公开(公告)号：US20200373972A1

公开(公告)日：2020-11-26

申请号：US16991583

申请日：2020-08-12

Applicant: INTELLIGENT FUSION TECHNOLOGY, INC.

Inventor： Zhonghai WANG ,  Lun LI ,  Jingyang LU ,  Genshe CHEN ,  Weifeng SU ,  Xingping LIN ,  Xingyu XIANG ,  Wenhao XIONG

IPC: H04B7/0413 ,  H04B17/336 ,  H04B17/391

Abstract: A multiple-input and multiple-output (MIMO) bolt-on device for a single-input and single-output (SISO) radio, a MIMO channel emulator for testing the MIMO bolt-on device, and a MIMO channel emulation method are provided. The MIMO bolt-on device includes: a plurality of antennas, a multi-channel receiver, a plurality of couplers, a micro-controller, and a switch device. The multi-channel receiver includes a plurality of channels for signal transmission. Each coupler is configured to couple the multi-channel receiver with one of the plurality of antennas. The micro-controller is coupled to the multi-channel receiver to compare signals from the plurality of channels, thereby identifying a channel with a highest signal-to-noise (SNR) among the plurality of channels. The switch device is coupled to the micro-controller and configured to select an antenna corresponding to the channel with the highest SNR among the plurality of antennas for a connection between a selected antenna and the SISO radio.

公开(公告)号：US20190228272A1

公开(公告)日：2019-07-25

申请号：US15878188

申请日：2018-01-23

Applicant: Intelligent Fusion Technology, Inc

Inventor： Dan SHEN ,  Peter ZULCH ,  Marcello DISASIO ,  Erik BLASCH ,  Genshe CHEN ,  Zhonghai WANG ,  Jingyang LU

IPC: G06K9/62 ,  G06N7/08 ,  G06K9/68 ,  G06T7/80 ,  G01S13/58 ,  H04N5/33

Abstract: The present disclosure provides a method for joint manifold learning based heterogenous sensor data fusion, comprising: obtaining learning heterogeneous sensor data from a plurality sensors to form a joint manifold, wherein the plurality sensors include different types of sensors that detect different characteristics of targeting objects; performing, using a hardware processor, a plurality of manifold learning algorithms to process the joint manifold to obtain raw manifold learning results, wherein a dimension of the manifold learning results is less than a dimension of the joint manifold; processing the raw manifold learning results to obtain intrinsic parameters of the targeting objects; evaluating the multiple manifold learning algorithms based on the raw manifold learning results and the intrinsic parameters to determine one or more optimum manifold learning algorithms; and applying the one or more optimum manifold learning algorithms to fuse heterogeneous sensor data generated by the plurality sensors.