An Improved Cellular positioning technique based on Database Correlation (original) (raw)
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On the Accuracy Improvement Issues in GSM Location Fingerprinting
IEEE Vehicular Technology Conference, 2006
Determining the position of mobile users in GSM networks has become more and more important. Such services as emergency calls and other location dependent services have been of great importance in the last years. A key factor for the success of any localization technology is its accuracy. This work is focused on localization in a dense urban scenario or in any other area where the GPS signal is not available or its error is very big due to some obstruction of the satellites. Different methods such as those based on neural network localization, database correlation, dead reckoning and a tracking algorithm in case of user mobility have been examined in this work in order to find the optimal in terms of accuracy. The pre-processing of the received signal strengths (rss) is performed to reduce the positioning error due to the rssstochastic behaviour. Results show that, a tracking algorithm using NN positioning results and an extended Kalman filter (EKF) supplies better results in case of mobility of the user.
Database Correlation for GSM Location in Outdoor & Indoor Environments
2008 4th International Conference on Information and Automation for Sustainability, 2008
Estimation of the location of a Mobile Station accurately is a key requirement to effectively provide a wide range of Location Based Services over mobile networks. Hence developing cellular positioning techniques has been a key research problem, with numerous localization solutions been proposed. These include technologies such as Cell ID, angle and time of arrival methods, statistical methods and fingerprinting methods. This paper presents fingerprinting based positioning techniques suitable for outdoor and indoor positioning. Multiple positioning techniques were proposed, implemented and evaluated with outdoor and indoor trials. The ultimate solution proposed in this paper is not a single positioning technique; rather it presents several positioning techniques that achieve optimum performance in each test environment. The results reports 67% positioning error as 112 m, 299 m and 221 m for urban, suburban and rural areas respectively. Experimental results show that the proposed positioning methods achieve accuracy far better than Cell-ID and trilateration approaches for the tested network environments especially for rural areas. The 67% positioning error for rural area is 1045 m and 1386 m with basic Cell-ID and trilateration techniques while proposed fingerprinting based technique reports 67% positioning error as 221m. With indoor positioning this paper reports 50% positioning error as 8.7 m and also it was possible to accurately differentiate between floors in the selected multi storey building.
Database Correlation for GSM Location in Outdoor & Indoor Environments
2008 4th International Conference on Information and Automation for Sustainability, 2008
Estimation of the location of a Mobile Station accurately is a key requirement to effectively provide a wide range of Location Based Services over mobile networks. Hence developing cellular positioning techniques has been a key research problem, with numerous localization solutions been proposed. These include technologies such as Cell ID, angle and time of arrival methods, statistical methods and fingerprinting methods. This paper presents fingerprinting based positioning techniques suitable for outdoor and indoor positioning. Multiple positioning techniques were proposed, implemented and evaluated with outdoor and indoor trials. The ultimate solution proposed in this paper is not a single positioning technique; rather it presents several positioning techniques that achieve optimum performance in each test environment. The results reports 67% positioning error as 112 m, 299 m and 221 m for urban, suburban and rural areas respectively. Experimental results show that the proposed positioning methods achieve accuracy far better than Cell-ID and trilateration approaches for the tested network environments especially for rural areas. The 67% positioning error for rural area is 1045 m and 1386 m with basic Cell-ID and trilateration techniques while proposed fingerprinting based technique reports 67% positioning error as 221m. With indoor positioning this paper reports 50% positioning error as 8.7 m and also it was possible to accurately differentiate between floors in the selected multi storey building.
Location Fingerprinting in GSM network and Impact of Data Pre-processing
Different techniques for locating the user terminal in cellular networks have been developed in the recent years. This paper proposes a method which is designed to obtain optimal performance in urban environments. Such performance metrics as accuracy, cost, reliability, scalability, and demanding effort are the key points in this paper. A fingerprint method is used for some of these reasons: firstly, no additional hardware is required for it implementation to existing networks. Secondly, compared to other methods, this one performs better in areas with significant multipath propagation. Due to the big demanding effort during radio signal strength (RSS) collection from the streets to realize a fingerprint database, predicted RSS data of the concerned area are rather used. The high sensitivity of the RSS to the environmental changes is taken into account. A system is developed which calibrates the predicted RSS on basis of a sample real collected RSS. A robust neural network (NN) architecture and training algorithm are used in the positioning unit in order to get a good mapping between locations and RSS.
International Journal of Electrical and Computer Engineering (IJECE), 2021
The rapid exponential growth in wireless technologies and the need for public safety has led to increasing demand for location-based services. Terrestrial cellular networks can offer acceptable position estimation for users that can meet the statutory requirements set by the Federal Communications Commission in case of network-based positioning, for safety regulations. In this study, the proposed radio frequency pattern matching (RFPM) method is implemented and tested to determine a user's location effectively. The RFPM method has been tested and validated in two different environment. The evaluations show remarkable results especially in the Micro cell scenario, at 67% of positioning error 15m and at 90% 31.78m for Micro cell scenario, with results of 75.66m at 67% and 141.4m at 90% for Macro cell scenario.
Localization in Real GSM Network with Fingerprinting Utilization
2010
This paper attempts to present current state in the area of user localization in cellular networks and shows custom solution for positioning using pocket computer and fingerprint method also known as fingerprinting. It operates in Global System for Mobile communications (GSM) network, although fingerprinting is also applicable in other wireless networks, such as Universal Mobile Telecommunications System (UMTS), Bluetooth or 802.11. Implementation is explained and it is compared to existing solutions. The performance of the system is evaluated for various scenarios by statistical characteristics and Circular Error Probability (CEP). The scenarios are proposed from observation of various parameters that influence the localization accuracy.
A Low-cost Fingerprint Positioning System in Cellular Networks
2007 Second International Conference on Communications and Networking in China, 2007
An ultimate aim of mobile positioning research is to find a method providing high estimation accuracy to the user within minimum delay and at minimum cost. Conventional location techniques based on trilateration and triangulation rely on line-of-sight path between the base station antenna and the mobile unit. In densely built urban areas, this assumption is rarely valid. This fact degrades the location performance of the conventional techniques and motivates the need for development of more accurate technique suited for these areas. Positioning system developed in this research is divided into three sub-systems. The first sub-system solves the problems related to fingerprint localization and involves neural network as key element of the positioning algorithm. The postprocessing tasks which include tracking and map-matching are performed in the second and third sub-systems respectively.
Mobile Positioning With the Aid of Network Planning Tool
This paper presents the results of work carried out to apply and evaluate the use of network planning tool predictions to the Database Correlation Method (DCM), for location estimation in cellular environments. In Database Correlation Method (DCM), the location dependent signal information seen by a mobile station is stored in a database in a form of fingerprints. During the location estimation, the measured signal sample is compared with the fingerprints stored in the database to come up with an accurate estimation. Even though the method has proven superior in accuracy over other cellular positioning techniques, the higher demanding effort involved in database formation using field measurements has become a big challenge which prevents the technique being deployed in large dynamic networks. Formation of a database using the predictions from the network planning tools or theoretical propagation models would be a possible solution for the issue. Then, the motivation arises to evaluate the accuracy of the method using predicted fingerprints instead of measured ones. During this research, the fingerprint database is created from the signal strength predictions of a network planning tool for the urban environment in Sri Lanka and the accuracy of the DCM using predicted database has been evaluated comparison with that obtained using a measured database. Further, different matching algorithms are developed, and their performance is evaluated. The results demonstrate that the penalty of using a predicted database is small in comparison to the advantage it gives in the practical implementation of such a system.
Design, implementation & testing of positioning techniques in mobile networks
2007 Third International Conference on Information and Automation for Sustainability, 2007
This paper illustrates two techniques for improved estimation of the location of Mobile Stations (MS) in cellular Networks. The first approach is the statistical method in which signal properties are treated as random variables which are statistically dependent on the location of the transmitter and the receiver. Location estimation for a set of observed signal strengths at a specific location is done as an inference problem. In the second approach, Database Correlation, signal information seen by an MS is stored in the form of fingerprints. To estimate the location, difference between the measurement and the database fingerprints is calculated using a correlation algorithm. Database fingerprints with least difference are selected as the nearest fingerprints for the location to be estimated. The two techniques have been tested through extensive measurements in two environments, urban and sub urban, to verify their performances. Two different implementations have been developed and presented for the statistical technique examined through simulations in the literature. In the implementation of database correlation method, different correlation algorithms and measurement conditions are proposed, implemented and tested. In addition, the accuracies of two techniques have been presented compared to the simple geometrical method for position estimation.
A new technique using signal correlation of one node b to estimate mobile location
2008
System (UIPS) project, Location Determining Techniques were developed to estimate mobile user's location (position) in 2G, 3G and beyond networks. Timing techniques were developed to improve accuracy of estimating mobile user's location when measurements (such as time differences of arrival) are available from 2 and more than 2 Node B (base station). In this extension of study, a technique called Signal Correlation Method (SCM), based on Artificial Neural Network is introduced when measurements are only available from one serving Node B. Received signals collected during drive test (survey) are stored in UIPS servers. Mobile user's current receive signal is compared to the stored signals to estimate user's location. Technique to match stored received signals from many base stations' (cells) to current user's received signals is referred to as fingerprinting technique. However, SCM technique only uses one Node B's signal measurement for location estimation and produces simulation results that meet FCC E-911 location accuracy requirements for network based positioning.