A-scan ISAR classification system and method therefor

Communications: directive radio wave systems and devices (e.g. – Return signal controls radar system – Receiver

Reexamination Certificate

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C342S02500R

Reexamination Certificate

active

06337654

ABSTRACT:

BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention generally relates to radar systems and radar signature identification methods and, more particularly, to methods and systems for automatically classifying targets imaged on Inverse Synthetic Aperture Radar systems.
2. Background Description
Synthetic Aperture Radars (SARs) produce a two-dimensional (2-D) image (with intensity as a pseudo non-spatial third image dimension) where one dimension in the image is a range and the other dimension is an azimuth (or cross range). Range is a measure of the “line-of-sight” distance from the radar to the target and is determined by measuring the time between the transmission of a radar signal to the receipt of the reflected signal (e.g., echo) from a target. The range resolution may be limited by the transmitted pulse width so that, for example, a narrow pulse width may be required to yield fine range resolution.
In order to produce a relatively fine azimuth resolution, a large antenna is needed to focus the transmitted and received energy into a sharp beam such that the sharpness of the beam defines the azimuth resolution. However, in many instances, a large antenna cannot be used due to the physical constraints of the radar platform. In these instances when a large antenna is required but cannot be used, a synthetic antenna (e.g., aperture) may be used in order to provide the needed fine azimuth (or cross range) resolution. In order to provide a synthetic aperture, a radar platform collects data while moving a certain known distance such that the distance moved is the dimension of the required antenna, thereby “synthesizing” the antenna (e.g., synthetic aperture). Thus, the term “synthetic aperture” is generally applied to the motion of the radar platform to effectively synthesize a relatively large array of transmitters and receivers (e.g., antennas).
Similar to the SAR, Inverse Synthetic Aperture Radars (ISAR) also produce a two-dimensional image (e.g., range and cross range, and intensity). However, the ISARs use the motion of the target to synthesize a large aperture antenna and not the motion of the radar platform itself. Thus, the term “inverse synthetic aperture” is generally applied to motion of the target which allows derivation of information concerning the shape and size of the target.
More specifically, ISARs are used to obtain information of a target at long range using the Doppler effect to produce an image by exploiting the relative motion of a target (typically pitching of a ship on an ocean surface) to develop an approximation of a visual profile of the target. In typical ISAR systems, the image produced is a two dimensional image (i.e., a 2-D Doppler versus range “picture”) of the target, with each image being 512 pixels by 128 Doppler cells. Each range/Doppler pair has six bits of amplitude (intensity) data for pixel display. The profile image is developed as shown in
FIG. 1
by assuming a center of rotation of the target and developing height versus range information based on the Doppler shift of the frequency of the returned signal.
While such systems have been quite successful in presenting images to trained personnel for interpretation, several major drawbacks have been encountered. Specifically, while rotational target motion need not be large for acceptable results to be obtained (i.e. about 1° of pitching motion is often sufficient), ISAR systems cannot produce a profile image if the target is stationary. Further, accuracy of the profile image development is also complicated and errors occasioned by other motions of the target such as roll and yaw. Therefore, the ISAR systems cannot be calibrated to provide any form of standardization of the profile images produced.
Moreover, automatic classification schemes require a computational “collapsing” of the profile image containing in excess of sixty thousand pixels, each represented by a six bit intensity value. As can be understood, the processing of the image is thus very computationally intensive and relatively slow in providing a response. For example, one proposed classification technique requires processing of several hundred images per classification with each of 512×128 pixels requiring 250 operations with 10 instructions per operation or roughly fifty billion computer operations per performed classification. It can be appreciated that classification can thus require several minutes of processing time on a relatively powerful high-speed data processor, and that processing times of much less than thirty seconds cannot be provided by processors currently practical for shipboard or airborne use.
Trained ISAR operators, using this technology, are able to identify surface combatants at large standoff ranges from the produced profile images. Unfortunately, this recognition skill is not trivial and a human operator must learn how to recognize distinct classes of ships in an ISAR image, requiring long training times. Typical training courses consist of an intensive course of several weeks duration to learn features of an ISAR image on which a classification may be based. Further, this recognition skill is easily lost because of the subtleties of differences of ISAR images and must be constantly reinforced and refreshed in order for the operator to retain such recognition skills. Some studies have suggested that the average operator only retains 20% of the ISAR recognition skill obtained during training after two weeks of training completion.
Consequently, automatic ISAR classification techniques have been proposed. These techniques process the ISAR image data, applying any number of pattern matching techniques against a library of known target types. These prior art approaches commonly apply image processing techniques (such as segmentation, noise rejection, major axis definition, edge detection, etc.) to enhance image recognition. However, pattern matching, whether of the entire image or particular features thereof, is particularly sensitive to the aspect of the target (i.e., the angle of the target relative to the direction of observation). Therefore, many images of each type of target to be classified or recognized must be first obtained, then maintained in an image library and processed against a given ISAR image. Thus, both data acquisition and database management present substantial difficulty in addition to data processing requirements.
State-of-the-art ISAR image classification requires processing data from hundreds of ISAR image frames. Typically, ISAR image classification requires several billion computations for several seconds for a single image while classification accuracy is known to be greatly improved by the processing of a plurality of images. Currently, there are no off-the-shelf mission computers with the processing capability to provide reasonably accurate classification in substantially real time. Consequently, available resources, in current off-the-shelf mission computers, fall far short of what is necessary to arrive at satisfactory recognition results. Thus, there is a need for reducing the number of computations performed in target classification on state of the art mission computers.
SUMMARY OF THE INVENTION
It is therefore an object of the present invention to reduce the number of computations necessary in target classification.
It is another object of the present invention to reduce the time necessary for target classification.
It is still another object of the present invention to increase the useable data to the classifier by reducing sensitivity to other target motions, for example, roll and yaw, and reducing sensitivity to the absence of target motion.
The present invention is a target recognition system and method wherein target amplitude data, known in the art as A-scan data, alone, is interrogated for target recognition. In the preferred embodiment method target recognition is on coherent A-Scan data, i.e., coherently integrated (over the ISAR aperture time) amplitude versus range data with aspect angle and other non-pitch target motions generally

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