The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jul. 08, 2025
Filed:
Jan. 01, 2024
Guozhen Zhu, Greenbelt, MD (US);
Beibei Wang, Clarksville, MD (US);
Weihang Gao, Rockville, MD (US);
Yuqian HU, Greenbelt, MD (US);
Chenshu Wu, Hong Kong, CN;
Xiaolu Zeng, Beijing, CN;
K. J. Ray Liu, Potomac, MD (US);
Oscar Chi-lim AU, Rockville, MD (US);
Guozhen Zhu, Greenbelt, MD (US);
Beibei Wang, Clarksville, MD (US);
Weihang Gao, Rockville, MD (US);
Yuqian Hu, Greenbelt, MD (US);
Chenshu Wu, Hong Kong, CN;
Xiaolu Zeng, Beijing, CN;
K. J. Ray Liu, Potomac, MD (US);
Oscar Chi-Lim Au, Rockville, MD (US);
ORIGIN RESEARCH WIRELESS, INC., Rockville, MD (US);
Abstract
Methods, apparatus and systems for wireless sensing based on deep learning are described. For example, a described method comprises: 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.