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An Improved fingerprint algorithm with Access Point Selection and Reference Point Selection Strategies for Indoor Positioning

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JOURNAL OF NAVIGATION 2020年第6期73卷 1182-1201页

作者： Li, Changgeng Huang, Hui Liao, Bowen Cent South Univ Sch Phys & Elect Changsha Hunan Peoples R China

The fingerprint positioning (FP) algorithm has been investigated extensively owing to the fact that it can provide a relatively ideal indoor positioning result. However, the effectiveness of the fingerprint algorithm relies on the size of fingerprint database, which prevents the algorithm from being widely applied in practical applications. In this paper, an improved fingerprint algorithm with access point (AP) selection strategy and reference point (RP) selection strategy is proposed to reduce the size of the fingerprint database and improve the positioning accuracy. The experimental results show that the proposed algorithm can reduce the storage size of the fingerprint database by more than 42 center dot 64%. Moreover, compared with the FP algorithm, the fingerprint algorithm with segment characteristic distance (FP-SCD) and the fingerprint algorithm with RP selection strategy (FP-RPSS), the average positioning error of the proposed algorithm is reduced by 20 center dot 15%, 10 center dot 83% and 11 center dot 57%, respectively. Therefore, the proposed algorithm has a good application in real positioning scenarios.

关键词： Indoor positioning fingerprint algorithm AP selection strategy RP selection strategy

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Localization of a Mobile Device with Sensor Using a Cascade Artificial Neural Network-Based fingerprint algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2019年第1期12卷 238-249页

作者： Erdem, Ebubekir Tuncer, Taner Dogan, Resul Firat Univ Dept Comp Engn TR-23119 Elazig Turkey

One of the important functions of sensor networks is that they collect data from the physical environment and transmit them to a center for processing. The location from which the collected data is obtained is crucial in many applications, such as search and rescue, disaster relief, and target tracking. In this respect, determination of the location with low-cost, scalable, and efficient algorithms is required. This study presents the implementation of a fingerprint-based location determination algorithm by using the cascade artificial neural network (ANN). A 15.6 x 13.8 m(2) implementation area, in which an anchor node is placed at each corner, is divided into grids with a 60-cm edge. The proposed algorithm consists of two phases: offline and online. In the offline phase, first a mobile device with an Xbee sensor, which is able to move sensitively and communicate with anchor nodes, is used. With this device, the implementation area is visited, and at each grid point, received signal strength indicator (RSSI) values and real distances measured from the anchor nodes are recorded in a database. The training of the cascade ANN is done using the database for both range and location determination. In the online phase, the RSSIs measured by the anchor nodes are provided as the input to the cascade ANN algorithm by means of a mobile device in any coordinate. The location of the mobile device and its distance to the anchor nodes are determined with minimum error. To show the superiority of the proposed method, the results obtained are compared with those in the literature and it has been shown that this location determination is made with a smaller error. (c) 2019 The Authors. Published by Atlantis Press SARL.

关键词： Zigbee fingerprint algorithm Wireless sensor network Cascade artificial neural network

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Gradient Boost Decision Tree fingerprint algorithm for Wi-Fi Localization 9th

Gradient Boost Decision Tree Fingerprint Algorithm for Wi-Fi...

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9th China Satellite Navigation Conference (CSNC) - Location, Time of Augmentation

作者： Liu, Yanxu Deng, Zhongliang Yin, Lu Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China

ISBN: (纸本)9789811300295;9789811300288

Location Based Services (LBS) is require the indoor and outdoor seamless positioning that providing real-time, stable and high-accuracy localization and navigation. Mobile devices can be positioning by using Wi-Fi signals based on correlation between Wi-Fi signals strength and coordinates. And Wi-Fi signals are common in modern buildings, so there needn't deploy equipment. But there are still some drawbacks, such as poorly positioning accuracy and too long online computing time during using Wi-Fi signals to localization. For this reason, we proposed a Gradient Boosting Decision Tree (GBDT) fingerprint algorithm for Wi-Fi localization, this algorithm adopt a linear combination of multiple decision trees to obtain an approximate model of the coordinates and received signal strength (RSS). Experiment shows that about 13% increases in positioning accuracy and 65% reduces in online computation time compares with AdaBoost-based algorithm.

关键词： LBS RSSI fingerprint algorithm Gradient boosting decision tree

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Gradient Boost Decision Tree fingerprint algorithm for Wi-Fi Localization

Gradient Boost Decision Tree Fingerprint Algorithm for Wi-Fi...

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第九届中国卫星导航学术年会

作者： Yanxu Liu Zhongliang Deng Lu Yin School of Electonic Engineering Beijing University of Posts and Telecommunications

Location Based Services(LBS)is require the indoor and outdoor seamless positioning that providing real-time,stable and high-accuracy localization and *** devices can be positioning by using Wi-Fi signals based on correlation between Wi-Fi signals strength and *** Wi-Fi signals are common in modern buildings,so there needn’t deploy *** there are still some drawbacks,such as poorly positioning accuracy and too long online computing time during using Wi-Fi signals to *** this reason,we proposed a Gradient Boosting Decision Tree(GBDT)fingerprint algorithm for Wi-Fi localization,this algorithm adopt a linear combination of multiple decision trees to obtain an approximate model of the coordinates and received signal strength(RSS).Experiment shows that about 13%increases in positioning accuracy and 65%reduces in online computation time compares with Ada Boost-based algorithm.

关键词： LBS RSSI fingerprint algorithm Gradient Boosting Decision Tree

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Detecting near-duplicates in russian documents through using fingerprint algorithm Simhash 12th

Detecting near-duplicates in russian documents through using...

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12th International Symposium on Intelligent Systems (INTELS)

作者： Rezaeian, N. Novikova, G. M. RUDN Univ 6 Miklukho Maklaya str Moscow 117198 Russia

Plagiarism is one of the major problems in the age of communication. In many languages such as English, this issue is seriously of high importance and many powerful devices have been invented to prevent this problem from occurring. This article aims at discovering plagiarism in Russian texts based on fingerprint algorithm. The fingerprint algorithms have high speeds in finding out the plagiarism due to the compact features it creates and purely because of the comparison of these properties between original documents and dubious documents. Increasing the power and accuracy of plagiarism discovery, there must be elimination of general words and word rooting before pre-processing applications such as words separation, numbers replacement, and homogenization. In this article, four Simhash algorithms have been used. The implementation of these algorithms confirmed on 800 articles with the scientific topics was found to have satisfactory results. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： plagiarism fingerprint algorithm Simhash

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Detecting Near-duplicates in Russian Documents through Using fingerprint algorithm Simhash

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Procedia Computer Science 2017年 103卷 421-425页

作者： N. Rezaeian G.M. Novikova RUDN University 6 Miklukho-Maklaya str. Moscowf 117198 Russia

关键词： plagiarism fingerprint algorithm Simhash

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fingerprint Clustering algorithm for Data Profiling using Pentaho Data Integration 2

Fingerprint Clustering Algorithm for Data Profiling using Pe...

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2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Dwiandriani, Febri Kusumasari, Tien Fabrianti Hasibuan, M. Azani Telkom Univ Sch Ind & Syst Engn Informat Syst Dept Bandung Indonesia

ISBN: (纸本)9781538606582

Data profiling is an abnormality in the search process data that undermine the value of the data. Type of data profiling analysis techniques to detect duplication of text within a single paragraph can by using a text cluster. In this paper aims at doing text algorithm transformation cluster with fingerprint method using Pentaho Data Integration (PDI). As for methods used to conduct implementation text cluster with Pentaho Data Integration is to do a mapping algorithm fingerprint method on component contained in Pentaho, then perform a transformation per component, and further evaluation of the results of a text cluster with open source data profiling tool. Implementation of text clusters with Pentaho Data Integration successfully done but there is still some rudimentary logic. The results from implementation of clusters number on the transformation by using Pentaho Data Integration is greater than a number of text clusters by using the OpenRefine tool.

关键词： Data profiling text clustering Pentaho Data integration fingerprint algorithm open source

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Parking Spaces Detection Based on fingerprint algorithm

Parking Spaces Detection Based on Fingerprint Algorithm

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2015 3rd International Conference on Machinery,Materials and Information Technology Applications(ICMMITA 2015)

作者： Kunshan Xing Zhigang Wen School of Electronic Engineering Beijing University of Posts and Telecommunications Institute of Sensing Technology and Business BUPT

With the development of society, the number of private cars is increasing year by year, leading to parking management become an important issue of nowadays social traffic. The traditional method of manual management of the parking lot cannot meet requirements of large-scale parking lots. Because of the above problems, smart parking lot based on Internet of things is getting more and more attention. The main research of this paper is the real-time detection of parking spaces in smart parking lots,establishing a parking spaces perception system and using fingerprint algorithm to detect the accurate occupancy situation of parking spaces.

关键词： fingerprint algorithm Parking Spaces Detection Internet of things

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IMPROVED INDOOR POSITIONING USING fingerprint TECHNIQUE AND WEIGHTED K-NEAREST NEIGHBOUR 6

IMPROVED INDOOR POSITIONING USING FINGERPRINT TECHNIQUE AND ...

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ISPRS GeoSpatial Conference / Joint 6th Sensors and Models in Photogrammetry and Remote Sensing Conference (SMPR) / 4th Geospatial Information Research (GIResearch) Conference

作者： Salim, Sh. Naderi Alizadeh, M. M. Chamankar, Sh. Schuh, H. KN Toosi Univ Technol Fac Geodesy & Geomat Engn Tehran Iran German Res Ctr Geosci GFZ Potsdam Germany Tech Univ Berlin Inst Geodesy & Geoinformat Sci Berlin Germany

Global Navigation Satellite Systems are not effective when there is no direct line of sight between the user and the satellites, such as indoor environments and dense urban areas. Today, location-based services are used significantly due to their utility and ease of access. The fingerprint method is one of the common methods of determining the location in indoor environments. In this research, the indoor positioning system based on the fingerprint algorithm with a wireless network has been implemented. The positioning system based on the method of nearest neighbour and weighted K-nearest neighbour with two access points has been implemented in two different scenarios. The output accuracy of each technique has been compared to each other. The main goal of this article is to compare the accuracy of positioning with the fingerprint method using the mentioned algorithms and to find the most suitable mode and algorithm for determining the indoor position in most places. The improved weighted nearest neighbour method will have an almost acceptable result in all scenarios and also in the first scenario with dense and regular reference points the weighted K-nearest neighbour method with RMSE=0.2812( m) has provided the best result. In the second scenario with scattered and irregular reference points the weighted K-nearest neighbour with RMSE=0.6735( m) has given lower accuracy result.

关键词： WLAN Positioning fingerprint algorithm Location-Based Services Nearest Neighbour Weighted K-Nearest Neighbour

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Performance Evaluation of Bluetooth Low Energy Positioning Systems When Using Sparse Training Data

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2022年第5期E105A卷 778-786页

作者： Manabe, Tetsuya Omura, Kosuke Saitama Univ Grad Sch Sci & Engn Saitama 3388570 Japan

This paper evaluates the bluetooth low energy (BLE) positioning systems using the sparse-training data through the comparison experiments. The sparse-training data is extracted from the database including enough data for realizing the highly accurate and precise positioning. First, we define the sparse-training data, i.e., the data collection time and the number of smartphones, directions, beacons, and reference points, on BLE positioning systems. Next, the positioning performance evaluation experiments are conducted in two indoor environments, that is, an indoor corridor as a one-dimensionally spread environment and a hall as a two-dimensionally spread environment. The algorithms for comparison are the conventional fingerprint algorithm and the hybrid algorithm (the authors already proposed, and combined the proximity algorithm and the fingerprint algorithm). Based on the results, we confirm that the hybrid algorithm performs well in many cases even when using sparse-training data. Consequently, the robustness of the hybrid algorithm, that the authors already proposed for the sparse-training data, is shown.

关键词： bluetooth low energy hybrid algorithm fingerprint algorithm proximity algorithm sparse training data

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