (WSN), and cellular communication systems. The approach is to employ the resilient backpropagation (Rprop), an artificial neural network learning algorithm, to compute weighted geometric dilution of precision (WGDOP), which represents the geometric effect on the relationship between measurement error and positioning error. The original four kinds of input-output mapping based on BPNN for GDOP calculation are extended to WGDOP based on Rprop. In addition, we propose two novel Rprop–based architectures to approximate WGDOP. To further reduce the complexity of our approach, the first is to select the serving BS and then combines it with three other measurements to estimate MS location. As such, the number of subsets is reduced greatly without compromising the location estimation accuracy. We further employed another Rprop that takes the higher precision MS locations of the first several minimum WGDOPs as the inputs into consideration to determine the final MS location estimation. This method can not only eliminate the poor geometry effects but also significantly improve the location accuracy.

Personnel Real Time Tracking in Hazardous Areas Using Wearable Technologies and Machine Learning

2021 ◽  
Author(s):  
Tareq Aziz AL-Qutami ◽  
Fatin Awina Awis
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Communication Systems ◽  
Average Distance ◽  
Location Estimation ◽  
Machine Learning Algorithms ◽  
Contact Tracing ◽  
Localization Algorithm ◽  
Positioning Systems ◽  
Wearable Technologies

Abstract Real-time location information is essential in the hazardous process and construction areas for safety and emergency management, security, search and rescue, and even productivity tracking. It's also crucial during pandemics such as the COVID-19 pandemic for contact tracing to isolate those who came to the proximity of infected individuals. While global positioning systems (GPS), can address the demand for location awareness in outdoor environments, another accurate location estimation technology for indoor environments where GPS doesn't perform well is required. This paper presents the development and deployment of an end-to-end cost-effective real-time personnel location system suitable for both indoor and outdoor hazardous and safe areas. It leverages on facility wireless communication systems, wearable technologies such as smart helmets and wearable tags, and machine learning. Personnel carries the client device which collects location-related information and sends it to the localization algorithm in the cloud. When the personnel moves, the tracking dashboard shows client location in real-time. The proposed localization algorithm relies on wireless signal fingerprinting and machine learning algorithms to estimate the location. The machine learning algorithm is a mix of clustering and classification that was designed to scale well with bigger target areas and is suitable for cloud deployment. The system was tested in both office and industrial process environments using consumer-grade handphones and intrinsically safe wearable devices. It achieved an average distance error of less than 2 meters in 3D space.

scholarly journals Inversion of selected structures of block matrices of chosen mechatronic systems

2016 ◽  
Vol 64 (4) ◽  
pp. 853-863
Author(s):  
T. Trawiński ◽  
A. Kochan ◽  
P. Kielan ◽  
D. Kurzyk
Keyword(s):  
Matrix Inversion ◽  
Block Matrix ◽  
Inversion Method ◽  
Mass Storage ◽  
Mechatronic Systems ◽  
Positioning Systems ◽  
Block Matrices ◽  
Storage Devices ◽  
Gaussian Method ◽  
Gauss Method

AbstractThis paper describes how to calculate the number of algebraic operations necessary to implement block matrix inversion that occurs, among others, in mathematical models of modern positioning systems of mass storage devices. The inversion method of block matrices is presented as well. The presented form of general formulas describing the calculation complexity of inverted form of block matrix were prepared for three different cases of division into internal blocks. The obtained results are compared with a standard Gaussian method and the “inv” method used in Matlab. The proposed method for matrix inversion is much more effective in comparison in standard Matlab matrix inversion “inv” function (almost two times faster) and is much less numerically complex than standard Gauss method.

scholarly journals A Geometric Layout Method for Synchronous Pseudolite Positioning Systems Based on a New Weighted HDOP

2021 ◽  
Vol 10 (9) ◽  
pp. 601
Author(s):  
Xinyang Zhao ◽  
Qiangqiang Shuai ◽  
Guangchen Li ◽  
Fangzhou Lu ◽  
Bocheng Zhu
Keyword(s):  
Indoor Environment ◽  
Simulated Annealing Algorithm ◽  
Time Synchronization ◽  
Simple Calculation ◽  
Positioning Systems ◽  
Dilution Of Precision ◽  
The Monte Carlo Method ◽  
Connection System ◽  
Geometric Dilution Of Precision ◽  
Geometric Layout

The positioning accuracy of a ground-based system in an indoor environment is closely related to the geometric configuration of pseudolites. This paper presents a simple closed-form equation for computing the weighted horizontal dilution of precision (WHDOP) with four eigenvalues, which can reduce the amount of calculation. By comparing the result of WHDOP with traditional matrix inversion operation, the effectiveness of WHDOP of the proposed simple calculation method is analyzed. The proposed WHDOP has a linear relationship with the actual static positioning result error in an indoor environment proved by the Pearson analysis method. Twenty positioning points are randomly selected, and the positioning variance and WHDOP of each positioning point have been calculated. The correlation coefficient of WHDOP and the positioning variance is calculated to be 0.82. A pseudolite system layout method based on a simulated annealing algorithm is proposed by using WHDOP, instead of Geometric dilution of precision (GDOP). In this paper, the constraints of time synchronization are discussed. In wireless connection system, the distance between master station and slave station should be kept within a certain range. Specifically, for a given indoor scene, many positioning target points are randomly generated in this area by using the Monte Carlo method. The mean WHDOP value of all positioning points corresponding to the synchronous pseudolite layout is used as the objective function. The results of brute force search are compared with the method, which proves the accuracy of the new algorithm.

scholarly journals GEOMETRIC DILUTION OF PRECISION OF THE GNSS FOR MARS (GNSS FATIMA)

Aviation ◽  
2016 ◽  
Vol 20 (4) ◽  
pp. 183-190
Author(s):  
Jozef KOZAR ◽  
Stanislav DURCO ◽  
Frantisek ADAMCIK
Keyword(s):  
Satellite System ◽  
Navigation Satellite ◽  
Positioning Systems ◽  
Dilution Of Precision ◽  
Positioning Errors ◽  
Factors Influencing ◽  
Main Factors ◽  
Geometric Dilution Of Precision ◽  
Global Navigation Satellite ◽  
Critical Aspects

Positioning on Mars is one of the critical aspects of every planetary mission. Current complex planetary exploration systems (orbital and surface) rely on complex navigation and positioning systems, which make these systems complicated, expensive and their missions dangerous. The project of the global navigation satellite system for Mars (proposed system name – FATIMA) can make this and even future manned missions more safe, less expensive and the whole positioning in real time more reliable. The GNSS can be used by more systems or users simultaneously. In this research paper we focus on possible positioning errors when such a system is used. This research is focused on the GDOP – Geometric Dilution of Precision as one of the main factors influencing the GNSS.

scholarly journals Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 798
Author(s):  
Hamed Darbandi ◽  
Filipe Serra Bragança ◽  
Berend Jan van der Zwaag ◽  
John Voskamp ◽  
Annik Imogen Gmel ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Biomechanical Analysis ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Motion Patterns ◽  
Positioning Systems ◽  
Machine Learning Approach ◽  
Global Positioning ◽  
Measurement Units

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

scholarly journals Non-Line-of-Sight Error Mitigation in Wireless Communication Systems

2011 ◽  
Vol 1 ◽  
pp. 173-177
Author(s):  
Szu Lin Su ◽  
Yi Wen Su ◽  
Ho Nien Shou ◽  
Chien Sheng Chen
Keyword(s):  
Communication Systems ◽  
Hybrid Methods ◽  
Mobile Station ◽  
Base Stations ◽  
Superior Performance ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Angle Of Arrival ◽  
Error Statistics ◽  
Non Line Of Sight

When there is non-line-of-sight (NLOS) path between the mobile station (MS) and base stations (BSs), it is possible to integrate many kinds of measurements to achieve more accurate measurements of the MS location. This paper proposed hybrid methods that utilize time of arrival (TOA) at five BSs and angle of arrival (AOA) information at the serving BS to determine the MS location in NLOS environments. The methods mitigate the NLOS effect simply by the weighted sum of the intersections between five TOA circles and the AOA line without requiring priori knowledge of NLOS error statistics. Simulation results show that the proposed methods always give superior performance than Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP).

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