Channel state information based human presence detection using non-linear techniques

Channel State Information (CSI) obtained from commercial Wi-Fi chipsets has proven to be efficient in detecting human interaction with radio waves. However, there is a lack of analytical modelling to define the impact of human presence on multidimensional CSI vectors. Existing approaches include linear, parameter-less techniques to reduce signal space dimensions and filter noise by assuming linear correlations among sub-carriers. In this work, we first model the human presence and then analyse occurrences of non-linear correlations among subcarriers. We then exploit these correlations by introducing non-linear techniques to reduce CSI dimensions and filter noise. These techniques offer adjustable parameters that enhance signal quality depending on the environment and the amount of human interaction. We analyse the performance of human presence detection using the introduced techniques with just two transceivers. Our results show that when human motion is insignificant or occurs far from the link, non-linear techniques improve the detection accuracy up to 5% compared to the linear approach.