Abstract:
A method of reducing cross-range streaking in a radar image includes determining a number of on-pixels in each line of at least a portion of the radar image, determining which lines have a determined number of on-pixels that exceeds a threshold number, and removing the on-pixels of lines having the determined number of on-pixels exceeding the threshold number.

Description:
GOVERNMENT RIGHTS 
       [0001]    This invention was made with U.S. Government support under contract No. FA8650-08-D-1446 awarded by the Department of Defense. The U.S. Government has certain rights in this invention. 
     
    
     BACKGROUND 
       [0002]    The present invention relates to the field of automatic target recognition. 
         [0003]    A typical synthetic aperture radar (SAR) automatic target detection/automatic target recognition (ATD/ATR) system may include three stages: a pre-screener/detection stage; a discriminator stage; and a classifier stage. 
         [0004]    Stationary targets detected using rotating radar arrays may cause cross-range streaking in the images produced by the SAR, wherein unwanted bright detections/bright pixels/“on” pixels corresponding to the rotation of the radar array are produced in the radar image. Cross-range streaking on the target may cause a decrease in the accuracy of both a position estimate and a pose estimate of the target calculated in the second level discriminator (SLD) of the ATD process. For example, the length, height, and/or width estimates of the target may be corrupted by the cross-range streaking, thereby leading to poor/inaccurate ATD scores. 
         [0005]    Prior attempts to deal with SAR target chips/images with cross-range streaks caused by rotating radars include searching all possible pose values to obtain a best match with ATR models or templates. However, such methods may result in longer software run times and a higher rate of false target identification declarations. 
         [0006]    Other solutions have included the modeling of cross-range streaks into the ATR models/templates. This solution, however, also typically results in longer software run times, and further requires a larger database. 
       SUMMARY 
       [0007]    The front end of a SAR ATR system will often estimate the pose and position of a potential target, such as a target vehicle. The front end, or ATD stage, passes the target pose and position information to the ATR algorithm, which then uses the information to constrain the pose and position search range during target identification. However, cross-range streaking, such as that caused by rotating radars on a vehicle, can result in incorrect ATD pose and position estimates, which may lead to the ATR producing an incorrect target identification declaration. A SAR chip level cross-range streak detector according to embodiments of the present invention aids the ATD in the process of estimating the pose and position of targets, such as target vehicles. 
         [0008]    Embodiments of the present invention detect cross-range streaks in a SAR chip and, if cross-range streaks are found, calculate an improved pose estimation by performing one or more of the following processes: 
         [0009]    1) define regions on the SAR chip where cross-range streaking will be checked; 
         [0010]    2) threshold a magnitude/power chip to form a bi-level output chip (e.g., a binary cluster image comprising “on” pixels and “off” pixels corresponding to the SAR chip), and use these detected pixels to create target pixel clusters (this process can be combined with the ATD SLD thresholding and clustering steps, discussed below); 
         [0011]    3) form a cross-range profile/histogram of cross-range bright pixels counts (e.g., the number of “on” pixels per chip line/row, which is done by a simple count of the number of pixels set “on” within each line/row/section of a corresponding target pixel cluster); 
         [0012]    4) filter the cross-range profile (e.g., using a low pass filter); 
         [0013]    5) threshold the cross-range profile, whereby rows having a cross-range bright pixels count exceeding the threshold are categorized as potential streaks/problem areas (this threshold may be determined empirically); 
         [0014]    6) test for minimum and/or maximum range-wise thicknesses of any potential cross-range detected streaks; and 
         [0015]    7) if the test from step 6 is satisfied, exclude the identified pixels corresponding to the cross-range detected streaks from bounding box calculations. The identified pixels may also be excluded from the SLD feature computations, while the unmodified magnitude/power chip may be still passed to the ATR. 
         [0016]    One embodiment of the present invention provides a method of reducing cross-range streaking in a radar image, the method including determining a number of on-pixels in each line of at least a portion of the radar image, determining which lines have a determined number of on-pixels that exceeds a threshold number, and removing the on-pixels of lines having the determined number of on-pixels exceeding the threshold number. 
         [0017]    Determining the number of on-pixels in each line of at least a portion of the radar image may include forming a target profile histogram and filtering the target profile histogram. 
         [0018]    Filtering the target profile histogram may include passing the target profile histogram through a low pass filter. 
         [0019]    Determining which lines have the determined number of on-pixels exceeding the threshold number may include determining a largest gradient of the target profile histogram and determining a thickness of the largest gradient of the target profile histogram. 
         [0020]    Removing the on-pixels of lines having the determined number of on-pixels exceeding the threshold number may include removing the on-pixels corresponding to the largest gradient when the thickness meets a threshold streak thickness. 
         [0021]    Determining the largest gradient of the target profile histogram may include incrementally varying a pose angle through a range of 90 degrees and determining a maximum gradient of the target profile histogram on a projection axis and on an axis perpendicular to the projection axis at each of increments corresponding to the range of 90 degrees. 
         [0022]    The lines of the at least a portion of the radar image may correspond to horizontal rows of the at least a portion of the radar image. 
         [0023]    The method may further include forming a modified binary cluster after removing the on-pixels of lines having the determined number of on-pixels exceeding the threshold number, determining a best fit of a rotating bounding box around a leading edge of the modified binary cluster, and determining a pose estimate and determining a position estimate corresponding to the determined best fit. 
         [0024]    Determining the pose estimate may include computing length and width features of the binary cluster, wherein the computed length may correspond to a direction corresponding to a peak range of histogram bins. 
         [0025]    Removing the on-pixels of lines having the determined number of on-pixels exceeding the threshold number may include replacing the on-pixels with pixel values determined from other regions of the radar image. 
         [0026]    The method may further include receiving an unmodified radar image, converting pixel data of the unmodified radar image into a binary cluster, forming target pixel clusters corresponding to the binary cluster, and analyzing the target pixel clusters as the at least a portion of the radar image. 
         [0027]    The method may further include forming a modified image corresponding to the removed on-pixels, computing one or more attributes of a potential target corresponding to the modified image, and categorizing the potential target corresponding to the computed attributes using automated target recognition. 
         [0028]    Another embodiment of the present invention provides a method of improving radar image analysis, the method including defining regions on a synthetic aperture radar (SAR) chip to be analyzed for cross-range streaking, thresholding a magnitude chip corresponding to the defined regions, forming a bi-level output chip corresponding to the thresholded magnitude chip and including on-pixels and off-pixels, forming target pixel clusters including one or more of the on-pixels and corresponding to a potential target, determining a number of the on-pixels in each of a plurality of sections of the target pixel clusters, forming a cross-range profile corresponding to the determined number of on-pixels of each section, filtering the cross-range profile, thresholding the cross-range profile by categorizing sections having the determined number of on-pixels above a threshold value as potential problem areas, testing the potential problem areas for at least one of minimum and maximum parameters, and excluding pixels corresponding to the potential problem areas determined to be beyond at least one of the minimum or maximum parameters. 
         [0029]    Yet another embodiment of the present invention provides a method of automated target detection, the method including downsampling radar data, converting the downsampled radar data to a square-root-of-magnitude format, identifying potential target pixels corresponding to local brightness, clustering the potential target pixels to form regions of interest, thresholding chip pixels corresponding to the regions of interest to determine which chip pixels correspond to a target, clustering the thresholded chip pixels to create a binary cluster, circumscribing the binary cluster, computing length and width features of the binary cluster, generating a joint feature discriminator score corresponding to the computed length and width features, and categorizing a target detection corresponding to the joint feature discriminator score and at least one of an automatic target recognition model and an automatic target recognition template. 
         [0030]    The potential target pixels may be identified using a classical Goldstein two-parameter constant false alarm rate method. 
         [0031]    Circumscribing the binary cluster may include determining a best fit of a bounding box around a leading edge of the binary cluster, and wherein the length and width features are computed corresponding to the determined best fit. 
         [0032]    Determining the best fit of a rotating bounding box around the leading edge of the binary cluster may include determining a number of pixels at each integer coordinate value on a projection axis of a target profile histogram, determining a largest gradient of the target profile histogram corresponding to the determined number of pixels, and removing pixels corresponding to the largest gradient if the largest gradient meets a threshold thickness value. 
         [0033]    The joint feature discriminator score may be used to determine whether the regions of interest belong to a target population or clutter population. 
         [0034]    Accordingly, embodiments of the present invention may provide: simple, real-time means of determining target cross-range streaks on a chip-by-chip/image-by-image basis in SAR images; estimations of pose and position of a target corresponding to an image with cross-range streak pixels removed; SAR chip data that needs no modification before being passed on to the ATR algorithm; ATR pose and position search regions that don&#39;t need to be increased; and an ATR model/template database that does not need to be increased. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0035]    The accompanying drawings, together with the specification, illustrate exemplary embodiments of the present invention, and, together with the description, serve to explain aspects of embodiments of the present invention. The above and other features and aspects of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings, in which: 
           [0036]      FIG. 1  is a block diagram of a three-stage SAR ATD/ATR system according to an embodiment of the present invention; 
           [0037]      FIG. 2  is a block diagram of an overall ATD/ATR process according to an embodiment of the present invention; 
           [0038]      FIG. 3  is a block diagram of a constant false alarm rate (CFAR) stage according to the embodiment shown in  FIG. 2 ; 
           [0039]      FIG. 4  is a block diagram of a SLD stage according to the embodiment shown in  FIG. 2 ; 
           [0040]      FIG. 5  is an example of a cluster threshold chip/binary cluster image (left) and a bounding box for the cluster threshold chip (right) used to estimate pose and position according to an embodiment of the present invention; 
           [0041]      FIG. 6  is an example of a radar image (left) that is thresholded and clustered to produce a binary cluster image (middle) used to derive a bounding box (right) for a chip with cross-range streaking according to an embodiment of the present invention; and 
           [0042]      FIG. 7  is a block diagram depicting a method of a chip cross-range streak detector according to an embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0043]    Referring to  FIG. 2 , ATR systems  100  may require that the ATD  40  meets a defined minimum probability of correctly estimating a target pose  150  (see  FIG. 5 ) within a predefined angle range. In the presence of cross-range streaking  25  on the target (see  FIG. 6 ), the pose estimate  150  calculated in the SLD stage  80  can have poor accuracy. Additionally, the SLD  80  feature estimates  85 , such as length and width estimates, can also be corrupted by the cross-range streaking  25 . An example of the effects of cross-range streaking  25  on the SLD  80  is shown in  FIG. 6 , wherein cross-range streaking  25  has resulted in a bounding box  22  with a poor/inaccurate pose angle  150 , thereby also causing poor target length and width estimations  85 .  FIG. 6  shows cross-range streaking  25  caused by a bright return point (e.g., caused by a corner reflector) on the target, as opposed to cross-range streaking  25  due to a mounted rotating radar, although embodiments of the present invention are not limited thereto. 
         [0044]    Embodiments of the present invention provide a SAR ATR  100  capable of reducing or eliminating cross-range streaks  25 , such as those caused by rotating radar, thereby decreasing the probability of false target identification declarations. 
         [0045]    Referring to  FIG. 1 , the pre-screener/detection stage  10  of a SAR ATR  100  according to an embodiment of the present invention selects candidate target pixels in a subject radar image/SAR chip/SAR map/SAR image pixel data  20  based on local brightness (e.g., the number/intensity of “on” pixels). This functionality may be performed in a constant false alarm rate (CFAR) stage  30  (see  FIGS. 2 and 3 ) of the ATD process  40 , according to embodiments of the present invention. Bright detections (e.g., “on” pixels) corresponding to the SAR map  20  in target-size regions are clustered/grouped and passed on for analysis as regions of interest (ROIs)  50 . 
         [0046]    The discriminator stage  60  analyzes the ROIs/ROI chips  50 , and attempts to reject clutter false alarms while accepting real/accurately represented targets. This process in the discriminator stage  60  reduces a computational load of the classifier stage  70  in an ATR system  100 . This functionality of selectively accepting targets is performed in the SLD stage  80  and the joint feature discriminator (JFD) stage  90  of the ATD process  40 , wherein ROI  50  features are calculated and used to produce a JFD/joint likelihood score  110 . 
         [0047]    Finally, the classifier stage  70  rejects clutter false alarms, and also classifies/categorizes target detections  75 , such as by vehicle type. The classifier functionality is performed in the ATR stage  140  of the ATD/ATR process  100 , which may use model-based or template-based matching. 
         [0048]    Referring to  FIGS. 2 and 3 , the CFAR detector stage  31  of the CFAR stage  30  may screen out much of the information corresponding to the radar image  20 , so that only data corresponding to possible targets/regions of interest  50  are passed on for further analysis. 
         [0049]    First, according to one embodiment of the present invention, downsampling  32  of the SAR image pixel data  20  is performed, which reduces the amount of processing that is required in the following stages. Next, the SAR image pixel data  20  is converted/transformed from its input format to square-root-of-magnitude format  33 . The mapping in this stage  33  causes the target/clutter to have a Gaussian-like probability distribution. Potential target pixels of the SAR image pixel data  20  are then identified in the CFAR detector stage  31 , for example, by using a classical Goldstein two-parameter CFAR method. The final stage  34  of the CFAR stage  30  is the clustering of pixels. A single target can produce multiple CFAR detections, so detections in common target size regions are grouped together. 
         [0050]    Referring to  FIG. 4 , the SLD  80  extracts several features (e.g., “cluster features”  85 ) from the ROI chips  50  determined by the CFAR detector stage  31 . According to embodiments of the present invention, a chip pixel thresholding  81  is performed using the mean and standard deviation of the pixel data grayscale chip corresponding to ROI centroid locations  38 . A threshold is computed  81  to determine which pixels in the chip  38  belong to the target. Two separate rounds of clustering and small cluster size removal are performed in the “Cluster Threshold Pixels” stage  82  of the SLD stage  80 . Bi-level chip/binary segmentation  21  (see  FIGS. 5-7 ) is formed through thresholding  81  and clustering  82 . 
         [0051]    In the “Circumscribe Cluster” stage  83 , the SLD  80  circumscribes and finds the best fit of a rotating rectangle/bounding box  22  around the leading edge of the binary cluster  21 . The binary cluster  21  is defined by, for example, the (X, Y) coordinate list of “on” pixels (shown in white in the black and white binary clusters  21  shown in  FIGS. 5-7 ) as determined by the “Threshold Chip Pixels” stage  81  and the “Cluster Threshold Pixel” stage  82 . The SAR map  20  direction/orientation determines the leading edge of the target. Referring to  FIG. 5 , the pose/orientation  150  of the rectangle  22 , estimated based upon a bounding box  22  calculation with 180 degree ambiguity, is determined by a steepest/largest/maximum gradient  24  of the projected target profile histogram  23  (see  FIG. 7 ) of the binary silhouette  21  in near-range. 
         [0052]    In determining a bounding box  22 , which is used to estimate target pose  150  and location/position  155 , the best fit of a rotating bounding box  22  around the target binary cluster  21  is determined using the steepest gradient  24  in the projected target profile histogram  23 . This is done by projecting the target pixels of the binary cluster  21  onto a projection axis, which is at a rotation angle relative to the SAR map  20  X-Y coordinate system. This target profile histogram  23  consists of the number of “on” pixels that are at each integer coordinate value on the projection axis (e.g., the number of “on” pixels in each row of the binary cluster  21 ). In one embodiment of the present invention, the rotation (pose) angle  150  is varied through a range of 90 degrees, in 1 degree increments. At each value of the rotation (pose) angle  150 , the steepest gradient  24  of the target profile histogram  23  is calculated for both the projection axis and an axis perpendicular to it (thus eliminating the need to vary the rotation angle through a range of 180 degrees while calculating the gradient for only a single axis). The length of the target vehicle may be distinguished from the width by determining a direction corresponding to a greater range of histogram bins. The pose (orientation) angle  150  of the target may be measured, for example, in a counterclockwise manner from the Y-axis. Once the pose angle  150  that provides the steepest gradient  24  is estimated, a bounding box  22  aligned to the calculated pose angle  150  around the target pixels of the binary cluster  21  is determined. Sample bounding boxes  22  derived from binary clustered pixel data  21  are shown in  FIGS. 5 ,  6 , and  7 . The target pose estimate  150  is passed on to the ATR algorithm of the ATR stage  140 , as shown in  FIG. 2 . 
         [0053]    Referring to  FIG. 4 , the SLD stage  80  computes a set of cluster features  85  (such as length and width) in the “Feature Computation” stage  84  corresponding to the information provided by the “Circumscribe Cluster” stage  83 , and these features  85  are used in the JFD stage  90  for generating a joint likelihood score  110  for each target. This joint likelihood score  110  then has a threshold applied (operating point)  120  to indicate whether or not the ROI  50  being tested belongs to a target population or clutter population (e.g., as determined in “Target/Clutter Discrimination” stage  130 , as shown in  FIG. 2 ). Target ROI  50  location estimates  155  and pose estimates  150  may be saved for processing by the ATR stage  140 . 
         [0054]    A “chip cross-range streak detector” method according to an embodiment of the present invention is depicted in  FIG. 7 . The algorithm process steps for the method include: (1) define regions on the SAR chip  20  where cross-range streaking  25  will be checked; (2) threshold the magnitude chip/power chip  26  to form a bi-level output chip/binary cluster  21  by using detected pixels to create target pixel clusters  21  (in embodiments of the present invention, this process may be combined with the thresholding and clustering stages  81  and  82  of the ATD SLD stage  80 ); (3) form a cross-range profile/target profile histogram  23  of the cross-range bright pixels counts by counting of the number of pixels set “on” within each row (for example, the number of “on” pixels may correspond to the X-axis of the histogram  23 , and each chip row may correspond to the Y-axis of the histogram  23 ); (4) low pass filter (LPF)  28  the cross-range profile  23 ; (5) identify chip rows (e.g., at each integer coordinate value on the projection axis) that have cross-range bright pixel counts exceeding a set threshold  120  as potential streaks  25  (the threshold  120  may be determined empirically, and an example threshold value may be 0.4); (6) test for minimum and maximum range-wise thickness  27  of any cross-range detected streaks  25 ; (7) if the test from step 6 is satisfied, the identified pixels corresponding to the cross-range detected streaks  25  are excluded from the bounding box  22  calculations, and may also be excluded from the SLD “Feature Computation” stage  84 ; and (8) calculate the bounding box  22  for pose estimation. According to embodiments of the present invention, the unmodified magnitude/power chip  26  may also be passed to the ATR  140 . 
         [0055]    After the completion of the above steps, according to embodiments of the present application, the following actions may be taken: the target chip  26  and improved pose estimation  150  may be passed on to the ATR  140  without modification to the target chip  26  (as shown in  FIG. 2 ); the target chip  26  may have the streak  25  identified pixels values replaced with values determined from other regions of the chip  26 , and the modified target chip  29  and improved pose estimation  150  may then be passed on to the ATR  140 ; and/or the unmodified target chip  26  and improved pose estimation  150  may be passed on to the ATR  140  along with information on the amount of “streak corrupted” pixels  25  that the chip  26  contains, which may be used by the ATR stage  140  to ignore a percentage of the target chip pixels in the matching calculations. 
         [0056]    The “chip cross-range streak detector” method of embodiments of the present invention improves the ATR identification (e.g., vehicle identification) of targets that have rotating radar antennas. Experimental data indicates that PID percentage improvement (not difference) of an ATR system  100  using the “chip cross-range streak detector” algorithm of an embodiment of the present invention over the baseline improvement relative to performance without the algorithm applied is 0% for targets without cross-range streaking, but 32% for targets with cross-range streaking. 
         [0057]    While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those of ordinary skill in the art that features of different embodiments may be combined to form further embodiments, and that various changes in form and details may be made therein, without departing from the spirit and scope of the present invention as defined by the following claims and their equivalents.