High-definition imaging apparatus and method
First Claim
1. A method of processing image data to produce a high-definition image, comprising the steps of:
- receiving the image data; and
adaptively processing the image data using a constrained minimum variance method to iteratively compute the high-definition image, wherein the high-definition image I is expressed in range and cross-range as I(r,c)=minω
HRω
, where ω
is a weighting vector and R is a covariance matrix of the image data, wherein a solution for I(r,c) is approximated by i) forming Y=[x1 . . . xK]T/{square root over (K)}, where x1 . . . xk are beamspace looks formed from image domain looks and with y1, y2, and y3 denoting the K×
1 columns of Y;
ii) computing r21=y2Ty1 and r31=y3Ty1, and b=r21y2+r31y3;
computing γ
as and iii) computing I(r,c) as I(r,c)=∥
y1−
γ
b∥
2.
2 Assignments
0 Petitions
Accused Products
Abstract
A high-definition radar imaging system and method receives image data and adaptively processes the image the data to provide a high resolution image. The imaging technique employs adaptive processing using a constrained minimum variance method to iteratively compute the high-definition image. The high-definition image I is expressed in range and cross-range as I(r,c)=minωHRω, where ω is a weighting vector and R is a covariance matrix of the image data. A solution for I(r,c) is approximated by i) forming Y=[x1 . . . xK]T/{square root over (K)} where x1 . . . xk are beamspace looks formed from image domain looks and with y1, y2, and y3 denoting the K×1 columns of Y; ii) computing r21=y2Ty1 and r31=y3Ty1, and b=r21y2+r31y3; computing γ as
and iii) computing I(r,c) as I(r,c)=∥y1−γb∥2.
-
Citations
18 Claims
-
1. A method of processing image data to produce a high-definition image, comprising the steps of:
-
receiving the image data; and
adaptively processing the image data using a constrained minimum variance method to iteratively compute the high-definition image, wherein the high-definition image I is expressed in range and cross-range as I(r,c)=minω
HRω
, where ω
is a weighting vector and R is a covariance matrix of the image data, wherein a solution for I(r,c) is approximated by i) forming Y=[x1 . . . xK]T/{square root over (K)}, where x1 . . . xk are beamspace looks formed from image domain looks and with y1, y2, and y3 denoting the K×
1 columns of Y;
ii) computing r21=y2Ty1 and r31=y3Ty1, and b=r21y2+r31y3;
computing γ
asand iii) computing I(r,c) as I(r,c)=∥
y1−
γ
b∥
2.- View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
forming image domain looks by;
i) applying a FFT to convert the complex-valued image data into M1×
M2 frequency-domain data;
ii) removing any weighting function;
iii) generating a plurality of frequency domain looks as a plurality of ML,1×
ML,2 subsets of the M1×
M2 frequency domain data, where ML,i is an integer near nMi with 0<
n<
1, i=1,2; and
iv) transforming each frequency domain look into a corresponding image domain look using a 2ML,1×
2ML,2 inverse FFT, packing the data such that it is centered about zero frequency.
-
-
3. The method of claim 2, further comprising the step of:
for each image domain look, generating one beamspace look using a real part of the image domain look and generating a second beamspace look using an imaginary part of the image domain look.
-
4. The method of claim 1, further comprising the step of:
combining the high-definition image with an unweighted image and a Taylor weighted image to form a more detailed image by taking a pixel-by-pixel minimum of the high-definition image, unweighted image, and Taylor weighted image.
-
5. The method as per claim 4, wherein a filter is applied to sharpen the more detailed image.
-
6. The method of claim 1, wherein the image data is 2-D complex-valued image data.
-
7. The method of claim 6, wherein the 2-D image data is SAR data.
-
8. The method of claim 1, wherein the image data is 1-D image data.
-
9. The method of claim 8, wherein the 1-D image data is high range resolution profile data.
-
10. A system for processing image data to produce a high-definition image, comprising:
-
a preprocessing routine to receive the image data and generate a plurality of image domain looks;
a make beamspace looks routine to generate k beamspace looks, x1 . . . xk, from the plurality of image domain looks;
a minimum variance method routine to iteratively compute the high-definition image from the beamspace looks, wherein the high-definition image I is expressed in range and cross-range as I(r,c)=minω
HRω
, where ω
is a weighting vector and R is a covariance matrix of the image data, wherein a solution for I(r,c) is approximated by i) forming Y=[x1 . . . xK]T/{square root over (K)}, with y1, y2, and y3 denoting the K×
1 columns of Y;
ii) computing r21=y2Ty1 and r31=y3Ty1, and b=r21y2+r31y3;
computing γ
asand iii) computing I(r,c) as I(r,c)=∥
y1−
γ
b∥
2.- View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
i) applying a FFT to convert the complex-valued image data into M1×
M2 frequency-domain data;
ii) removing any weighting function;
iii) generating a plurality of frequency domain looks as a plurality of ML,1×
ML,2 subsets of the M1×
M2 frequency domain data, where ML,i is an integer near nMi with 0<
n<
1, i=1,2; and
iv) transforming each frequency domain look into a corresponding image domain look using a 2ML,1×
2ML,2 inverse FFT, packing the data such that it is centered about zero frequency.
-
-
12. The system of claim 11, wherein, for each image domain look, one beamspace look is generated using a real part of the image domain look and a second beamspace look is generated using an imaginary part of the image domain look.
-
13. The system of claim 10, further comprising:
an image combining routine to combine the high-definition image with an unweighted image and a Taylor weighted image to form a more detailed image by taking a pixel-by-pixel minimum of the high-definition image, unweighted image, and Taylor weighted image.
-
14. The system of claim 13, wherein the image combining routine applies a filter to sharpen the more detailed image.
-
15. The system of claim 10, wherein the image data is 2-D complex-valued image data.
-
16. The system of claim 15, wherein the 2-D image data is SAR data.
-
17. The system of claim 10, wherein the image data is 1-D data.
-
18. The system of claim 17, wherein the 1-D image data is high range resolution profile data.
Specification