Projection-Based Location System via Multiple Discriminant Analysis in Wireless Local Area Networks

Dept. of Electr. Eng., Yuan Ze Univ., Taoyuan, Taiwan
IEEE Transactions on Vehicular Technology (Impact Factor: 1.98). 12/2009; 58(9):5009 - 5019. DOI: 10.1109/TVT.2009.2025134
Source: IEEE Xplore


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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    • "In other words, using scenarios with lots of APs, some of them do not add new location information and can even introduce error in the system. In these cases, some mechanisms must be implemented to determine which AP is " good " or " bad " neighbor [23]. Therefore, APs' distribution is a vital part of the system and the development of an algorithm capable of best APs' distribution determination must be considered, without compromising the network coverage and quality of service. "
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    • "One of the most popular mapping function is the probabilistic models [19], [20] which regards positioning as a regression problem [21] by l = R r=1 l r · p(X|l r ), where X is the online measured RSS, l r represents the r-th reference location, p(l r |X) indicates the likelihood of location l r given the observation X, R is the number of reference locations and l represents the estimated result. Additionally, some projectionbased approaches constructs the mappping in different signal spaces [9], [22]. In the kernel method, the likelihood value is assigned to a kernel function around each of the observations in the training data as p( "
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    ABSTRACT: Abstract-This study focuses on indoor localization in Wireless Local Area Networks (WLANs). We investigate the unequal contribution of each access point (AP) on location estimation. The main contribution is two parts. First, a novel mechanism is proposed to measure the degrees of the AP importance. The importance of each AP is quantified by the signal discrimination between distinct locations. We utilize such numerical relevancies to select important APs for positioning. Second, the importance is further embedded into our positioning system. We provide a weighted kernel function where the effect of APs is differentiated. That is, the larger weights are assigned to the more important APs. Moreover, we develop a quasi-entropy function to avoid an abrupt change on the weights. Our positioning system is developed in a real-worldWLAN environment, where the realistic measurement of receive signal strength (RSS) is collected. Experimental results show that the positioning accuracy is significantly improved by taking the different importance into consideration.
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    ABSTRACT: Projection techniques have been used in Wi-Fi location fingerprinting systems to improve positioning accuracy. However, environmental dynamics present challenges to projection design. Furthermore, current projection-optimization techniques used in positioning, such as principal component analysis (PCA) and multiple discriminant analysis (MDA), have both advantages and limitations. This paper proposes a dynamic hybrid projection (DHP) technique for improved Wi-Fi localization, in which the projection is dynamically determined by simultaneously exploiting the complementary advantages of PCA and MDA while avoiding their unfavorable properties. The main contribution of this work is twofold: First, this study provides a novel formulation of a hybrid projection, which embeds the discriminative power into PCA and compensates for the two numerical problems of MDA in a unified framework. Second, DHP dynamically adjusts the hybrid mechanism with additional information, regarding the online-input region. That is, the proposed projection is input dependent, whereas traditional projections are fixed after training. This study applies the proposed algorithm to location fingerprinting in a realistic indoor Wi-Fi environment. On-site experimental results demonstrate that DHP outperforms static projection schemes, reducing the 50th and 67th percentile localization errors by 24.73%-30% and 18.18%-19.51%, respectively, compared with PCA and MDA.
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