IP Library › Granted Patent US 10,591,586
Granted Patent B2
US 10,591,586 · App. 15/703,605 · Granted Mar 17, 2020

System and method for identifying a target using radar sensors

Inventors: Saverio Trotta (Munich, DE); Ashutosh Baheti (Munich, DE); Reinhard-Wolfgang Jungmaier (Aying, DE); Avik Santra (Munich, DE)
Assignee: INFINEON TECHNOLOGIES AG
G01S7/412G01S7/417G01S13/867G01S13/878G01S13/89G06F21/32G07C9/00158
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Quick Facts
Patent No.
US 10,591,586
App. No.
15/703,605
Granted
Mar 17, 2020
Kind
B2
Abstract

In accordance with an embodiment, a method of recognizing a biological target includes performing radar measurements for a plurality of sites on the biological target using a millimeter-wave radar sensor, producing a target data set for the plurality of sites based on the radar measurements, extracting features from the target data set, comparing the extracted features to stored features, and determining whether the extracted features match the stored features based on the comparing.

Claims (64)

1. A method of recognizing a biological target, the method comprising:

performing radar measurements for a plurality of sites on the biological target using a millimeter-wave radar sensor;

producing a target data set for the plurality of sites based on the radar measurements;

extracting features from the target data set;

producing a set of stored features comprising:

performing a set of reference radar measurements for a plurality of reference sites on the biological target using the millimeter-wave radar sensor,

producing a training data set for the plurality of reference sites based on the reference radar measurements, and

forming the stored features based on the training data set;

comparing the extracted features to the stored features; and

determining whether the extracted features match the stored features based on the comparing.

2. The method of claim 1 , wherein performing the radar measurements comprises forming a plurality of radar beams, wherein each of the plurality of radar beams are directed toward a corresponding site of the plurality of sites on the biological target.

3. The method of claim 1 , wherein:

the biological target comprises a human face; and

performing the radar measurements comprising performing the radar measurements on a plurality of sites on the human face.

4. The method of claim 3 , further comprising aligning the human face with the millimeter-wave radar sensor.

5. The method of claim 4 , wherein aligning comprises:

determining when the human face within a first distance of the millimeter-wave radar sensor; and

performing the radar measurements when the human face is within the first distance based on the determining.

6. The method of claim 5 , wherein the first distance is between 10 centimeters and 30 centimeters.

7. The method of claim 5 , wherein the aligning further comprises:

capturing an image of the human face using a camera; and

positioning the human face within a first region based on the captured image.

8. The method of claim 1 , wherein

the millimeter-wave radar sensor comprises a plurality of millimeter-wave radar sensor circuits; and

each of the plurality of millimeter-wave radar sensor circuits are associated with a corresponding site on the biological target.

9. The method of claim 1 , wherein comparing the extracted features to stored features comprises using at least one of a random forest algorithm or a neural network algorithm.

10. The method of claim 1 , wherein forming the stored features comprises iteratively adjusting the forming of the stored features using a machine learning algorithm.

11. The method of claim 10 , wherein:

forming the stored features comprises performing a FFT on the training data set; and

iteratively adjusting the forming of the stored features comprises adjusting parameters of the FFT based using the machine learning algorithm.

12. The method of claim 11 , wherein adjusting the parameters of the FFT comprises adjusting windowing of the FFT and adjusting zero padding of the training data set prior to performing the FFT.

13. The method of claim 11 , wherein performing the FFT comprises:

performing a range FFT for each target data set corresponding to each of the plurality of reference sites; and

performing a two-dimensional FFT over the plurality of reference sites for a selected time point.

14. The method of claim 13 , wherein:

the millimeter-wave radar sensor comprises a plurality of millimeter-wave radar sensors, each of the plurality of millimeter-wave radar sensors associated with a corresponding one of the plurality of sites; and

the method further comprises adjusting the range FFT and the two-dimensional FFT to compensate for range and azimuth migration of the plurality of millimeter-wave radar sensors.

15. The method of claim 1 , further comprising performing a first action when the extracted features match the stored features based on the determining.

16. A system comprising:

a processing system configured to be coupled to a millimeter-wave radar sensor, the processing system configured to

receive radar measurements for a plurality of sites of a biological target from the millimeter-wave radar sensor;

produce a target data set for the plurality of sites based on the radar measurements performed by the millimeter-wave radar sensor;

extract features from the target data set;

produce a set of stored features by:

receiving a set of reference radar measurements for a plurality of reference sites on the biological target from the millimeter-wave radar sensor,

producing a training data set for the plurality of reference sites based on the reference radar measurements, and

forming the stored features based on the training data set, wherein forming the stored features comprises iteratively adjusting the forming of the stored features using a machine learning algorithm;

compare the extracted features to stored features; and

determine whether the extracted features match the stored features based on the comparing.

17. The system of claim 16 , further comprising the millimeter-wave radar sensor, wherein the millimeter-wave radar sensor comprises a plurality of millimeter-wave radar sensor circuits.

18. The system of claim 17 , wherein the each of the plurality of millimeter-wave radar sensor circuits comprises two receive antenna and one transmit antenna.

19. The system of claim 17 , wherein each of the plurality of millimeter-wave radar sensor circuits comprises four receive antennas and two transmit antennas.

20. The system of claim 17 , wherein the plurality of millimeter-wave radar sensor circuits are arranged in at least one of a linear array or a rectangular array.

21. The system of claim 20 , wherein the linear array is a uniform linear array and the rectangular array is a uniform rectangular array.

22. A non-transitory computer readable storage medium with an executable program stored thereon, the executable program including instructions to:

receive radar measurements for a plurality of sites of a biological target from a millimeter-wave radar sensor;

produce a target data set for the plurality of sites based on the radar measurements performed by the millimeter-wave radar sensor;

extract features from the target data set;

produce a set of stored features by:

receiving a set of reference radar measurements for a plurality of reference sites on the biological target from the millimeter-wave radar sensor,

producing a training data set for the plurality of reference sites based on the reference radar measurements, and

forming the stored features based on the training data set, wherein forming the stored features comprises iteratively adjusting the forming of the stored features using a machine learning algorithm;

compare the extracted features to stored features; and

determine whether the extracted features match the stored features based on the comparing.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 13, 2017
From: TROTTA, SAVERIO; BAHETI, ASHUTOSH; JUNGMAIER, REINHARD-WOLFGANG; SANTRA, AVIK
To: INFINEON TECHNOLOGIES AG
Reel/Frame 043578/0628 →
Continuity (2)
Provisional Application 62529967 · Jul 7, 2017
Related Publication 20190011534A1 · Jan 10, 2019