Junyang Zhou

Providing location services within a radio cellular network using ellipse propagation model

Mobile positioning is becoming an important service on radio cellular network. Among different kind of location estimation technologies, the one, which estimates the location of mobile stations using signal strength is able to be applied to different kinds of cellular network, and therefore, is more general. We have designed a directional propagation model - the ellipse propagation model (EPM), which makes use of a wave propagation model to perform location estimation. The EPM enhanced the traditional propagation model by resembling the contour line of signal strength as an ellipse rather than a circle and hence becoming more realistic. We have tested the EPM with real data taken in Hong Kong and it is proven that the EPM out performing other existing location estimation algorithms in different kinds of terrains.

Enhanced fingerprint-based location estimation system in wireless LAN environment

Received Signal Strength (RSS) is one of the most useful information used for location estimation in Wireless LAN (WLAN). Most of the proposed WLAN positioning systems obtain RSS from either the Access Point or from the Mobile Device, but there are few researches that make use of the RSS obtained from both Access Points and Mobile Devices to perform location estimation. In this paper, we propose a new WLAN positioning system which makes use of the RSS collected from both the Access Points and Mobile Device. Our experimental result shows that the performance of our system is enhanced more than 23%, as compares to the traditional fingerprint-based WLAN positioning system which uses either RSS information obtained at Access Points or Mobile Device exclusively.

Enhancing Indoor Positioning Accuracy by Utilizing Signals from Both the Mobile Phone Network and the Wireless Local Area Network

Indoor positioning technology and its accuracy are crucial research topics for ubiquitous computing. While the GSM-based approach has always been used to provide outdoor positioning to compensate the lost of GPS in urban area, we seldom see systems that utilize the GSM-based approach for indoor positioning. On the other hand, the WLAN-based approach is widely used to provide indoor positioning service. However, with its Ad hoc layout and signal fluctuation, it is hard to provide a good performance based on the WLAN-based approach. In this paper, we develop an indoor positioning system that makes use of both GSM and WLAN signals to do location estimation such that the resultant system is more accurate and more stable and thus enhancing the performance of the whole system. Experimental results show that our system is stable and can reach centimeter-level accuracy, which outperforms other existing indoor positioning systems that utilizes a single network only.

An improved ellipse propagation model for location estimation in facilitating ubiquitous computing

Positioning is a crucial technology for ubiquitous computing. A directional propagation model - the ellipse propagation model (EPM) is proposed by our research group for locating a mobile station (MS) within a radio cellular network with an accuracy that can enable a number of location based services to realize ubiquitous computing. By using a geometric algorithm, the location of the mobile station can be estimated. However, since one parameter in our geometric algorithm is fixed, errors may be induced as the surrounding environment changes. In view of this, we would like to propose a new algorithm - the iterative algorithm to provide the positioning based on EPM. With the technical support of two local mobile phone operators, we have conducted a series of experiments using real data and experiment results showed that the proposed iterative algorithm outperforms the geometric algorithm by a good margin of 18% in terms of average error.

A Train-Once Approach for Location Estimation Using the Directional Propagation Model

Location estimation that is based on the mobile phone network has drawn considerable attention in the field of wireless communications. Among the different mobile location estimation methods, the one that estimates a mobile station location with reference to a wave propagation model is shown to be effective and is applicable to different kinds of cellular networks, including Global System for Mobile Communications (GSM), cdmaOne, CDMA2000, and the Universal Mobile Telecommunications System. We have designed a train-once approach for location estimations using the directional propagation model (DPM). The DPM is an improved model that is based on the traditional free-space wave propagation model with the directional gain and environmental factors integrated in the estimation. The train-once approach works because we observe that different types of antennas are designed for different types of environments. Thus, a parameter estimation is related to the antenna type and, in turn, related to the environment. In this paper, we report our study of the train-once approach with the DPM for location estimations. We have tested our model with 192 177 sets of real-life data that have been collected from a major mobile phone operator in Hong Kong. Experimental results show that the train-once approach with the DPM is practical and outperforms the existing location estimation algorithms in terms of accuracy, stability among different types of terrains, and success rates.

A Data Fusion Approach to Mobile Location Estimation based on Ellipse Propagation Model within a Cellular Radio Network

Mobile location estimation is drawing considerable attention in the field of wireless communications. In this paper, we present a new estimator which considers all the information to reduce the effect of signal fluctuation and fading-the statistical estimation. The Statistical Estimation is derived from the information of the received signal strengths (RSSs) and the locations of their corresponding base stations (BSs) and then estimates the location of the mobile station (MS). The statistical estimation uses all the information to provide the estimation of the location of the MS, which can provide an accurate estimation and reduce the effect of signal fluctuation and fading. It is a data fusion method to handle the signal fluctuation and fading problem. We test our approach with real data collected from Hong Kong. Experimental results show that our approach outperforms other existing location estimation algorithms among different kinds of terrains. The improvements based on the geometric algorithm with EPM and the iterative algorithm with EPM are 18.87% and 4.46%, respectively.

Using the ellipse propagation model for mobile location estimation

Mobile location estimation or mobile positioning is becoming an important service for a mobile phone network. Among different kinds of mobile location estimation technologies, only the class of the signal strength based algorithm which estimates the location of Mobile Stations (MSs) by the Received Signal Strength (RSS) can be applied to different kinds of radio cellular networks, and therefore, is more general. We have designed a directional propagation model – the Ellipse Propagation Model (EPM), which makes use of a common signal propagation model to perform a location estimation. EPM enhanced the traditional propagation model by resembling the contour line of the signal strength as an ellipse rather than a circle and hence becoming more realistic. We have tested EPM with real data taken in Hong Kong and it is proven that EPM outperforms other existing location estimation algorithms among different kinds of terrains.

A Probabilistic Approach to Mobile Location Estimation within Cellular Networks

Mobile location estimation is becoming an important value added service for a mobile phone network. It is well-known that GPS can provide an accurate location estimation. But it is also a known fact that GPS does not perform well in urban areas like downtown New York and cities like Hong Kong. Many mobile location estimation approaches based on cellular networks have been proposed to compensate the problem of the lost of GPS signals in providing location services to mobile users in metropolitan areas. Among different kinds of mobile location estimation technologies, only the class of signal strength based algorithm which estimates the location of the Mobile Station (MS) by signal strength received from the nearly Base Stations (BSs) can be applied to different kinds of cellular networks, and therefore, it is a more general solution. In this paper, we design a directional propagation model, the Modified Directional Propagation Model (MDPM), which makes use of a common signal propagation model to perform location estimation. We test MDPM with real data taken in Hong Kong and experimental results show that MDPM outperforms other existing location estimation algorithms among different kinds of terrains and environmental factors.

Using LDA Method to Provide Mobile Location Estimation Services within a Cellular Radio Network

Mobile location estimation is becoming an important value-added service for a mobile phone operator. It is well-known that GPS can provide an accurate location estimation. But it is also a known fact that GPS does not perform well in urban areas like downtown New York and cities like Hong Kong. Then many mobile location estimation approaches based on the cellular radio networks have been proposed to compensate the problem of the lost of GPS signals for providing location services to mobile users in metropolitan areas, but there exists no general solution since each algorithm has its own advantage depending on specific terrain and environmental factors. In this paper, we propose a selector method with LDA among different kinds of mobile location estimation algorithms we had proposed in previous work to combine their merits, then provide a more accurate estimation for location services. And we build up a three-level binary decision tree to classify these four algorithms. These three levels are named as Stat-Geo level, CG-nonCG level and CT-EPM level. And the success ratios of these three levels are 85.22%, 88.45% and 88.89% respectively. We have tested our selector method with real data taken in Hong Kong and the experiment results have shown that our selector method outperforms other existing location estimation algorithms among different kinds of terrains.

Algorithm Selectors for Providing Location Estimation Services within a Cellular Radio Network

Mobile location estimation is becoming an important value-added service for mobile phone operators. Many mobile location estimation algorithms based on the cellular radio networks have been proposed but there exists no general solution since each algorithm has its own advantage depending on specific terrain and environmental factors. In this paper, we propose and investigate three algorithm selectors, one with a LDA classifier and the other two with Bayes classifiers using either a Naive Bayes probabilistic model or a Bayes probabilistic model, to select the best mobile location estimation algorithms from our previous work in order to combine their merits, and provide a more accurate estimation for location services. We have tested these three algorithm selectors with real data taken in Hong Kong. Experiment results have shown that they are all useful in particular, and the one with a Bayes probabilistic model outperforms all other existing location algorithms among different kinds of terrains in terms of average errors