Invention Application
- Patent Title: RADAR DEEP LEARNING
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Application No.: PCT/US2019/063849Application Date: 2019-11-29
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Publication No.: WO2020113160A2Publication Date: 2020-06-04
- Inventor: FONTIJNE, Daniel Hendricus Franciscus , ANSARI, Amin , MAJOR, Bence , SUKHAVASI, Ravi Teja , GOWAIKAR, Radhika Dilip , WU, Xinzhou , SUBRAMANIAN, Sundar , HAMILTON, Michael John
- Applicant: QUALCOMM INCORPORATED
- Applicant Address: ATTN: International IP Administration 5775 Morehouse Drive San Diego, California 92121-1714 US
- Assignee: QUALCOMM INCORPORATED
- Current Assignee: QUALCOMM INCORPORATED
- Current Assignee Address: ATTN: International IP Administration 5775 Morehouse Drive San Diego, California 92121-1714 US
- Agency: OLDS, Mark E.
- Priority: US62/774,018 20181130; US16/698,870 20191127
- Main IPC: G01S7/41
- IPC: G01S7/41 ; G01S13/86 ; G01S13/89 ; G01S13/931 ; G01S17/89 ; G01S17/931 ; G06N3/02
Abstract:
Disclosed are techniques for employing deep learning to analyze radar signals. In an aspect, an on-board computer of a host vehicle receives, from a radar sensor of the vehicle, a plurality of radar frames, executes a neural network on a subset of the plurality of radar frames, and detects one or more objects in the subset of the plurality of radar frames based on execution of the neural network on the subset of the plurality of radar frames. Further, techniques for transforming polar coordinates to Cartesian coordinates in a neural network are disclosed. In an aspect, a neural network receives a plurality of radar frames in polar coordinate space, a polar-to-Cartesian transformation layer of the neural network transforms the plurality of radar frames to Cartesian coordinate space, and the neural network outputs the plurality of radar frames in the Cartesian coordinate space.
Public/Granted literature
- WO2020113160A3 RADAR DEEP LEARNING Public/Granted day:2020-06-04
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