English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Access to the right information anytime, anywhere is becoming the new driving force for the information technology revolution. The 'right' information's relevance is based on the user's profile and his/her current geographical position and/or time. Location Based Service (LBS) is an innovative technology that provides information or makes information available based on the geographical location of the mobile user. Analysts predict that LBSs will lead to new applications, generating billions of US dollars worldwide (Leite, 2001; Searle, 2001). The need for an efficient and accurate mobile station (MS) positioning system is growing day by day. The ability to pinpoint the location of an individual has an obvious and vital value in the context of emergency services (Chan, 2003; Olama et al., 2008). Pinpointing the location of people and other valuable assets also opens the door to a new world of previously unimagined information services and m-commerce probabilities. For example, availability of services like 'Where is the nearest ATM?', 'Check traffic conditions on the highway on my route', 'Find a parking lot nearby', as well as answers to 'Where is my advisor?', and 'Where is my car?' will be an everyday rule in our lives (Charalambous & Panayiotou, 2004). A technologymore » independent LBS architecture can be considered as comprised by three main parts (Girodon, 2002): A user requesting information, a mobile network operator and its partners, and several content providers (e.g. data, maps). The subscriber requests a personalized service dependant on his geographic location. The system will ask the Location Services Manager (which is in charge of handling requests, i.e., send/receive to the Location Calculator and the Content Providers) to pinpoint the location of the mobile. The Location Services Manager (LSM), using the Location Calculator, will ask the Content Provider (CP) to supply qualified information according to the mobile's geographical position. The LSM will eventually receive the answer from the CP and send it to the mobile, performing the essential data translations. Fig. 1 outlines the precedent concept. For effective provision of LBS, one has to provide an accurate location, as well as suitable information for users required by the corresponding service, with minimal expenditure. Thus, there are three main technology issues that have to be resolved for LBS: positioning technology, application technology, and location services (Dru & Saada, 2001). A very important technology is of course the positioning technology, the way to find out the location of a mobile device accurately. Due to the unique characteristics of the cellular environment, it is a great challenge to locate the user precisely. However, in many cases, application technology and location services are important consideration of LBS. Application technology manages the geographic information and delivers the customer requests to the appropriate service provider, thus it constitutes the communication system involved. LBS uses the geographic information to provide geographically sensitive information and services. Location-based applications and services are not sensitive to the type of location technology that is used - they merely rely on reasonably accurate geographic coordinates (Chan, 2003). This chapter is structured as follows: In Section 2, we describe the use and applications of LBSs. The current location determination technologies and standards are presented in Section 3. In Section 4 we describe the mathematical models used for the location and velocity estimation algorithms. An initial attempt for MS location estimation via received signal level using the maximum likelihood estimation (MLE) approach and triangulation is presented in Sections 5. Since the former approach lacks acceptable accuracy for demanding services as numerical results reveal, the extended Kalman filter (EKF) approach, which is the main topic in this chapter, is introduced in Sections 6. In Section 7 we present numerical results. Section 8 provides concluding remarks.« less

Position and Velocity Tracking in Cellular Networks Using the Kalman Filter