Automatic identification of fingerprint regions for quick and reliable location estimation

Lemelson, Hendrik; Schnaufer, Sascha; Effelsberg, Wolfgang

doi:10.1109/percomw.2010.5470497

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…The positioning area is 40 × 40 m 2 . Seven beacon points are set in the area, and their coordinates are (10,0), (30,0), (0,20), (20,20), (40,20), (10,40), and (30,40). In this scenario, the lognormal distribution model is used in Eq.…”

Section: Improved Weighted Centroid Localization Algorithmmentioning

confidence: 99%

“…This simulation is set up under the MATLAB simulation environment. Six beacon points are arranged in an 80 × 60 m 2 rectangular positioning area and their coordinates are (20,20), (40,20), (60,20), (20,40), (40, 40), and (60, 40). Then, 500 points are generated randomly and their RSSI values are calculated from the six beacon nodes in accordance with the lognormal distribution model (in the real experiment, an RSSI value can be measured directly using reliable hardware).…”

Section: Comparison Of Three Nnsmentioning

confidence: 99%

“…The first is to increase the positioning accuracy, and the second is to reduce the workload. (17)(18)(19)(20)(21) With the fingerprint database, intelligent algorithms especially neural network (NN) algorithms can be applied to achieve a high positioning accuracy. The back-propagation neural network (BPNN) is in common use, (22,23) but has a local minimum problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Indoor Localization Based on Received Signal Strength Indicator and General Regression Neural Network

Xu¹,

Wang²,

Zhang³

et al. 2019

Sensors and Materials

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Keywords: received signal strength indicator (RSSI), ZigBee, localization, filter, maximum likelihood estimation (MLE), general regression neural network (GRNN)Nowadays, indoor positioning is becoming one of the most important issues in smart cities. With the rapid progress of wireless communication and digital electronic technology, wireless sensor networks (WSNs) have been developed and are playing an important role in indoor positioning systems. The received signal strength indicator (RSSI) is adopted by most rangebased localization algorithms. However, the positioning system based on the RSSI is vulnerable to environmental interference and the RSS itself is unstable. To tackle this problem, we propose an improved indoor localization based on the RSSI and general regression neural network (GRNN). In the raw data processing module, an improved average filter is proposed to make the raw data stable and reliable. Then, an improved weighted centroid localization algorithm (IWCLA) is proposed to revise the positioning result on the basis of maximum likelihood estimation (MLE). In the view of the complex and changeable indoor environment, an improved GRNN localization algorithm is proposed to achieve better applicability and higher positioning accuracy. The effectiveness of the proposed methods is verified in different cases through simulation and experiment studies. . Her current research interests include network-based control systems, multiagent systems, and complex networks.

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Section: Improved Weighted Centroid Localization Algorithmmentioning

confidence: 99%

Section: Comparison Of Three Nnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved Indoor Localization Based on Received Signal Strength Indicator and General Regression Neural Network

Xu¹,

Wang²,

Zhang³

et al. 2019

Sensors and Materials

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show abstract

“…Hence, extra sensors are unnecessary. Currently, the fingerprinting technique is one of the most commonly used for indoor location [ 3 , 4 ] due to the fact that it uses RSS from each access point to estimate the device location. Localization based on fingerprinting is usually carried out in two phases.…”

Section: Introductionmentioning

confidence: 99%

A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization

Sánchez-Rodríguez¹,

Hernández-Morera²,

Quinteiro³

et al. 2015

Sensors

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Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity. The localization system is built using a dataset from sensor fusion, which combines the strength of radio signals from different wireless local area network access points and device orientation information from a digital compass built-in mobile device, so that extra sensors are unnecessary. Experimental results indicate that the proposed system leads to substantial improvements on computational complexity over the widely-used traditional fingerprinting methods, and it has a better accuracy than they have.

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“…Therefore, extra sensors are unnecessary. Currently, fingerprinting technique is one of the most commonly used for indoor location [4] [5] due to the fact that it uses received signal strength from each access point to infer device location. Location based on fingerprinting is usually carried out in two phases.…”

Section: Introductionmentioning

confidence: 99%

Using data mining and fingerprinting extension with device orientation information for WLAN efficient indoor location estimation

Sanchez

Quinteiro

Hernández-Morera

et al. 2012

2012 IEEE 8th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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Research based on indoor location systems has recently been developed due to growing interest in locationaware services to be implemented in light mobile devices. Most of this work is based on received signal strength (RSS) from access points. However, a major drawback from using RSS is its variability due to indoor multipath effect caused by reflection, diffraction and scattering of signal propagation. Therefore, different device orientations in a fixed location provide significant and different RSS values. In this paper, we propose to extend fingerprinting with device orientation information. Implementation of our location system is based on data mining techniques employing decision tree algorithms. Experimental results demonstrate that using RSS samples with the device orientation information improves the location estimation with high accuracy.

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Automatic identification of fingerprint regions for quick and reliable location estimation

Cited by 5 publications

References 16 publications

Improved Indoor Localization Based on Received Signal Strength Indicator and General Regression Neural Network

Improved Indoor Localization Based on Received Signal Strength Indicator and General Regression Neural Network

A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization

Using data mining and fingerprinting extension with device orientation information for WLAN efficient indoor location estimation

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