An RFID Indoor Positioning Algorithm Based on Support Vector Regression

Xu, He; Wu, Manxing; Li, Peng; Zhu, Feng; Wang, Ruchuan

doi:10.3390/s18051504

Cited by 82 publications

(49 citation statements)

References 30 publications

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“…Since the above describe target positioning as a classification problem that relies on a collected fingerprint dataset, some regression algorithms have been applied, such as Gaussian regression [26], support vector machines (SVMs) [27], or combinations of these methods [28]. Jang et al [29] presented robust image classification of the change in input data caused by the indoor multipath, where they built a 2D virtual radio map from the original 1-D Wi-Fi RSSI signal values and then constructed a CNN using 2-D radio maps as inputs. Channel state information (CSI)-based methods, such as [30][31][32][33][34], have proposed several ideas to process the CSI from Wi-Fi-based orthogonal frequency division modulation (OFDM) signals using deep CNNs.…”

Section: Related Workmentioning

confidence: 99%

Comparison of CNN Applications for RSSI-Based Fingerprint Indoor Localization

Sinha

Hwang

2019

Electronics

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The intelligent use of deep learning (DL) techniques can assist in overcoming noise and uncertainty during fingerprinting-based localization. With the rise in the available computational power on mobile devices, it is now possible to employ DL techniques, such as convolutional neural networks (CNNs), for smartphones. In this paper, we introduce a CNN model based on received signal strength indicator (RSSI) fingerprint datasets and compare it with different CNN application models, such as AlexNet, ResNet, ZFNet, Inception v3, and MobileNet v2, for indoor localization. The experimental results show that the proposed CNN model can achieve a test accuracy of 94.45% and an average location error as low as 1.44 m. Therefore, our CNN model outperforms conventional CNN applications for RSSI-based indoor positioning.

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Section: Related Workmentioning

confidence: 99%

Comparison of CNN Applications for RSSI-Based Fingerprint Indoor Localization

Sinha

Hwang

2019

Electronics

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“…It estimates the location information and signal strength of the virtual reference tag by combining the position information and signal strength of the actual reference tag with a linear interpolation method to achieve indoor transmission [28]. Xu et al [29] proposed an algorithm based on support vector regression to further improve the positioning accuracy of LANDMARC, and the experimental result shows that the RMSE is about 20.2 cm. Xu et al [30] then proposed an algorithm, called the BKNN, based on the K-nearest neighbor and Bayesian probability.…”

Section: E Kinds Of the Indoor Positioning Algorithmsmentioning

confidence: 99%

Determination of an Indoor Target Position: An Accurate and Adaptable Approach Based on Differential Positioning

Wang

Ding²,

Huang

et al. 2019

International Journal of Antennas and Propagation

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e growing demand for new products that rely on the accurate identification of a target's location indoors, while remaining mindful of cost, continues to drive research in this important and challenging area. Researchers are actively pursuing algorithmic improvements to eliminate errors introduced from complex interference factors present in indoor, wireless communication environments. In this work, we adopt a differential signal strength method in the design of our new indoor localization algorithm. e proposed algorithm reduces errors in the time domain by smoothing out the wireless signal fluctuations, thus stabilizing the signal; a single exponential algorithm is applied to the signal strength parameters collected to accomplish this. e target's position is then computed by utilizing both the plane geometric method and difference localization theory. is combination of techniques is reasonable for the environment under consideration (small scale, wireless), as the multipath effects for the signal are approximately equal under these conditions. In addition, the proposed approach is compatible with a wide variety of technologies (e.g., RFID and Bluetooth); it can be cost-effectively deployed by leveraging an existing hardware infrastructure. e proposed approach has been implemented and experimentally validated. e test results are very promising: they indicate that our algorithm improves the positioning accuracy by 70%-80% in comparison with the trilateration and LANDMARC positioning algorithms.

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“…Currently, pedestrian indoor navigation and positioning can be achieved using two types of methods. The first type of method is based on wireless technologies, e.g., WiFi [3][4][5], ultra-wideband (UWB) [6,7], visual sensors [8], radio frequency identification (RFID) [9], ibeacon [10], Bluetooth, and/or ZigBee, with a multi-source information fusion technique [11] to obtain pedestrian location information. For the first type of method, its location errors do not accumulate over time.…”

Section: Introductionmentioning

confidence: 99%

A Novel Pedestrian Dead Reckoning Algorithm for Multi-Mode Recognition Based on Smartphones

Xiong

Liu

et al. 2019

Remote Sensing

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With the rapid development of smartphone technology, pedestrian navigation based on built-in inertial sensors in smartphones shows great application prospects. Currently, most smartphone-based pedestrian dead reckoning (PDR) algorithms normally require a user to hold the phone in a fixed mode and, thus, need to correct the gyroscope heading with inputs from other sensors, which restricts the viability of pedestrian navigation significantly. In this paper, in order to improve the accuracy of the traditional step detection and step length estimation method for different users, a state transition-based step detection method and a step length estimation method using a neural network are proposed. In order to decrease the heading errors and inertial sensor errors in multi-mode system, a multi-mode intelligent recognition method based on a neural network was constructed. On this basis, we propose a heading correction method based on zero angular velocity and an overall correction method based on lateral velocity limitation (LV). Experimental results show that the maximum positioning errors obtained by the proposed algorithm are about 0.9% of the total path length. The proposed novel PDR algorithm dramatically enhances the user experience and, thus, has high value in real applications.

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An RFID Indoor Positioning Algorithm Based on Support Vector Regression

Cited by 82 publications

References 30 publications

Comparison of CNN Applications for RSSI-Based Fingerprint Indoor Localization

Comparison of CNN Applications for RSSI-Based Fingerprint Indoor Localization

Determination of an Indoor Target Position: An Accurate and Adaptable Approach Based on Differential Positioning

A Novel Pedestrian Dead Reckoning Algorithm for Multi-Mode Recognition Based on Smartphones

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