The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Currently, WiFi indoor positioning system based on IEEE 802.11 is widely attractive for its free infrastructure and high localization performance. However, due to working on-demand strategy in green WiFi scenario, the access points are not always available for mobile when radio map is built in the offline phase. Radio map with unknown received signal strength is not valid for positioning and usually...
As a very popular positioning system, WLAN positioning attracts widely researches and investigations throughout the world. It implements the fingerprint technique to realize indoor navigation. The fingerprinting technique which employs the KNN algorithm has to make use of RSS (Received Signal Strength) from the Access Points (APs) without any classification. However, not all of the APs provide the...
Location estimation based on received signal strength (RSS) in WLAN environment is an attractive method for indoor positioning system. Unfortunately, due to the explicit nonlinearity and uncertainty of RSS signal, the traditional approaches always fail to deliver good location accuracy. This paper presents a novel positioning algorithm with kernel direct discriminant analysis (KDDA). We deploy the...
WLAN Indoor tracking system is presented based on the comparison between the off-line pre-stored Radio-map and new recorded signal strength in the on-line phase to estimate user's motion trajectory. Furthermore, the improved particle filter tracking algorithm that consists of the particles-reference points (P-RPs) transferring for getting the likelihood function and velocity estimation from the ANN...
This paper proposes the WiFi indoor location determination method based on adaptive neuro-fuzzy inference system (ANFIS) with principal component analysis (PCA). It reduces the WiFi signal vectors dimensions and saves the storage cost and simplifies the fuzzy rules generated by subtractive clustering method for ANFIS training. In the off-line phase, the received signal strength (RSS) or signal to...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.