Abstract
Indoor localization is an important issue for indoor location-based services. As opposed to the other indoor localization approaches, the radio frequency (RF) based approaches are low-energy solutions with simple implementation. The kernel learning has been used for the RF-based indoor localization in 2D environment. However, the kernel learning has not been used in 3D environment. Hence, this paper proposes a multi-kernel learning scheme for 3D indoor localization. Based on the signals collected in the area of interest, the WiFi signals with better quality and closer to the user are selected so as to reduce the multipath effect and the external interference. Through the construction of multi-kernel, the localization accuracy can be improved as opposed to the localization based on the single kernel. We build multiple kernels to get the user’s location by collecting wireless received signal strengths (RSS) and signal-to-noise ratios (SNR). The kernel learning maps data to high dimension space and uses the optimization process to find the surface where the data are mapped. By multi-kernel training, the surface is fine-tuned and eventually converges to form the location database during the mapping process. The proposed localization scheme is verified by the real RSS and SNR collected from multiple wireless access points (AP) in a building. The experimental results verify that the proposed multi-kernel learning scheme performs better than the multi-DNN scheme and the existing kernel-based localization schemes in terms of localization accuracy and error in 3D indoor environment.
Highlights
The location-based service (LBS) for internet of things (IoT) becomes popular in many fields such as office building, shopping mall, and community building
Since the GPS system cannot catch satellite signals in an indoor environment, many indoor localization systems based on different radio frequency (RF) signals have been presented, such as RFID [1,2,3], Bluetooth, and LoRa [4] based localization systems
Our goal is to improve the localization accuracy in 3D indoor environments by using
Summary
The location-based service (LBS) for internet of things (IoT) becomes popular in many fields such as office building, shopping mall, and community building. Based on the WiFi localization systems, fingerprint schemes [5,6,7,8,9,10] show great advantages and accuracy in indoor localization with RSS and SNR signals. Select multiple physical locations in the region of interest and the signals received from APs built in indoor environment are defined as labeled data in these selected locations. The obtained position and the corresponding labeled data are defined as the fingerprint database. The RSS and SNR received by devices determine the specific location based on the radio map. This kind of scheme shows high potential in indoor localization
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