SAR ATR tree line extended operating condition

Abstract

A synthetic aperture radar acquires an image of one or more objects and identifies them as targets. The objects are located in the proximity of clutter within the image such of trees, or a tree line. The radar acquires a SAR image having pixels descriptive of the clutter and the object(s). Regions having object pixels are identified within the synthetic aperture image using an object identification (algorithm), where the object identification (algorithm) utilizes one or more historically known target characteristics and one or more measured characteristic to obtain an output. Boundaries are identified for the one or more objects within the output using an object isolation, such as, for example, a Watershed transform. Clutter pixels are identified external to the one or more objects. The clutter pixels are suppressed from the synthetic aperture image thereby generating a clutter reduced image containing the one or more objects. The objects are compared with known images of a probable target until a match is found, the match representing the target identification.

1 . A side looking synthetic aperture radar for acquiring an image of one or more stationary objects and identifying said one or more stationary objects, said one or more stationary objects located in the proximity of tree clutter within said image, said side looking synthetic aperture radar comprising:
 analog to digital converter for converting a plurality of radar returns into a digital stream, said radar returns representing said tree clutter and said stationary objects;   a computer for:   converting said digital stream into a synthetic aperture image having clutter pixels descriptive of said tree clutter and object pixels descriptive of said one or more stationary objects;   identifying one or more regions having object pixels within said synthetic aperture image using a stationary object identification, said stationary object identification utilizing one or more historically known target characteristic and one or more measured characteristic to obtain an output;   identifying boundaries for said one or more stationary objects within said output using an object isolation;   identifying tree clutter pixels external to said one or more stationary objects;   suppressing said tree clutter pixels from said synthetic aperture image thereby generating a clutter reduced image containing said one or more stationary objects;   comparing each of said one or more stationary objects within said clutter reduced image with known images of a probable target until a match is found, said match representing said target identification.   
   
   
       2 . A radar as described in  claim 1  wherein said historically known target characteristic are bright pixels within said synthetic aperture image. 
   
   
       3 . A radar as described in  claim 2  wherein said measured target characteristic is a density image above a threshold extracted from said synthetic aperture image. 
   
   
       4 . A radar as described in  claim 3  wherein said output is said density image above a threshold. 
   
   
       5 . A radar as described in  claim 4  wherein said object isolation is performed using a Watershed transform operating on said output. 
   
   
       6 . A radar as described in  claim 5  wherein said one or more objects within said clutter reduced image are sorted in order of most likely to be a target using a criterion. 
   
   
       7 . A radar as described in  claim 6  wherein portions of said synthetic aperture image are masked to generate one or more masks and then sorted by maximum density of each of said masks. 
   
   
       8 . A method for using a side looking synthetic aperture radar for acquiring an image of one or more stationary objects and identifying said one or more stationary objects, said one or more stationary objects located in the proximity of tree clutter within said image, said method comprising the steps of:
 converting a plurality of radar returns into a digital stream, said radar returns representing said tree clutter and said stationary objects;   converting said digital stream into a synthetic aperture image having clutter pixels descriptive of said tree clutter and object pixels descriptive of said one or more stationary objects;   identifying one or more regions having object pixels within said synthetic aperture image using a stationary object identification, said stationary object identification utilizing one or more historically known target characteristic and one or more measured characteristic to obtain an output;   identifying boundaries for said one or more stationary objects within said output using an object isolation;   identifying tree clutter pixels external to said one or more stationary objects;   suppressing said tree clutter pixels from said synthetic aperture image thereby generating a clutter reduced image containing said one or more stationary objects;   comparing each of said one or more stationary objects within said clutter reduced image with known images of a probable target until a match is found, said match representing said target identification.   
   
   
       9 . A method as described in  claim 8  wherein said historically known target characteristic are bright pixels within said synthetic aperture image. 
   
   
       10 . A method as described in  claim 9  wherein said measured target characteristic is a density image above a threshold extracted from said synthetic aperture image. 
   
   
       11 . A method as described in  claim 10  wherein said out-put is said density image above a threshold. 
   
   
       12 . A method as described in  claim 11  wherein said object isolation is performed using a Watershed transform operating on said output. 
   
   
       13 . A method as described in  claim 12  wherein said one or more objects within said clutter reduced image are sorted in order of most likely to be a target using a criterion. 
   
   
       14 . A method as described in  claim 13  wherein portions of said synthetic aperture image are masked to generate one or more masks and then sorted by maximum density of each of said masks.