Indoor Location Fingerprinting Based on Data Reduction

2011 ◽  
Author(s):  
Dragan Kukolj ◽  
Marina Vuckovic ◽  
Szilveszter Pletl
Keyword(s):  
Data Reduction ◽  
Indoor Location ◽  
Location Fingerprinting

Extreme learning machine for indoor location fingerprinting

2016 ◽  
Vol 28 (3) ◽  
pp. 867-883 ◽  
Author(s):  
Felis Dwiyasa ◽  
Meng-Hiot Lim ◽  
Yew-Soon Ong ◽  
Bijaya Panigrahi
Keyword(s):  
Extreme Learning Machine ◽  
Indoor Location ◽  
Location Fingerprinting ◽  
Learning Machine

scholarly journals Dealing with Insufficient Location Fingerprints in Wi-Fi Based Indoor Location Fingerprinting

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Dong ◽  
Zhen Ling ◽  
Xiangyu Xia ◽  
Haibo Ye ◽  
Wenjia Wu ◽  
...  
Keyword(s):  
Value Added ◽  
Likelihood Estimation ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Stochastic Gradient Descent ◽  
Indoor Environments ◽  
Localization Accuracy ◽  
Indoor Location ◽  
Location Fingerprinting ◽  
Gradient Descent Algorithm

The development of the Internet of Things has accelerated research in the indoor location fingerprinting technique, which provides value-added localization services for existing WLAN infrastructures without the need for any specialized hardware. The deployment of a fingerprinting based localization system requires an extremely large amount of measurements on received signal strength information to generate a location fingerprint database. Nonetheless, this requirement can rarely be satisfied in most indoor environments. In this paper, we target one but common situation when the collected measurements on received signal strength information are insufficient, and show limitations of existing location fingerprinting methods in dealing with inadequate location fingerprints. We also introduce a novel method to reduce noise in measuring the received signal strength based on the maximum likelihood estimation, and compute locations from inadequate location fingerprints by using the stochastic gradient descent algorithm. Our experiment results show that our proposed method can achieve better localization performance even when only a small quantity of RSS measurements is available. Especially when the number of observations at each location is small, our proposed method has evident superiority in localization accuracy.

scholarly journals Combination of DNN and Improved KNN for Indoor Location Fingerprinting

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Peng Dai ◽  
Yuan Yang ◽  
Manyi Wang ◽  
Ruqiang Yan
Keyword(s):  
Nearest Neighbor ◽  
Support Vector ◽  
Time Of Arrival ◽  
Test Bed ◽  
K Nearest Neighbor ◽  
Angle Of Arrival ◽  
Access Points ◽  
Indoor Location ◽  
Location Fingerprinting ◽  
Lower Accuracy

Fingerprinting based on Wi-Fi Received Signal Strength Indicator (RSSI) has been widely studied in recent years for indoor localization. While current algorithms related to RSSI Fingerprinting show a much lower accuracy than multilateration based on time of arrival or the angle of arrival techniques, they highly depend on the number of access points (APs) and fingerprinting training phase. In this paper, we present an integrated method by combining the deep neural network (DNN) with improved K-Nearest Neighbor (KNN) algorithm for indoor location fingerprinting. The improved KNN is realized by boosting the weights on K-nearest neighbors according to the number of matching access points. This will overcome the limitation of the original KNN algorithm on ignoring the influence of the neighboring points, which directly affect localization accuracy. The DNN algorithm is first used to classify the Wi-Fi RSSI Fingerprinting dataset. Then these possible locations in a certain class are also classified by the improved KNN algorithm to determine the final position. The proposed method is validated inside a room within about 13⁎9 m2. To examine its performance, the presented method has been compared with some classical algorithms, i.e., the random forest (RF) based algorithm, the KNN based algorithm, the support vector machine (SVM) based algorithm, the decision tree (DT) based algorithm, etc. Our real-world experiment results indicate that the proposed method is less dependent on the dense of access points and indoor radio propagation interference. Furthermore, our method can provide some preliminary guidelines for the design of indoor Wi-Fi test bed.

A Location Fingerprinting Algorithm Based on RSSI Statistic Parameters Matching Estimate

2011 ◽  
Vol 480-481 ◽  
pp. 1179-1184
Author(s):  
Wei Guo Guan ◽  
Zhong Liang Deng ◽  
Yue Tao Ge ◽  
Yan Pei Yu
Keyword(s):  
Indoor Environment ◽  
Euclidean Distance ◽  
Signal Strength ◽  
Distribution Characteristics ◽  
Indoor Location ◽  
Location Fingerprinting ◽  
Positioning Method ◽  
Location Algorithm ◽  
Case Simulation ◽  
Simulation Results

This study focuses on the indoor location system based on radio fingerprinting, and a grid positioning method based on weighted Euclidean distance matching by RSSI statistic parameters is presented in this paper. We analyze the principle of location fingerprint system and study the signal intensity distribution characteristics in indoor environment. According to the unsteadiness problem of the indoor signal strength caused by environmental impact, the positioning parameter model based on distributed grid is given. Moreover, the grid matching location algorithm is put forward based on the RSSI statistic parameters fusion estimation. Attributed to the proposed two-dimensional grid model, the algorithm is fit for the rapid matching calculations in continuous positioning case. Simulation results show that the approach has better robustness and accuracy, and it can effectively solve the problem of location estimates stability in general RSSI location fingerprinting system.

Indoor Location Fingerprinting Using FM Radio Signals

2014 ◽  
Vol 60 (2) ◽  
pp. 336-346 ◽  
Author(s):  
Vahideh Moghtadaiee ◽  
Andrew G. Dempster
Keyword(s):  
Indoor Location ◽  
Location Fingerprinting ◽  
Fm Radio ◽  
Radio Signals
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