Integrated Multiple Kernel Learning for Device-Free Localization in Cluttered Environments Using Spatiotemporal Information

Ubiquitous WiFi signals not only provide fundamental communications for a large number of Internet of Things devices, but also enable to estimate target’s location in a contactless manner. However, most of the existing device-free localization (DFL) methods only utilize the time dynamics of the received WiFi signals, leading to inaccurate DFL in the cluttered indoor environments. Because different layouts of environments and deployments of WiFi devices cause the different mathematical distributions of the data collected from the cluttered indoor environments. In this article, a multiple kernel representation-based extreme learning machine (ELM) is proposed, named integrated multiple kernel ELM (IMK-ELM), for strengthening the localization performance in the cluttered indoor environments utilizing spatiotemporal information. In the proposed IMK-ELM-based DFL, the whole data set is first divided into several subsets depending on their mathematical distributions through the
$K$
-means clustering algorithm, and then a corresponding number of local DFL models are built for all the subsets to capture both the time dynamics and spatial properties of the data. Finally, a global DFL model is achieved by seamlessly integrating all the local DFL models due to the consistency mechanism. In addition, the Fresnel zone sensing theory is utilized for helping understand and explain the essence of indoor DFL. Comprehensive experiments indicate that the proposed IMK-ELM-based DFL outperforms state-of-the-art methods in the cluttered indoor environments.