Neural Fuzzy Based Indoor Localization by Extending Kalman Filtering with Propagation Channel Modeling

Wu, Bing-Fei; Jen, Cheng-Lung; Chang, Kuei-Chung

doi:10.5772/9596

Cited by 5 publications

(9 citation statements)

References 10 publications

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“…Appendix E]. In addition, other studies have reported the use of filtering and tracking methods showing that these location-estimation systems can significantly improve location accuracy by using these robust schemes [25,[29][30][31][32][33]. These studies report improved accuracy by filtering out the variations in received SS data or by combining location information from more than one observer.…”

Section: Introductionmentioning

confidence: 93%

“…Recently, some studies have reported that the use of the fuzzy logic or neural network (NN) approaches can work with the supervised learning strategy to reduce the error caused by the dynamic indoor environment, and these algorithms are shown to improve the location accuracy. However, there are challenges to minimize the number of the learning epoch for an appropriate positioning system [23][24][25], and the main disadvantage is difficulty in preparing training and testing data sets [26][27][28] [cf. Appendix E].…”

Section: Introductionmentioning

confidence: 99%

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An adaptive location estimator using tracking algorithms for indoor WLANs

2010

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This paper presents adaptive algorithms for estimating the location of a mobile terminal (MT) based on radio propagation modeling (RPM), Kalman filtering (KF), and radio-frequency identification (RFID) assisting for indoor wireless local area networks (WLANs). The location of the MT of the extended KF positioning algorithm is extracted from the constant-speed trajectory and the radio propagation model. The observation information of the KF tracker is extracted from the empirical and RPM positioning methods. Specifically, a sensor-assisted method employs an RFID system to adapt the sequential selection cluster algorithm. As compared with the empirical method, not only can the RPM algorithm reduce the number of training data points and perform on-line calibration in the signal space, but the RPM and KF algorithms can alleviate the problem of aliasing. In addition, the KF tracker with the RFID-assisted scheme can calibrate the location estimation and improve the corner effect. Experimental results demonstrate that the proposed location-tracking algorithm using KF with the RFID-assisted scheme can achieve a high degree of location accuracy (i.e., more than 90% of the estimated positions have error distances of less than 2.1 m).

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

An adaptive location estimator using tracking algorithms for indoor WLANs

2010

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“…It was concluded that the adaptive neural fuzzy system provides good distance estimates due to its nonlinear ability and fast learning capacity. However, the authors in [14], [15], [16], and [17] have not used an ANN in combination with the fingerprinting approach. A performance comparison of three types of dynamic neural networks was presented in [3].…”

Section: Introductionmentioning

confidence: 99%

“…However, this simplicity of implementation is acquired at the cost of relatively poor accuracy of location estimates [10]. Many authors have also investigated the use of ANNs for indoor localization, see for example [14], [15], [16], [17], and [18]. In [14] the authors have used an ANN along with TOA and AOA methods to reduce location estimation errors in non-lineof-sight (NLOS) scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…In [16] the authors evaluated node localization in a wireless sensor network using an ANN and obtained higher accuracy than traditional schemes of location estimation. In [17], an adaptive neural fuzzy inference system was employed to model the dependency between RSS values and physical distances in the offline stage. In the online stage the distance between AP and target mobile device was determined by using this RSS-position dependency model and then an extended Kalman filter was used to compute the unknown position of mobile device in 2D space.…”

Section: Introductionmentioning

confidence: 99%

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Wlan–Based Indoor Localization Using Neural Networks

Saleem

Wyne

2016

Journal of Electrical Engineering

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Wireless indoor localization has generated recent research interest due to its numerous applications. This work investigates Wi-Fi based indoor localization using two variants of the fingerprinting approach. Specifically, we study the application of an artificial neural network (ANN) for implementing the fingerprinting approach and compare its localization performance with a probabilistic fingerprinting method that is based on maximum likelihood estimation (MLE) of the user location. We incorporate spatial correlation of fading into our investigations, which is often neglected in simulation studies and leads to erroneous location estimates. The localization performance is quantified in terms of accuracy, precision, robustness, and complexity. Multiple methods for handling the case of missing APs in online stage are investigated. Our results indicate that ANN-based fingerprinting outperforms the probabilistic approach for all performance metrics considered in this work.

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