Method, apparatus, and system for wireless sensing based on deep learning

A method for wireless sensing, comprising:

• transmitting a wireless signal through a wireless multipath channel of a venue, wherein the wireless multipath channel is impacted by a motion of an object in the venue;
• receiving the wireless signal through the wireless multipath channel of the venue, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless multipath channel and the motion of the object;
• obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the received wireless signal;
• computing a plurality of autocorrelation functions based on the TSCI, each autocorrelation function (ACF) computed based on CI of the TSCI in a respective sliding time window;
• constructing at least one ACF vector, wherein each respective ACF vector is a vector associated with a respective ACF comprising multiple vector elements each associated with a respective time lag, each vector element being a value of the respective ACF evaluated at the respective time lag;
• rearranging the at least one ACF vector into rearranged ACF data, wherein each ACF vector is a one-dimensional (1D) ACF-block;
• and
• performing a wireless sensing task based on a task engine to do a processing using the rearranged ACF data as an input.

An apparatus for wireless sensing, comprising:

• a receiver configured to receive a wireless signal transmitted through a wireless multipath channel of a venue, wherein the wireless multipath channel is impacted by a motion of an object in the venue, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless multipath channel and the motion of the object;
• and
• a processor configured to:
• obtain a time series of channel information (TSCI) of the wireless multipath channel based on the received wireless signal,
• compute a plurality of autocorrelation functions based on the TSCI, each autocorrelation function (ACF) computed based on CI of the TSCI in a respective sliding time window,
• construct at least one ACF vector, wherein each respective ACF vector is a vector associated with a respective ACF comprising multiple vector elements each associated with a respective time lag, each vector element being a value of the respective ACF evaluated at the respective time lag,
• rearrange the at least one ACF vector into rearranged ACF data, wherein each ACF vector is a one-dimensional (1D) ACF-block, and
• perform a wireless sensing task based on a task engine to do a processing using the rearranged ACF data as an input.