Projection-Based Location System via Multiple Discriminant Analysis in Wireless Local Area Networks
ABSTRACT This paper presents a projection-based location system in an indoor wireless local area network (WLAN) environment. Our algorithm projects the received signal strength (RSS) onto a discriminative space such that the information of all access points (APs) is more efficiently utilized. The projection is determined by multiple discriminant analysis (MDA), thereby guaranteeing maximal discriminative information involved in the positioning system. The study conducts a series of experiments on the effects of our approach in a realistic indoor environment. The results show that not only is the positioning accuracy significantly improved, but the system cost, including the computation and data collection, is also greatly reduced at the same time. This is because our approach extracts only useful information for positioning, whereas the redundant noise is discarded to avoid the problem of overfitting and unnecessary calculations. Compared with prior works, this technique can produce a more graceful balance between the positioning accuracy and the computational complexity for the resource-weak clients.
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ABSTRACT: Subspace learning methods have been used to improve indoor positioning accuracy in wireless local area network (WLAN). However, these methods all suffer from the multimodal signal distributions. Furthermore, the variability of RSS over physical locations presents challenge to learning methods. This paper proposes local fisher discriminant analysis (LFDA) for improved WLAN positioning. LFDA adapts multimodality of signal distributions effectively and extracts more separate location features than previous methods. This is because LFDA further considers preserving the within-class local structure of signal space, thereby more freedom is left for maximizing the between-class separability of physical locations. Moreover, we do not perform monolithic LFDA model over the whole region. Instead, clustering analysis is incorporated to take advantages of spatially localized LFDA and reduce complexity. The proposed method is carried and compared with previous methods in a realistic WLAN indoor environment. Experiments show that the proposed method achieves significant accuracy improvement while reducing computation cost.Wireless Communications and Networking Conference (WCNC), 2013 IEEE; 01/2013
Conference Paper: Locating and monitoring tenants in PV based buildings[Show abstract] [Hide abstract]
ABSTRACT: This paper describes a wireless location and occupancy system used to create house and user energetic profile systems. Both procedures will be used to raise consumers' energy awareness in PV efficient building systems. Home energy consumption must be improved in order to evolve towards Net Zero Energy Buildings. Only the combine use of home PV grid-connected power systems with the active and responsible citizen's engagement in addressing the carbon and energy reduction challenge will allow homes and buildings to become Net Zero Energy Buildings. The developed system is composed by two subsystems. The first one (designated as WiLOS) deals with the problem of users' location inside a building, while the second (designated as HUEPS) uses the energy consumption of the building and the location information to create the users' energetic profiles. Both subsystems are described and experimental data is presented in order to show the effectiveness of the proposed methodology.2nd International Conference on Renewable Energy Research and Applications, Madrid, Spain; 10/2013
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ABSTRACT: This paper proposes a new localization method for mobile robots based on received signal strength (RSS) in indoor wireless local area networks (WLANs). In indoor wireless networks, propagation conditions are very difficult to predict due to interference, reflection, and fading effects. As a result, an explicit measurement equation is not available. In this paper, an observation likelihood model is accomplished using kernel density estimation to characterize the dependence of location and RSS. Based on the measured RSS, the robot's location is dynamically estimated using the proposed adaptive local search particle filter (ALSPF), which adopts the covariance adaptation for correcting the system states and updating the motion uncertainty. To deal with low sensor density in large-space environments, we present a strategy based on the strongest signal with minimum variance to choose a subset of detectable access points (APs) for enhancing robot localization and reducing the computational burden. The proposed approaches are verified by realistic low-density WLAN APs to demonstrate the feasibility and suitability. Experimental results indicate that the proposed ALSPF provides approximately 1-m error and significant improvements over particle filtering.IEEE Transactions on Industrial Electronics 12/2014; 61(12):6860-6870. DOI:10.1109/TIE.2014.2327553 · 6.50 Impact Factor