With the spreading of smartphones, location based services like GPS and GLONASS are becoming popular in recent years. These services detect the user equipment with the help three or more satellite’s .Applications are require for positioning technique not only outdoors but also in indoor environments.
As most of the current applications depend on GPS and the quality of GPS indoor positioning is still poor because GPS and GLONASS can’t work without a direct visible of sky. The two major problems of current indoor positioning technologies are accuracy and cost. A new method is becoming popular in indoor positioning that is Wi-Fi Positioning System (WPS), it is used where GPS is inadequate due to various causes including multipath and signal blockage indoors. Wi-Fi positioning takes advantage of the rapid growth in the early 21st century of wireless access points in urban areas. It is now widely acknowledged, however, that Google, Apple, and various phone makers and carriers have compiled their own very extensive databases of Wi-Fi access point locations by correlating Wi-Fi access points with GPS locations of cell phone, smartphone, and in some cases, tablet computer users. Anonymously determining users' location in this way is part of virtually every cell phone terms-of-service agreement, though most phones allow the user to turn off location services.[gps.about]. WPS may be combined with cell phone tower triangulation and GPS to provide reliable and accurate position data under a wide range of conditions, including among tall buildings and indoors, when GPS signals may be weak or intermittent.
The data is needed to be collected for location based service and is collected by cell towers and wifi access points in variety of ways, including UE information. These data is aggregated and store in public domain. This requires installation of access points which supply their location information, which is also costly. On the other hand, cellular networks have a wide coverage on the scale of kilometers and can serve a whole building. However, measurement accuracy based on cellular signals is not high enough to locate which shop or restaurant a person is in, and does not meet the requirements of most indoor applications[google].
There are two typical indoor positioning approaches, Propagation-based and Location Fingerprinting (LF) based. Propagation-based approaches estimate the position by measuring the received signal strength (RSS) with path loss. The drawbacks of these approaches lie in the requirement to have a strong Wi-Fi coverage in order to compute every condition that the signal can blend. The LF-based approaches such as locate a device by comparing its coordinates with the received signal strengths (RSSs) in a pre-recorded database. The drawbacks of these approaches are highly affected by internal building infrastructure changes, presence of humans, and interference among other devices. All these lead to unstable Wi-Fi coverage and inaccurate localization[main].
The fingerprinting technique is relatively simple to deploy compared to the other techniques such as angle-of arrival (AOA) and time-of-arrival (TOA). Moreover, there is no specialized hardware required at the mobile station (MS). Any existing wireless LAN infrastructure can be reused for this positioning system. The deployment of fingerprinting based positioning systems can be divided into two phases. First, in the offline phase, the location fingerprints are collected by performing a site-survey of the received signal strength (RSS) from multiple access points (APs). The entire area is covered by a rectangular grid of points. The RSS is measured with enough statistics to create a database or a table of predetermined RSS values on the points of the grid. The vector of RSS values at a point on the grid is called the location fingerprint of that point. Second, in the on-line phase, a MS will report a sample measured vector of RSSs from different APs to a central server (or a group of APs will collect the RSS measurements from a MS and send it to the server). The server uses an algorithm to estimate the location of the MS and reports the estimate back to the MS (or the application requesting the position information). The most common algorithm to estimate the location computes the Euclidean distance between the measured RSS vector and each fingerprint in the database. The coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position[toward]. The orientation filter and the Newton Trust Region (TR) algorithm to enhance the traditional LF by filtering the noisy signal. Although the average distance error is 1.82 m, this was not completely effective because they still suffer from the poor Wi-Fi coverage region [main].
The data is needed to be collected for location based service and is collected by cell towers and wifi access points in variety of ways, including UE information. These data is aggregated and store in public domain. This requires installation of access points which supply their location information, which is also costly. On the other hand, cellular networks have a wide coverage on the scale of kilometers and can serve a whole building. However, measurement accuracy based on cellular signals is not high enough to locate which shop or restaurant a person is in, and does not meet the requirements of most indoor applications[google].
There are two typical indoor positioning approaches, Propagation-based and Location Fingerprinting (LF) based. Propagation-based approaches estimate the position by measuring the received signal strength (RSS) with path loss. The drawbacks of these approaches lie in the requirement to have a strong Wi-Fi coverage in order to compute every condition that the signal can blend. The LF-based approaches such as locate a device by comparing its coordinates with the received signal strengths (RSSs) in a pre-recorded database. The drawbacks of these approaches are highly affected by internal building infrastructure changes, presence of humans, and interference among other devices. All these lead to unstable Wi-Fi coverage and inaccurate localization[main].
The fingerprinting technique is relatively simple to deploy compared to the other techniques such as angle-of arrival (AOA) and time-of-arrival (TOA). Moreover, there is no specialized hardware required at the mobile station (MS). Any existing wireless LAN infrastructure can be reused for this positioning system. The deployment of fingerprinting based positioning systems can be divided into two phases. First, in the offline phase, the location fingerprints are collected by performing a site-survey of the received signal strength (RSS) from multiple access points (APs). The entire area is covered by a rectangular grid of points. The RSS is measured with enough statistics to create a database or a table of predetermined RSS values on the points of the grid. The vector of RSS values at a point on the grid is called the location fingerprint of that point. Second, in the on-line phase, a MS will report a sample measured vector of RSSs from different APs to a central server (or a group of APs will collect the RSS measurements from a MS and send it to the server). The server uses an algorithm to estimate the location of the MS and reports the estimate back to the MS (or the application requesting the position information). The most common algorithm to estimate the location computes the Euclidean distance between the measured RSS vector and each fingerprint in the database. The coordinates associated with the fingerprint that provides the smallest Euclidean distance is returned as the estimate of the position[toward]. The orientation filter and the Newton Trust Region (TR) algorithm to enhance the traditional LF by filtering the noisy signal. Although the average distance error is 1.82 m, this was not completely effective because they still suffer from the poor Wi-Fi coverage region [main].
0 comments:
Post a Comment
Thanks for your Valuable comment