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Harvesting Ambient RF for Presence Detection Through Deep Learning
Feb 13, 2020
This paper explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using WiFi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver contains rich information about the propagation environment. Through judicious pre-processing of the estimated CSI followed by deep learning, reliable presence detection can be achieved. Several challenges in passive RF sensing are addressed. With presence detection, how to collect training data with human presence can have a significant impact on the performance. This is in contrast to activity detection when a specific motion pattern is of interest. A second challenge is that RF signals are complex-valued. Handling complex-valued input in deep learning requires careful data representation and network architecture design. Finally, human presence affects CSI variation along multiple dimensions; such variation, however, is often masked by system impediments such as timing or frequency offset. Addressing these challenges, the proposed learning system uses pre-processing to preserve human motion induced channel variation while insulating against other impairments. A convolutional neural network (CNN) properly trained with both magnitude and phase information is then designed to achieve reliable presence detection. Extensive experiments are conducted. Using off-the-shelf WiFi devices, the proposed deep learning based RF sensing achieves near perfect presence detection during multiple extended periods of test and exhibits superior performance compared with leading edge passive infrared sensors. The learning based passive RF sensing thus provides a viable and promising alternative for presence or occupancy detection.
K-medoids Clustering of Data Sequences with Composite Distributions
Jul 31, 2018
This paper studies clustering of data sequences using the k-medoids algorithm. All the data sequences are assumed to be generated from \emph{unknown} continuous distributions, which form clusters with each cluster containing a composite set of closely located distributions (based on a certain distance metric between distributions). The maximum intra-cluster distance is assumed to be smaller than the minimum inter-cluster distance, and both values are assumed to be known. The goal is to group the data sequences together if their underlying generative distributions (which are unknown) belong to one cluster. Distribution distance metrics based k-medoids algorithms are proposed for known and unknown number of distribution clusters. Upper bounds on the error probability and convergence results in the large sample regime are also provided. It is shown that the error probability decays exponentially fast as the number of samples in each data sequence goes to infinity. The error exponent has a simple form regardless of the distance metric applied when certain conditions are satisfied. In particular, the error exponent is characterized when either the Kolmogrov-Smirnov distance or the maximum mean discrepancy are used as the distance metric. Simulation results are provided to validate the analysis.
Autonomous Localization and Mapping Using a Single Mobile Device
Dec 17, 2016
This paper considers the problem of simultaneous 2-D room shape reconstruction and self-localization without the requirement of any pre-established infrastructure. A mobile device equipped with co-located microphone and loudspeaker as well as internal motion sensors is used to emit acoustic pulses and collect echoes reflected by the walls. Using only first order echoes, room shape recovery and self-localization is feasible when auxiliary information is obtained using motion sensors. In particular, it is established that using echoes collected at three measurement locations and the two distances between consecutive measurement points, unique localization and mapping can be achieved provided that the three measurement points are not collinear. Practical algorithms for room shape reconstruction and self-localization in the presence of noise and higher order echoes are proposed along with experimental results to demonstrate the effectiveness of the proposed approach.