US 12,455,365 B2
Manipulation of radar readings
Hendrik Lambertus Muller, Cumbria (GB); and Douglas Roger Pulley, Bath (GB)
Assigned to XMOS LTD, Bristol (GB)
Appl. No. 18/037,153
Filed by XMOS LTD, Bristol (GB)
PCT Filed Aug. 26, 2021, PCT No. PCT/EP2021/073586
§ 371(c)(1), (2) Date May 16, 2023,
PCT Pub. No. WO2022/122195, PCT Pub. Date Jun. 16, 2022.
Claims priority of application No. 2019402 (GB), filed on Dec. 9, 2020.
Prior Publication US 2023/0417896 A1, Dec. 28, 2023
Int. Cl. G01S 13/58 (2006.01); G01S 13/42 (2006.01); G01S 13/89 (2006.01)
CPC G01S 13/584 (2013.01) [G01S 13/426 (2013.01); G01S 13/89 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
a radar sensor configured to emit a radar signal and receive back reflections thereof, and thereby produce a set of radar readings distributed amongst a plurality of distance bins each corresponding to a different respective distance of reflection, wherein in each distance bin that contains a radar reading, the radar reading comprises at least a respective azimuth angle and elevation of the radar reading;
an image projection module configured to project at least some of the radar readings onto an image comprising a 2D Cartesian grid of pixels based on the respective azimuth angles and elevations, wherein for each pixel where a radar reading is present the pixel comprises a respective value of at least one non-binary channel comprising at least one of measured property of the radar reading and a respective value of a binary channel comprising a binary indicator,
wherein the binary indicator is asserted if the pixel contains one of the radar readings but not asserted otherwise, or
the binary indicator is asserted if the radar reading contains a radar reading above a noise floor but not asserted if the pixel contains a radar reading below the noise floor; and
a machine learning model for image recognition, arranged to receive the image and to detect an object therein, wherein the machine learning model is configured to perform the detection based on at the values of the binary and non-binary channels for each pixel, including pixels with both asserted and non-asserted values of the binary channel.