A Novel Algorithm Based on Clustering and Access Points Selection for Indoor Fingerprint Localization

2013 ◽  
Vol 756-759 ◽  
pp. 3527-3531 ◽  
Author(s):  
Xing Zhang ◽  
Lin Ma ◽  
Xue Zhi Tan ◽  
Shi Zeng Guo
Keyword(s):  
Local Area ◽  
Location Based Service ◽  
Positioning Accuracy ◽  
Access Points ◽  
The Real ◽  
Traditional Algorithm ◽  
Selection For ◽  
Novel Algorithm ◽  
Calculated Amount ◽  
Localization Systems

With the growing popularity of location-based service (LBS), wireless local area networks (WLAN) indoor positioning has gained widespread attention. Unlike the traditional algorithm concentrating on positioning accuracy, we discuss how to improve the real-time property in WLAN indoor fingerprinting localization systems. In this paper, we present a novel algorithm which first divides the positioning area into sub-areas utilizing k-means clustering, and then selects appropriate access points (APs) for positioning to make the calculated amount as less as possible. By collecting data and performing in the real WLAN environment, our proposed algorithm shows high positioning accuracy while the computational burden has been decreased almost 93.7%.

scholarly journals Properties of Channel Interference for Wi-Fi Location Fingerprinting

2010 ◽  
Vol 6 (2) ◽  
pp. 56 ◽  
Author(s):  
Eddie C. L. Chan ◽  
George Baciu ◽  
S.C. Mak
Keyword(s):  
Channel Assignment ◽  
Ad Hoc ◽  
Research Work ◽  
Skewed Distribution ◽  
Positioning Accuracy ◽  
Access Points ◽  
Location Fingerprinting ◽  
Channel Interference ◽  
Assignment Scheme ◽  
Localization Systems

Localization systems for indoor areas have recently been suggested that make use of existing wireless local areanetwork (WLAN) infrastructure and location fingerprinting approach. However, most existing research work ignores channel interference between wireless infrastructures and this could affect accurate and precise positioning. A better understanding of the properties of channel interference could assist in improving the positioning accuracy while saving significant amounts of resources in the location-aware infrastructure. This paper investigates to what extent the positioning accuracy is affected by channel interference between access points. Two sets of experiments compare how the positioning accuracy is affected in three different channel assignment schemes: ad-hoc, sequential, and orthogonal data is analyzed to understand what features ofchannel interference affect positioning accuracy. The results show that choosing an appropriate channel assignment scheme could make localization 10% more accurate and reduces the number of access points that are required by 15%. The experimental analysis also indicates that the channel interference usually obeys a right-skewed distribution and positioning accuracy is heavily dependent on channel interference between access points (APs).

Dynamic and Positioning Performances of Linear Electromechanical Actuator

2015 ◽  
Vol 809-810 ◽  
pp. 682-687
Author(s):  
Vasile Nasui ◽  
Mihai Banica ◽  
Dinu Darabă
Keyword(s):  
Dynamic Characteristics ◽  
Practical Importance ◽  
Correct Identification ◽  
Positioning System ◽  
Positioning Error ◽  
Servo Motor ◽  
Positioning Accuracy ◽  
Electromechanical Actuator ◽  
Initial Errors ◽  
Selection For

This paper presents the dynamic characteristics and the proposed positioning performance of the system to them investigated experimentally. In this research, we developed the positioning system and we evaluated positioning accuracy. The developed system uses a servo motor for motion actuation. In this paper, we focused on studying the dependency of the positioning error – elementary errors – the position of the conducting element for the mechanism of the transformation of the rotation translation movement, representatively the mechanism screw – screwdriver and on emphasizing the practical consequences in the field of design, regulation and exploitation of the correct identification of all the initial errors in the structure of the mechanism, their character and the selection for an ultimate calculus of these which are of a real practical importance.

scholarly journals A Fast Radio Map Construction Method Merging Self-Adaptive Local Linear Embedding (LLE) and Graph-Based Label Propagation in WLAN Fingerprint Localization Systems

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 767 ◽  
Author(s):  
Yepeng Ni ◽  
Jianping Chai ◽  
Yan Wang ◽  
Weidong Fang
Keyword(s):  
Label Propagation ◽  
Positioning Accuracy ◽  
Local Linear Embedding ◽  
Map Construction ◽  
Local Linear ◽  
Linear Embedding ◽  
Fingerprint Database ◽  
Radio Map ◽  
Localization Systems ◽  
Self Adaptive

Indoor WLAN fingerprint localization systems have been widely applied due to the simplicity of implementation on various mobile devices, including smartphones. However, collecting received signal strength indication (RSSI) samples for the fingerprint database, named a radio map, is significantly labor-intensive and time-consuming. To solve the problem, this paper proposes a semi-supervised self-adaptive local linear embedding algorithm to build the radio map. First, this method uses the self-adaptive local linear embedding (SLLE) algorithm based on manifold learning to reduce the dimension of the high-dimensional RSSI samples and to extract a neighbor weight matrix. Secondly, a graph-based label propagation (GLP) algorithm is employed to build the radio map by semi-supervised learning from a large number of unlabeled RSSI samples to a few labeled RSSI samples. Finally, we propose a k self-adaptive neighbor weight (kSNW) algorithm, used for radio map construction in this paper, to realize online localization. The results of the experiments conducted in a real indoor environment show that the proposed method reduces the demand for large quantities of labeled samples and achieves good positioning accuracy. With only 25% labeled RSSI samples, our system can obtain positioning accuracy of more than 88%, within 3 m of localization errors.

scholarly journals An Improved Hierarchical WLAN Positioning Method Based on Apriori Knowledge

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 475 ◽  
Author(s):  
Kwo-Ting Fang ◽  
Cheng-Tao Lee ◽  
Li-min Sun
Keyword(s):  
Compressive Sensing ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Signal Reconstruction ◽  
Local Area ◽  
Area Network ◽  
Fingerprint Matching ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
Fine Localization

The hierarchical-based structure is recognized as a favorable structure for wireless local area network (WLAN) positioning. It is comprised of two positioning phases: the coarse localization phase and the fine localization phase. In the coarse localization phase, the users’ positions are firstly narrowed down to smaller regions or clusters. Then, a fingerprint matching algorithm is adopted to estimate the users’ positions within the clusters during the fine localization phase. Currently the clustering strategies in the coarse localization phase can be divided into received signal strength (RSS) clustering and 3D clustering. And the commonly seen positioning algorithms in the fine localization phase include k nearest neighbors (kNN), kernel based and compressive sensing-based. This paper proposed an improved WLAN positioning method using the combination: 3D clustering for the coarse localization phase and the compressive sensing-based fine localization. The method have three favorable features: (1) By using the previously estimated positions to define the sub-reference fingerprinting map (RFM) in the first coarse localization phase, the method can adopt the prior information that the users’ positions are continuous during walking to improve positioning accuracy. (2) The compressive sensing theory is adopted in the fine localization phase, where the positioning problem is transformed to a signal reconstruction problem. This again can improve the positioning accuracy. (3) The second coarse localization phase is added to avoid the global fingerprint matching in traditional 3D clustering-based methods when the stuck-in-small-area problem is encountered, thus, sufficiently lowered the maximum positioning latency. The proposed improved hierarchical WLAN positioning method is compared with two traditional methods during the experiments section. The resulting positioning accuracy and positioning latency have shown that the performance of the proposed method has better performance in both aspects.

scholarly journals Underground Coal Mine Fingerprint Positioning Based on the MA-VAP Method

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5401
Author(s):  
Mingzhi Song ◽  
Jiansheng Qian
Keyword(s):  
Real Time ◽  
Coal Mine ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Access Point ◽  
Cumulative Distribution ◽  
Kernel Principal Component Analysis ◽  
Area Network ◽  
Positioning Accuracy ◽  
Access Points

The access points (APs) in a coal mine wireless local area network (WLAN) are generally sparsely distributed. It can, with difficulty, satisfy the basic requirements of the fingerprint positioning based on Wi-Fi. Currently, the effectiveness of positioning is ensured by deploying more APs in an underground tunnel, which significantly increases system cost. This problem can be solved by using the Virtual Access Point (VAP) method that introduces virtual access points (VAPs), which can be virtually arranged in any part of the positioning area without installing actual access points. The drawback of the VAP method is that the generated received signal strength (RSS) value of a VAP is calculated based on the mapping of RSS value from only one corresponding access point (AP). This drawback does not consider the correlation between different AP signals and the generated RSS value of a VAP, which makes the modeling of fingerprint samples and real-time RSS collection incomplete. This study proposed a Multi-Association Virtual Access Point (MA-VAP) method takes into account the influence of multi-association. The multi-association coefficient is calculated based on the correlation between the RSS values of a VAP and multiple access points (APs). Then, the RSS value generated by a VAP is calculated using the multi-association function. The real-time collected RSS values from multiple APs related to this VAP are the input of the multi-association function. The influence of the number of VAPs and their arrangement on positioning accuracy is also analyzed. The experimental positioning results show that the proposed MA-VAP method achieves better positioning performance than the VAP method for the same VAP arrangement. Combined with the Weight K-Nearest Neighbors (WKNN) algorithm and Kernel Principal Component Analysis (KPCA) algorithm, the positioning error of the MA-VAP method of the error distance cumulative distribution function (CDF) at 90% is 4.5 m (with WKNN) and 3.5 m (with KPCA) in the environment with non-line-of-sight (NLOS) interference, and the positioning accuracy is improved by 10% (with WKNN) and 22.2% (with KPCA) compared with the VAP method. The MA-VAP method not only effectively solves the fingerprint positioning problem when APs are sparse deployed, but also improves the positioning accuracy.

scholarly journals Indoor Positioning on Disparate Commercial Smartphones Using Wi-Fi Access Points Coverage Area

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4351 ◽  
Author(s):  
Ashraf ◽  
Hur ◽  
Park
Keyword(s):  
Human Mobility ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Indoor Environments ◽  
Coverage Area ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Access Points ◽  
Intersection Area ◽  
The Impact

The applications of location-based services require precise location information of a user both indoors and outdoors. Global positioning system’s reduced accuracy for indoor environments necessitated the initiation of Indoor Positioning Systems (IPSs). However, the development of an IPS which can determine the user’s position with heterogeneous smartphones in the same fashion is a challenging problem. The performance of Wi-Fi fingerprinting-based IPSs is degraded by many factors including shadowing, absorption, and interference caused by obstacles, human mobility, and body loss. Moreover, the use of various smartphones and different orientations of the very same smartphone can limit its positioning accuracy as well. As Wi-Fi fingerprinting is based on Received Signal Strength (RSS) vector, it is prone to dynamic intrinsic limitations of radio propagation, including changes over time, and far away locations having similar RSS vector. This article presents a Wi-Fi fingerprinting approach that exploits Wi-Fi Access Points (APs) coverage area and does not utilize the RSS vector. Using the concepts of APs coverage area uniqueness and coverage area overlap, the proposed approach calculates the user’s current position with the help of APs’ intersection area. The experimental results demonstrate that the device dependency can be mitigated by making the fingerprinting database with the proposed approach. The experiments performed at a public place proves that positioning accuracy can also be increased because the proposed approach performs well in dynamic environments with human mobility. The impact of human body loss is studied as well.

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