Activity signal separation method based on commercial WiFi

Abstract

The invention discloses an activity signal separation method based on commercial Wi-Fi, and the method comprises the following steps: (10) CSI data collection: obtaining a group of CSI amplitude information Ak*t containing k subcarriers from a data packet in sampling time t; (20) active signal segmentation: calculating an average cross correlation coefficient of subcarriers in each second, and judging whether a person moves in the current environment or not, and if someone moves, continuing to calculate the amplitude difference of the interval time window within one second, and further refining the accurate moment of the user activity; (30) effective signal separation: iteratively calculating the minimum error of A-L-S by using an alternating direction multiplier method in an augmented Lagrange form, constraining the rank r of a background matrix L and the sparsity c of an activity matrix S, introducing bilateral random projection to reduce the calculated amount, performing wavelet transformation on the main components of the activity signals, and selecting specific frequency band activity signals generated by specific limb actions. The activity signal separation method is high inrobustness and high in operation efficiency.

Description

technical field [0001] The invention belongs to the technical field of wireless perception, and relates to a method for separating activity signals based on commercial WiFi with high robustness and good operating efficiency. Background technique [0002] Activity recognition systems are an important enabler of smart buildings. It can not only give birth to the "people-oriented" perception service in the building, but also make the efficient and energy-saving operation of the building possible. For example, sedentary white-collar workers need to be reminded to do stretching exercises from time to time; employees who leave work in a hurry need to be forced to leave only after turning off all electrical appliances. So far, most user activity recognition systems are based on body-worn devices (such as wearable devices, sports bracelets, smartphones, etc.), computer vision (such as cameras, depth cameras, etc.) or wireless sensor networks (such as infrared, Bluetooth, RFID, etc...

AI Technical Summary

Problems solved by technology

[0004]However, the existing work lacks a high-efficiency and robust activity signal separation method in the signal processing stage, so that the signals generated by human activities can be separated in different In the experimental scene, it is still significantly different from the static background and sudden disturbance noise

This also makes the existing work unable to become a robust full-scenario application enabling port

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