US6259396B1
Expired / Lapsed
US
⏱️ 0.0 years remaining
Target acquisition system and radon transform based method for target azimuth aspect estimation
Assignee: Raytheon Co
•
Filing: 1999-08-26
•
Granted: 2001-07-10
•
Expires: 2019-08-26
Abstract
Abstract A Radon transform based method that provides for target azimuth aspect estimation and target shape measurement. A Radon transform is applied to a binary (N×N pixels) target chip to extract target features that are then used to measure length, width, and diagonal features of the target. In parallel, these features are used to estimate the azimuth aspect angle, or size, of the target. The method is effective in discriminating targets from clutter. The method is also very time efficient and highly flexible in its operation because the features can automatically account for any target rotation or shift. The present invention also provides for a target acquisition system that employs the Radon transform based method for target azimuth aspect estimation.
Inventors
- 👤 Quoc H. Pham
- 👤 Albert Ezekiel
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-27 18:06
CPC Classifications
G; G01; G01S; G01S7/00; G01S7/02; G01S7/41; G01S7/411; G01S7/412; G01S13/00; G01S13/88; G01S13/89; G01S13/90; G01S13/9021; G01S13/9027; G01S13/9094; G06; G06T; G06T7/00; G06T7/60; G06V; G06V10/00; G06V10/20; G06V10/255
Claims Analysis
Total Claims: 14
Claim 1
Independent Claim
91 words
A method for use in target azimuth aspect estimation, comprising the steps of: acquiring a binary image chip containing a predetermined region comprising a block of pixels of an image area with a target located therein; processing the pixels of the binary image chip containing a predetermined region using a two-dimensional Radon Transform to extract target features therefrom; estimating the length, width, and diagonal of the target using the extracted target features; and estimating azimuth aspect angle, or size, of the target using the length, width, and diagonal of the target.
Claim 2
Independent Claim
91 words
A method for use in target azimuth aspect estimation, comprising the steps of: acquiring a binary image chip containing a predetermined region comprising a block of pixels of an image area with a target located therein; processing the pixels of the binary image chip containing a predetermined region using a two-dimensional Radon Transform to extract target features therefrom; estimating the length, width, and diagonal of the target using the extracted target features; and estimating azimuth aspect angle, or size, of the target using the length, width, and diagonal of the target.
Claim 3
Dependent Claim
31 words
The method recited in claim 1 wherein the binary image chip containing a predetermined region comprises an image area with a target located therein mapped by a synthetic aperture radar system.
Claim 4
Dependent Claim
31 words
The method recited in claim 1 wherein the binary image chip containing a predetermined region comprises an image area with a target located therein derived from a forward looking infrared system.
Claim 5
Dependent Claim
30 words
The method recited in claim 1 wherein the binary image chip containing a predetermined region comprises an image area with a target located therein derived from a laser radar system.
Claim 6
Dependent Claim
21 words
The method recited in claim 1 wherein the features extracted from Radon transform a re processed to classify the target type.
Claim 7
Dependent Claim
21 words
The method recited in claim 1 wherein the features extracted from Radon transform are processed to search for a target index.
Claim 8
Dependent Claim
83 words
An automatic target recognition system for use with a synthetic aperture radar system to automatically detect target locations and classify target types, comprising: a detection stage comprising a focus of attention algorithm for identifying possible target locations in a full scene image; a discrimination stage comprising a Radon transform based feature extraction method that processes areas located by the focus of attention algorithm and discriminates the features as targets or clutter; and a template based classification stage for identifying targets as predetermined types.
Claim 9
Dependent Claim
83 words
An automatic target recognition system for use with a synthetic aperture radar system to automatically detect target locations and classify target types, comprising: a detection stage comprising a focus of attention algorithm for identifying possible target locations in a full scene image; a discrimination stage comprising a Radon transform based feature extraction method that processes areas located by the focus of attention algorithm and discriminates the features as targets or clutter; and a template based classification stage for identifying targets as predetermined types.
Claim 10
Dependent Claim
19 words
The system recited in claim 7 wherein the discrimination stage comprises region segmentation, feature extraction, and discrimination statistic generation.
Claim 11
Dependent Claim
26 words
The system recited in claim 8 wherein region segmentation is performed using two parameter constant false alarm rate detection followed by morphology operations and feature clustering.
Claim 12
Dependent Claim
112 words
The system recited in claim 9 wherein constant false alarm rate detection is implemented using a two parameter constant false alarm rate detection rule defined as f ( n,m )=1 if ( x ( n,m−μ c /σ c )≧ Th 1 ; 2 if ( x ( n,m−μ c /σ c )≦ Th 2 ; 0 otherwise, where x(n,m) is the amplitude of a test pixel, μ c and σ c are the estimated mean and standard deviation of the pixels contained within a constant false alarm rate window, Th 1 and Th 2 are constant false alarm rate constants, 0 represents background, 1 denotes a target, and 2 symbolizes a shadow.
Claim 13
Dependent Claim
24 words
The system recited in claim 8 wherein the morphology operation comprises a cascade of stages including successive applications of dilations, erosions, and rank filtering.
Claim 14
Dependent Claim
24 words
The system recited in claim 8 wherein the classification stage contains target azimuth aspect prediction, reference library modeling, and a mean square error classifier.
References Cited (Backward Citations)
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2004-07-09 | 2005-01-08 | 2005-07-10 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2008-07-09 | 2009-01-08 | 2009-07-10 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2012-07-09 | 2013-01-08 | 2013-07-10 | $7700.00 | Window Closed / Lapsed |