System and method of monitoring an air flow using a millimeter-wave radar sensor

1. A method of monitoring an air flow, the method comprising:

performing a plurality of radar measurements using a millimeter-wave radar sensor with a line of sight having a component perpendicular to a direction of the air flow;

detecting an object in the air flow based on the plurality of radar measurements;

calculating a displacement of the object and a size of the object based on the plurality of radar measurements;

estimating a velocity of the object based on the calculated displacement; and

calculating a risk metric based on the calculated size of the object and estimated velocity of the object.

2. The method of claim 1 , wherein detecting the size of the object comprises:

forming a two-dimensional radar image from the plurality of radar measurements, the two-dimensional radar image having a range axis and an azimuth axis;

iteratively applying a two-dimensional Gaussian filter to the two-dimensional radar image with a changing filter variance;

determining a maximum filter variance at which an increase of a sum amplitude of the Gaussian filtered two-dimensional radar image is below a first threshold to form a variance value; and

determining the sum amplitude of the Gaussian filtered two-dimensional radar image.

3. The method of claim 2 , further comprising:

determining size category based on the determined maximum filter variance;

determining a radar cross section (RCS) category based on the sum amplitude;

determining a velocity category based on a magnitude of the estimated velocity; and

determining an incidence category based on an angle of the estimated velocity.

4. The method of claim 3 , wherein:

determining the size category comprises categorizing the detected object in a “large object” category when the maximum filter variance is greater than a first size threshold value and categorizing the detected object in a “small object” category when the maximum filter variance is not greater than the first size threshold value;

determining the RCS category comprises categorizing the detected object in a first RCS category when the sum amplitude is greater than a first RCS threshold, categorizing the detected object in a second RCS category when the sum amplitude is greater than a second RCS threshold and not greater than the first RCS threshold, and categorizing the detected object in a third RCS category when the sum amplitude not greater than the second RCS threshold;

determining the velocity category comprises categorizing the detected object in a “high velocity” category when the magnitude of the estimated velocity is greater than a first velocity threshold value and categorizing the detected object in a “low object” category when the magnitude of the estimated velocity is not greater than the first velocity threshold value; and

determining the incidence category comprises categorizing the detected object in a “direct” category when the angle of the estimated velocity with respect to the direction of the air flow is less than a first angle threshold, and categorizing the detected object in an “indirect” category when the angle of the estimated velocity with respect to the direction of the air flow is not less than the first angle threshold.

5. The method of claim 2 , wherein calculating the displacement of the object comprises:

forming the two-dimensional radar image for each radar measurement of the plurality of radar measurements;

estimating a position of the detected object for each radar measurement of the plurality of radar measurements based on the two-dimensional radar images; and

calculating a Euclidean distance between the estimated position of the detected object from a first radar measurement of the plurality of radar measurements and the estimated position of the detected object from a second radar measurement of the plurality of radar measurements.

6. The method of claim 5 , wherein estimating the velocity of the object comprises determining a magnitude of the velocity of the detected object and an angle of the velocity of the detected object based on the calculated Euclidian distance.

7. The method of claim 1 , wherein the performing the plurality of radar measurements comprises forming a beam using an antenna array of the millimeter-wave radar sensor.

8. The method of claim 1 , wherein calculating the risk metric based on the calculated size of the object and the estimated velocity of the object comprises performing a weighted sum of the calculated size of the object and the estimated velocity.

9. The method of claim 1 , further comprising transmitting an alert when the risk metric is greater than a risk metric threshold.

10. The method of claim 1 , wherein the air flow occurs within a duct, and at least a portion of the duct is within the line of sight of the millimeter-wave radar sensor.

11. A method of monitoring an air flow, the method comprising:

performing a plurality of radar measurements using a millimeter-wave radar sensor having a line of sight collinear to a direction of the air flow;

detecting an object in the air flow based on the plurality of radar measurements;

calculating a size and trajectory of the detected object based on the plurality of radar measurements; and

calculating a risk metric based on the calculated size of the object and calculated trajectory of the object.

12. The method of claim 11 , wherein calculating the trajectory of the object comprises:

performing a range FFT from the plurality of radar measurements;

performing a Doppler FFT from the range FFT to produce a two-dimensional range-Doppler map; and

estimating a range, azimuth angle and elevation angle of the detected object based on from the two-dimensional range-Doppler map.

13. The method of claim 12 , wherein calculating the trajectory of the detected object further comprises determining a velocity of the detected object.

14. The method of claim 12 , wherein estimating the azimuth angle and the elevation angle comprises applying a minimum variance distortionless response (MVDR) algorithm to the two-dimensional range-Doppler map.

15. The method of claim 12 , wherein detecting the size of the object comprises:

determining a radar cross section (RCS) from the range FFT.

16. The method of claim 11 , wherein calculating the risk metric based on the calculated size of the object and the calculated trajectory the object comprises performing a weighted sum of the calculated size of the object and the calculated trajectory of the object.

17. The method of claim 16 , further comprising transmitting an alert when the risk metric is greater than a risk metric threshold.

18. The method of claim 11 , further comprising determining a structural integrity of a fan generating the air flow from the plurality of radar measurements.

19. The method of claim 18 , wherein the fan is disposed adjacent to or within a duct, and at least a portion of the duct is within the line of sight of the millimeter-wave radar sensor.

20. The method of claim 18 , wherein determining the structural integrity of the fan comprises:

creating an inverse synthetic-aperture radar (ISAR) image from the plurality of radar measurements;

extracting a micro-Doppler signature of the fan from the ISAR image; and

estimating the structural integrity of the fan from the extracted micro-Doppler signature.

21. The method of claim 20 , wherein the estimating the structural integrity from the extracted micro-Doppler signature comprises classifying the extracted micro-Doppler signature using a machine learning algorithm.

22. The method of claim 18 , further comprising time multiplexing the steps of detecting the object in the air flow and determining the structural integrity of the fan.

23. The method of claim 18 , further comprising frequency multiplexing the steps of detecting the object in the air flow and determining the structural integrity of the fan.

24. A method of monitoring a structural integrity of a rotating object, the method comprising:

performing a plurality of radar measurements of the rotating object using a plurality millimeter-wave radar sensors;

creating an inverse synthetic-aperture radar (ISAR) image from the plurality of radar measurements;

extracting a micro-Doppler signature of the rotating object from the ISAR image; and

estimating the structural integrity of the rotating object from the extracted micro-Doppler signature.

25. The method of claim 24 , further comprising classifying the extracted micro-Doppler signature using a machine learning algorithm.

26. The method of claim 24 , further comprising determining a vibrational mode of the rotating object from the extracted micro-Doppler signature.

27. A system for monitoring an air flow, the system comprising:

a millimeter-wave radar sensor having a line of sight having a component perpendicular to a direction of the air flow;

a processor coupled to the millimeter-wave radar sensor, the processor configured to

detect an object in the air flow based on radar measurements from the millimeter-wave radar sensor,

calculate a displacement of the object and a size of the object based on the radar measurements,

estimate a velocity of the object based on the calculated displacement; and

calculate a risk metric based on the calculated size of the object and estimated velocity of the object.