RESOLUTION ENHANCEMENT SYSTEM (RES) FOR NETWORKED RADARS

• US 20110102249A1
• Filed: 10/20/2010
• Published: 05/05/2011
• Est. Priority Date: 10/20/2009
• Status: Active Grant

• Current Assignee: Colorado State University Research Foundation (Colorado State University System)
• Original Assignee: Colorado State University Research Foundation (Colorado State University System)
• Inventors: Bharadwaj, Nitin, Venkatachalam, Chandrasekaran
• Granted Patent: US 8,462,040 B2
• Time in Patent Office: Days
• US Class Current: 342/26.R
• CPC Class Codes: G01S 13/003 Bistatic radar systems; Multistatic radar systems
• CPC Class Codes: G01S 13/582 adapted for simultaneous range and velocity measurements
• CPC Class Codes: G01S 13/878 Combination of several spaced transmitters or receivers of known location for determining the position of a transponder or a reflector G01S13/874 takes precedence
• CPC Class Codes: G01S 13/951 ground based
• CPC Class Codes: G01S 7/024 using polarisation effects in waveguides H01P1/165; for aerials H01Q, e.g. H01Q15/22, H01Q15/24, H01Q19/195
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Embodiments provide methods, systems, and/or devices that can provide measurements of the inherent reflectivity distribution from different look angles using N radar nodes. Doppler weather radars generally operate with very good spatial resolution in range and poor cross range resolution at farther ranges. Embodiments provide methodologies to retrieve higher resolution reflectivity data from a network of radars. In a networked radar environment, each radar may observe a common reflectivity distribution with different spreading function. The principle that the underlying reflectivity distribution should remain identical for all the nodes may be used to solve the inverse problem to determine intrinsic reflectivities.

Claims

1. A method of operating a radar network, the method comprising:

generating respective radar beams with each of a plurality of radars disposed at different positions within an environment;

determining a plurality of respective measured reflectivities of the environment along a respective path of each of the respective radar beams from the generated respective radar beams, wherein each respective measured reflectivity has a respective cross-azimuthal resolution; and

determining a plurality of intrinsic reflectivities for a plurality of volume elements within the environment from the plurality of respective measured reflectivities along the respective path of each of the respective radar beams, wherein the respective intrinsic reflectivities for the respective volume elements each has a respective resolution greater than the respective cross-azimuthal resolutions of the measured reflectivities.

2. The method of claim 1, further comprising utilizing a spreading function for each of the plurality of radars to determine the intrinsic reflectivities for the plurality of volume elements.

3. The method of claim 2, wherein utilizing the spreading function for each of the plurality of radars to determine the intrinsic reflectivities for the plurality of volume elements utilizes a spreading function that is position variant.

4. The method of claim 2, wherein determining the intrinsic reflectivities further comprises solving a minimization problem described by min∥

G_nz_vec−

z_gm∥

₂², where G_n=Σ

_j=1^NG_j^TG_j represents a networked radar transformation matrix, z_gm=Σ

_j=1^NG_j^Tz_m(j) represents a network transformed measured reflectivity, G_j represents the spreading function for a jth radar, N equals the number of radars in the plurality of radars, and z_m(j)=G(j)z_vec represents a vector of the plurality of measured reflectivities for the jth radar that correspond to z_vec that represents a vectorized intrinsic reflectivity matrix.

5. The method of claim 1, wherein determining the plurality of respective measured reflectivities further comprises correcting for attenuation along at least a portion of the respective path of at least one of the respective radar beams.

8. The method of claim 1, wherein determining the plurality of intrinsic reflectivities for the plurality of volume elements further comprises dividing a common coverage area into a plurality of regions, wherein each region includes at least a subset of the plurality of volume elements, and processing each region in parallel to determine the plurality of intrinsic reflectivities.

9. The method of claim 1, wherein at least two of the respective radar beams have different frequencies.

10. The method of claim 1, wherein at least one of the respective radar beams has an X-band frequency.

6. The method of 1, further comprising utilizing a hexagonal grid for sampling the plurality of intrinsic reflectivities for the plurality of volume elements within the environment.

7. The method of 1, further comprising utilizing a Cartesian grid for sampling the plurality of intrinsic reflectivities for the plurality of volume elements within the environment.

11. A networked-radar evaluation system comprising:

a communications device;

a storage device;

a processor in communication with the communications device and with the storage device; and

a memory coupled with the processor, the memory comprising a computer-readable medium having a computer-readable program embodied therein for directing operation of the processing system to retrieve a plurality of intrinsic reflectivities from an environment, the computer-readable program comprising;

instructions for receiving, with the communications device, a respective measured reflectivity of the environment along a respective path of each of a plurality of radar beams generated from respective ones of a plurality of radars disposed at different positions within the environment, wherein each respective measured reflectivity has a respective cross-azimuthal resolution; and

instructions for determining, with the processor, the plurality of intrinsic reflectivities for different volume elements within the environment from the respective measured reflectivity along the respective path of each of the plurality of radar beam, wherein the respective intrinsic reflectivities for the respective volume elements each has a respective resolution greater than the respective cross-azimuthal resolutions of the measured reflectivities.

12. The networked-radar evaluation system of claim 11, wherein the instructions for determining the intrinsic reflectivities for the plurality of volume elements comprises instructions for utilizing a spreading function for each of the plurality of radars.

13. The networked-radar evaluation system of claim 12, wherein the instructions for utilizing the spreading function for each of the plurality of radars to determine the intrinsic reflectivities for the plurality of volume elements comprises utilizing a spreading function that is position variant.

14. The networked-radar evaluation system of claim 12, wherein the instructions for determining the intrinsic reflectivities comprise instructions for solving a minimization problem described by min∥

G_nz_vec−

z_gm∥

₂², where G_n=Σ

_j=1^NG_j^TG_j represents a networked radar transformation matrix, and z_gm=Σ

_j=1^NG_j^Tz_m(j) represents a network transformed measured reflectivity, G_j represents the spreading function for a jth radar, N equals the number of radars in the plurality of radars, z_m(j)=G(j)z_vec represents a vector of the plurality of measured reflectivities for the jth radar that correspond to z_vec that represents a vectorized intrinsic reflectivity matrix.

15. The networked-radar evaluation system of claim 11, wherein the instructions for determining the plurality of respective measured reflectivities further comprise instructions for correcting for attenuation along at least a portion of the respective path of at least one of the respective radar beams.

16. The networked-radar evaluation system of claim 11, further comprising instructions for utilizing a hexagonal grid for sampling the plurality of intrinsic reflectivities for the plurality of volume elements within the environment.

17. The networked-radar evaluation system of claim 11, further comprising instructions for utilizing a Cartesian grid for sampling the plurality of intrinsic reflectivities for the plurality of volume elements within the environment.

18. The networked-radar evaluation system of claim 11, wherein at least two of the respective radar beams have different frequencies.

19. The networked-radar evaluation system of claim 11, wherein at least one of the respective radar beams has an X-band frequency.

20. A radar network comprising:

a plurality of radars disposed at different positions within an environment; and

a computational unit interfaced with the plurality of radars, the computational unit having instructions to determine a plurality of intrinsic reflectivities for a plurality of volume elements within the environment from a plurality of respective measured reflectivities along a respective path of each of a plurality of respective radar beams, wherein each respective measured reflectivity has a respective cross-azimuthal resolution and the respective intrinsic reflectivities for the respective volume elements each has a respective resolution greater than the respective cross-azimuthal resolutions of the measured reflectivities.

21. The radar network of claim 20, wherein the computational unit further has instructions to utilize a spreading function for each of the plurality of radars to determine the intrinsic reflectivities for the plurality of volume elements.

22. The radar network of claim 21, wherein the instructions to utilize the spreading function for each of the plurality of radars to determine the intrinsic reflectivities for the plurality of volume elements utilizes a spreading function that is position variant.

23. The radar network of claim 21, wherein the computational unit further has instructions for determining the intrinsic reflectivities further comprising instructions to solve a minimization problem described by min∥

G_nz_vec−

z<sub>gm∥

2</sub>², where G_n=Σ

_j=1^NG_j^TG_j represents a networked radar transformation matrix, z_gm=Σ

_j=1^NG_j^Tz_m(j) represents a network transformed measured reflectivity, G_j represents the spreading function for a jth radar, N equals the number of radars in the plurality of radars, z_m(j)=G(j)z_vec represents a vector of the plurality of measured reflectivities for the jth radar that correspond to z_vec that represents a vectorized intrinsic reflectivity matrix.

24. The radar network of claim 20, wherein the instructions to determine the plurality of respective measured reflectivities further comprise instructions to correct for attenuation along at least a portion of the respective path of at least one of the respective radar beams.

25. The radar network of claim 20, wherein the computational unit further has instructions to utilize a hexagonal grid for sampling the plurality of intrinsic reflectivities for the plurality of volume elements within the environment.

26. The radar network of claim 20, wherein the computational unit further has instructions to utilize a Cartesian grid for sampling the plurality of intrinsic reflectivities for the plurality of volume elements within the environment.

27. The radar network of claim 20, wherein at least two of the respective radar beams have different frequencies.

28. The radar network of claim 20, wherein at least one of the respective radar beams has an X-band frequency.