An Indoor Localization Algorithm Based on Continuous Feature Scaling and Outlier Deleting

Fu, Yuli; Chen, Peilin; Yang, Shuai; Tang, Jie

doi:10.1109/jiot.2018.2795615

Cited by 30 publications

(16 citation statements)

References 21 publications

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“…Although the passive RFID system is more reliable and robust in LOS environments, we considered in our evaluation of the proposed algorithm NLOS indoor environments in addition to LOS environments for co-polarization, cross-polarization, and combined-polarization antenna configurations. In order to delete the outlier measurements, a modified Thompson Tau test was utilized to detect outliers as proposed in [ 27 ].…”

Section: Resultsmentioning

confidence: 99%

“…Another direction to enhance RSSI localization is to use pattern matching and fingerprinting based methods for reducing the influence of range measurement errors [ 17 , 19 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 37 , 38 , 39 , 40 , 41 ]. The LANDMARC indoor localization system is presented in [ 22 ] as a pattern matching method to enhance the overall accuracy of locating objects using some reference tags.…”

Section: Related Workmentioning

confidence: 99%

“…In [ 26 ], continuous wavelet transform is used to extract time-frequency or time-scaling information from each RSSI samples for indoor fingerprinting localization algorithm. The work in [ 27 ] proposes to use the feature-scaling-based k-nearest neighbor algorithm with RSSI. In [ 28 , 29 ], neural network and machine learning algorithms are used for RSSI fingerprints, respectively, in order to improve the localization accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Generous research effort has been devoted to developing localization methods that can compensate for the adverse effects of uncertainties in RSSI measurements [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Most of these proposed methods depend on the optimization techniques or fingerprinting, which requires high computational complexity, making them unpractical for IoT localization applications.…”

Section: Introductionmentioning

confidence: 99%

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Indoor Large-Scale MIMO-Based RSSI Localization with Low-Complexity RFID Infrastructure

El-Absi

Zheng

Abuelhaija

et al. 2020

Sensors

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Indoor localization based on unsynchronized, low-complexity, passive radio frequency identification (RFID) using the received signal strength indicator (RSSI) has a wide potential for a variety of internet of things (IoTs) applications due to their energy-harvesting capabilities and low complexity. However, conventional RSSI-based algorithms present inaccurate ranging, especially in indoor environments, mainly because of the multipath randomness effect. In this work, we propose RSSI-based localization with low-complexity, passive RFID infrastructure utilizing the potential benefits of large-scale MIMO technology operated in the millimeter-wave band, which offers channel hardening, in order to alleviate the effect of small-scale fading. Particularly, by investigating an indoor environment equipped with extremely simple dielectric resonator (DR) tags, we propose an efficient localization algorithm that enables a smart object equipped with large-scale MIMO exploiting the RSSI measurements obtained from the reference DR tags in order to improve the localization accuracy. In this context, we also derive Cramer–Rao lower bound of the proposed technique. Numerical results evidence the effectiveness of the proposed algorithms considering various arbitrary network topologies, and results are compared with an existing algorithm, where the proposed algorithms not only produce higher localization accuracy but also achieve a greater robustness against inaccuracies in channel modeling.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Indoor Large-Scale MIMO-Based RSSI Localization with Low-Complexity RFID Infrastructure

El-Absi

Zheng

Abuelhaija

et al. 2020

Sensors

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“…Besides ANN, there are ML methods that have been used for indoor fingerprinting. These approaches include nearest neighbors [46], GD-based methods [47], Gauss Process model [48], random forest [49], and support vector machine [50]. When using such methods for wireless fingerprinting, RSS measurements are used as inputs while two-dimensional locations are the outputs.…”

Section: Introductionmentioning

confidence: 99%

Wireless Fingerprinting Uncertainty Prediction Based on Machine Learning

Гао

et al. 2019

Sensors

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Although wireless fingerprinting has been well researched and widely used for indoor localization, its performance is difficult to quantify. Therefore, when wireless fingerprinting solutions are used as location updates in multi-sensor integration, it is challenging to set their weight accurately. To alleviate this issue, this paper focuses on predicting wireless fingerprinting location uncertainty by given received signal strength (RSS) measurements through the use of machine learning (ML). Two ML methods are used, including an artificial neural network (ANN)-based approach and a Gaussian distribution (GD)-based method. The predicted location uncertainty is evaluated and further used to set the measurement noises in the dead-reckoning/wireless fingerprinting integrated localization extended Kalman filter (EKF). Indoor walking test results indicated the possibility of predicting the wireless fingerprinting uncertainty through ANN the effectiveness of setting measurement noises adaptively in the integrated localization EKF.

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