Target shadow detector for synthetic aperture radar

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

Reexamination Certificate

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C342S013000, C342S014000, C342S016000, C342S02500R, C342S027000, C342S028000, C342S090000, C342S175000, C342S195000

Reexamination Certificate

active

06756934

ABSTRACT:

BACKGROUND OF THE INVENTION
1. Field of Invention
The present invention relates generally to radar target detection and identification using Synthetic Aperture Radar (SAR) images. More specifically, the present invention relates to an improved method and apparatus for detecting target shadows to improve the performance of target identification.
2. Description of the Related Art
Radar target detection and identification have been proven to be very effective in military surveillance, reconnaissance, and combat missions. The detection and identification of targets provide real-time assessment of the number and the locations of the targets of interest. One method of target detection and identification is to process the image acquired by the radar using the Synthetic Aperture Radar (SAR) technology. By processing the SAR image, the features of a target can be extracted and matched to a database for identification. One distinct feature of a target is the shadow cast by the target. When the shadow is present, more target features can be used to increase the probability of correct identification.
Synthetic Aperture Radar (SAR) is used for ground mapping as well as target identification. The general principle behind SAR is to coherently combine the amplitude and phase information of radar returns from a plurality of sequentially transmitted pulses using a relatively small antenna on a moving platform. Synthetic Aperture Radar (SAR) systems have been developed to acquire images of stationary objects by coherently integrating phase history from multiple pulse returns. High resolution maps are achieved by coherently combining return signals reflected from transmitted pulses in the cross range direction. Formation of focused SAR images or maps requires accurate information on platform position and velocity to coherently combine pulse returns from multiple pulses. The process of aligning pulses for coherent combination is referred to as motion compensation, and is usually performed with the raw radar data, at the early stage of image formation process. The plurality of returns generated by the transmitted pulses along a known path of the platform make up an array length. During the array length, amplitude as well as phase information returned from each of the pulses, for each of many range bins, is preserved, forming a SAR image. The SAR image is formed from the coherent combination of the amplitude and phase of return(s) within each range bin, motion compensated for spatial displacement of the moving platform during the acquisition of the returns for the duration of the array length. Because of the high precision required for this process, sometimes background noise and ground scatter can make certain range bins or pixels within the image shift value by large amounts, creating an effect similar to snow on a television image.
For a relatively tall radar target on the ground, certain range bins will be obscured because the target's height will create a shadow from the radar energy emitted by the transmitter. That is, no radar energy will be returned from those range bins in the shadow of the tall target. This shadow, present in certain range bins, indicates features of the tall target casting the shadow. Features of the shadow, and its relationship to the target itself, can be used to further identify the target casting the shadow. Unfortunately, ground features such as roads also reflect little radar energy, and thus look like shadows. What is a target shadow and what is only a low radar energy reflectance ground feature unrelated to a target may not be clearly discernible.
Interpreting shadows in SAR imagery has been described in
Knowledge
-
based Segmentation of SAR Data with Learned Priors
, IEEE transactions on Image Processing, vol 9, no 2, pp 299-301, February 2000, which uses Bayes' rule to segment the target, the shadow and background terrain pixels, given prior knowledge of the intensity distribution of each class and the prior probability that a particular pixel belongs to a certain class. Another interpretation of shadows in SAR imagery is described in
Reconstruction of Man Made Objects from High Resolution Radar Images
, in Proceedings of IEEE Aerospace Conference, vol 3, March 2000, pp 287-292. Here, a threshold operation is used in conjunction with morphological steps to perform segmentation of the shadow region.
Similar efforts at working with radar shadows are described in U.S. Pat. No. 6,259,396, filed Aug. 26, 1999, titled
Target acquisition system and Randon transform based method for target azimuth aspect estimation
incorporated herein by reference in its entirety.
Prior art efforts have sometimes failed at reliably detecting the shadow of a target and associating the shadow with the target itself. Because of this, false alarm rates are relatively high, failing to correctly identify target types.
SUMMARY OF THE INVENTION
Above limitations are solved by the present invention by method and apparatus for using a Synthetic Aperture Radar (SAR) system for authenticating that a suspected target shadow is cast by a target. The method comprises the steps of:
a) generating a radar image using radar returns from said SAR, said radar image containing both the target and its suspected target shadow;
b) forming a pentagonal perimeter adjacent to the target (within the radar image), the pentagonal perimeter chosen to contain the suspected target shadow. The pentagonal perimeter separates the target from its suspected target shadow; and
c) testing the suspected target shadow within said pentagonal perimeter to authenticate that said suspected target shadow is cast by said target.
One aspect of the testing performed on the suspected target shadow uses a 2 by 2 dilation and majority filter. Other tests performed are an adjacent overlap test, an edge pixel count test, a maximum area and minimum distance test as well as an area threshold test.
The original radar data is converted to a magnitude only form from its I and Q components and a magnitude thresholding is applied to the radar image to obtain a binary image therefrom, said binary image facilitating shadow region identification.


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S.W. Leung et al., “A Shadow Feature Signal Processing Algorithm for Radar Systems”; Transactions of the China 1991 International Conference on Circuits and Systems (Shenzhen, China); pp. 452-455.*
S.W. Leung et al., “A Fuzzy Shadow Feature Scheme for Radar Signal Detection”; Proceedings of the International Conference on Information, Communications, and Signal Processing ICICS '97 (Singapore, 1997); pp. 1386-1388.*
V.V. Chapurskiy et al., “SISAR: Shadow Inverse Synthetic Aperture Radiolocation”; Proceedings of the IEEE International Radar Conference (the year 2000); pp. 322-328.

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