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Image based indoor localization is an important problem with many useful application. This paper proposes an indoor localization system for performing fine localization and less latency with more priori information, including tile angel and the relative height between camera optical center and origin in reference coordinate system (RCS). The system is divided into two stages: offline stage and online...
The fingerprinting method based on Received Signal Strength (RSS) in localization has been drawing great attention these days due to the popularity of WLAN and mobile devices. However, this method usually requires tremendous time and efforts in building the fingerprinting map in the offline phase. In this paper, we propose a fast radio map building method utilizing the floor plan of the localization...
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...
This paper proposes an ANFIS indoor positioning system based on improved genetic algorithm (GA). In the offline phase, fuzzy rules are abstracted by means of subtractive clustering algorithm with training data, generating the structure of each ANFIS positioning subsystem in X and Y directions. Then each positioning subsystem is trained with improved-GA. In this training algorithm, BP algorithm acts...
This paper proposes the optimal K nearest neighbors (KNN) positioning algorithm via theoretical accuracy criterion (TAC) in wireless LAN (WLAN) indoor environment. As far as we know, although the KNN algorithm is widely utilized as one of the typical distance dependent positioning algorithms, the optimal selection of neighboring reference points (RPs) involved in KNN has not been significantly analyzed...
This paper presents the optimal networking strategy based on signal coverage requirement in wireless local area network (WLAN) indoor location environment. Up to now, much attention has been paid for the improvement of various positioning algorithms to guarantee the location efficiency in WLAN environment. However, the layout of access points (APs) and corresponding topological structures also significantly...
With the development of positioning in indoor wireless environments, RSS-based indoor positioning algorithm has been widely applied. Compared with other indoor positioning algorithms, the greatest advantage of RSS-based is that it can be configured easily and can get the signal strength from various types of networks that support the 802.11 protocol. Furthermore, it doesn't need complex clock synchronization...
Neural network optimized by genetic algorithm (GA) based WLAN indoor location method is proposed. GA based artificial neural network (GA-ANN) method can effectively reduce the storage cost, enhance real-time ability, and greatly improves the accuracy of indoor location. By analyzing the inherent shortage in neural network when applying in indoor environment, make use of genetic algorithm to encode...
As a fingerprint match method, k-nearest neighbors (KNN) has been widely applied for indoor location in Wireless Local Area Networks (WLAN), but its performance is sensitive to number of neighbors k and positions of reference points (RPs). So fuzzy c-means (FCM) clustering algorithm is applied to improve KNN, which is the KNN-FCM hybrid algorithm presented in this paper. In the proposed algorithm,...
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