Target detection utilizing image array comparison
US-2015061926-A1 · Mar 5, 2015 · US
US9702971B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-9702971-B2 |
| Application number | US-201414215737-A |
| Country | US |
| Kind code | B2 |
| Filing date | Mar 17, 2014 |
| Priority date | Mar 17, 2014 |
| Publication date | Jul 11, 2017 |
| Grant date | Jul 11, 2017 |
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A system and method for high-availability inverse synthetic aperture radar (ISAR). In one embodiment a set of quadratic phase vectors, each corresponding to a different acceleration, is multiplied, one at a time, in a Hadamard product, with a 3-dimensional data cube, and a fast Fourier transform (FFT) is taken of the result, to form a 2-dimensional array. The two-dimensional array is made sparse by setting to zero elements that fall below a threshold based on a coherency metric, and the sparse arrays are stacked to form a sparse 3-dimensional image. Projections of the sparse 3-dimensional image are formed for presentation to an operator or an image exploitation system.
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What is claimed is: 1. A method for generating a high-availability inverse synthetic aperture radar (ISAR) image, the method comprising: forming, by a processing unit, a range resolved data cube over subapertures, fine range bins, and coarse Doppler bins, the range resolved data cube comprising a plurality of first data vectors in the subaperture direction; forming a plurality of product vectors from the first data vectors and a plurality of quadratic phase vectors; performing a fast Fourier transform (FFT) of each of the plurality of product vectors to form a plurality of two-dimensional arrays; forming a plurality of sparse two-dimensional arrays by calculating a coherency metric for each element of each of the plurality of two-dimensional arrays; and stacking the plurality of sparse two-dimensional arrays to form a sparse 3-dimensional (3-D) image. 2. The method of claim 1 , wherein the forming of the plurality of product vectors from the plurality of first data vectors and the plurality of quadratic phase vectors comprises forming, for each of the plurality of quadratic phase vectors, a plurality of Hadamard products of: the plurality of first data vectors; and the quadratic phase vector. 3. The method of claim 1 , wherein the j th quadratic phase vector, of the plurality of quadratic phase vectors, comprises values calculated according to the equation: quadratic_phase ( j ) = - qpes [ j ] · 2 T ISAR 2 · t sub 2 , wherein: t sub 2 is an element-by-element square of a vector t sub with elements given by t sub = prfHz δ · ( q - ceil ( N sub + 1 2 ) ) , with the value of the index q ranging from 1 to N sub , qpes[j] is a j th quadratic phase error, from a set of quadratic phase errors, with a stride of π radians, ranging from −qpe to qpe, with qpe = 4 π λ A LOS 2 ( T ISAR 2 ) 2 , A LOSmax is an estimated worst-case line-of-sight acceleration in the scene, λ is a radar carrier wavelength, T ISAR is an aperture time for ISAR image formation, N sub is a number of subapertures in the range resolved data cube, prfHz is a radar pulse repetition frequency, and δ is a number of pulses between the start of a subaperture and the start of a subsequent subaperture in the range resolved data cube. 4. The method of claim 3 , wherein the estimated worst-case line-of-sight acceleration is formed by a spatially-varying autofocus (SVAF) block. 5. The method of claim 1 , wherein the forming of the plurality of sparse two-dimensional arrays by calculating the coherency metric for each element of each of the plurality of two-dimensional arrays comprises setting to zero array elements the absolute value of the coherency metric of which is less than a threshold value. 6. The method of claim 5 , wherein the threshold value is 0.1. 7. The method of claim 1 , comprising forming a projection of the sparse 3-D image to form a viewable image. 8. The method of claim 7 , wherein the forming of the projection of the sparse 3-D image to form the viewable image comprises forming a projection onto a coordinate plane. 9. The method of claim 7 , wherein the forming of the projection of the sparse 3-D image to form a viewable image comprises forming a projection onto a plane determined by principal component analysis (PCA) of the sparse 3-D image. 10. A system for generating a high-availability inverse synthetic aperture radar (ISAR) image, the system comprising a processing unit configured to: form a range resolved data cube over subapertures, fine range bins, and coarse Doppler bins, the range resolved data cube comprising a plurality of first data vectors in the subaperture direction; form a plurality of product vectors from the first data vectors and a plurality of quadratic phase vectors; perform a fast Fourier transform (FFT) of each of the plurality of product vectors to form a plurality of two-dimensional arrays; form a plurality of sparse two-dimensional arrays by calculating a coherency metric for each element of each of the plurality of two-dimensional arrays; and stack the plurality of sparse two-dimensional arrays to form a sparse 3-dimensional (3-D) image. 11. The system of claim 10 , wherein the forming of the plurality of product vectors from the plurality of first data vectors and the plurality of quadratic phase vectors comprises forming, for each of the plurality of quadratic phase vectors, a plurality of Hadamard products of: the plurality of first data vectors; and the quadratic phase vector. 12. The system of claim 10 , wherein the j th quadratic phase vector, of the plurality of quadratic phas
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Inverse SAR [ISAR] · CPC title
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