Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine

Abstract

Nowadays, the increasing demands of location-based services (LBS) have spurred the rapid development of indoor positioning systems (IPS). However, the performance of IPSs is affected by the fluctuation of the measured signal. In this study, a Gaussian filtering algorithm based on an extreme learning machine (ELM) is proposed to address the problem of inaccurate indoor positioning when significant Received Signal Strength Indication (RSSI) fluctuations happen during the measurement process. The Gaussian filtering method is analyzed and compared, which can effectively filter out the fluctuant signals that were caused by the environment effects in an RFID-based positioning system. Meanwhile, the fast learning ability of the proposed ELM algorithm can reduce the time consumption for the offline and online service, and establishes the network positioning regression model between the signal strengths of the tags and their corresponding positions. The proposed positioning system is tested in a real experimental environment. In addition, system test results demonstrate that the positioning algorithms can not only provide higher positioning accuracy, but also achieve a faster computational efficiency compared with other previous algorithms.

Mobile geolocation techniques for indoor environment monitoring

“…Using the method of ranging, where the distance between two nodes is predefined, the coordinates of the device are computed using lateration or angulation approaches. Extensive researches for indoor positioning system (IPS) has focused on the machine learning approaches such as Extreme Learning Machine (ELM) [25]. In this context, [25] proposed the ELM in order to overcome the shortcomings faced by the traditional positioning methods and provide higher positioning accuracy and robustness.…”

“…Extensive researches for indoor positioning system (IPS) has focused on the machine learning approaches such as Extreme Learning Machine (ELM) [25]. In this context, [25] proposed the ELM in order to overcome the shortcomings faced by the traditional positioning methods and provide higher positioning accuracy and robustness. The major contribution of that work consists of a Gaussian filter combined with ELM.…”

“…The key idea of "mathematical programming" based approaches is to formulate the position estimation problem as a constraint optimization problem, which can be solved by iterative (e.g. [16-21, 32, 36, 42]) or direct methods (e.g., [23,25,28,31]). Direct methods are generally based on factorization of the matrix where this operation is absent in iterative methods.…”

“…It is usually determined by the location estimation error (13). Table 5 shows the results of comparison between the state of the art and the proposed approaches TCL and COL. Table 4 compares Dolphin [18], Cricket [19,20], TDoA-based VLP [27], ELM-Gauss filtering [25], PSO-Gauss filtering [25] and Geometric localization [47] approaches with the proposed approaches. To do that, we used unified conditions under which the simulations have been performed for both existing and proposed algorithms.…”

Wireless LAN Service Quality Optimization in Academic Environments

“…Experimental results showed that the proposed hybrid system improved accuracy by reducing the average distance error, and applying genetic algorithm (GA) based optimization technique did not improve the accuracy, further. In Wang, Shi and Wu (2017), a Gaussian filtering algorithm based on an extreme learning machine (ELM) was proposed to address the problem of inaccurate indoor positioning in the midst of significant RSSI fluctuations during the measurement process. The proposed positioning system was tested in a real experimental environment, and found to achieve higher position accuracy and speedup, compared to previous algorithms.…”