Abstract:
In one example, a method to reduce scattering centers (SC) includes receiving a set of SC data points associated with an object in three-dimensional space, partitioning the SC data points into a plurality of volumes, aggregating the SC data points within each volume based on an aggregate threshold and combining the aggregated SC data points associated with each volume to form a reduced set of SC data points. The method also includes comparing the reduced set of SC data points with the received set of SC data points to determine if the reduced set of SC data points meets a set of comparison metrics and if the reduced set of SC data points meets the set of comparison metrics, increasing the size of the volumes and performing another iteration of reducing the SC data points by volume.

Description:
GOVERNMENT SPONSORED RESEARCH 
   This invention was made with Government under Contract HQ0006-03-C-0047 awarded by the Missile Defense Agency. The United States Government has certain rights in the invention. 

   BACKGROUND 
   A radar system emits radio waves that are reflected by an object (also referred to as a target) in a form of a reflect signal that is detected by the radar system. In general, the reflected signal includes a component associated with a direct reflection from the object (sometimes called a single bounce (SB)) and a component from indirect reflections from the object (e.g., reflections off of other objects in space such as ground, buildings and so forth) (sometimes called a multiple bounce (MB)). Based on the intensity and angle of the reflected signal, the location of the object may be determined. 
   In training scenarios, instead of using actual objects, it is more practical and cost effective to use simulated radar objects. The simulated radar objects may be generated using radar signature modeling tools that emulate the radar object. For example, radar signature modeling tools are used to generate radar signature models to emulate a variety of objects that include, for example, ballistic missiles, airplanes, other 3-Dimensional (3-D) objects and so forth. One such radar signature modeling tool is XPATCH®. 
   SUMMARY 
   In one example, a method to reduce scattering centers (SC) includes receiving a set of SC data points associated with an object in three-dimensional space, partitioning the SC data points into a plurality of volumes, aggregating the SC data points within each volume based on an aggregate threshold and combining the aggregated SC data points associated with each volume to form a reduced set of SC data points. The method also includes comparing the reduced set of SC data points with the received set of SC data points to determine if the reduced set of SC data points meets a set of comparison metrics and if the reduced set of SC data points meets the set of comparison metrics, increasing the size of the volumes and performing another iteration of reducing the SC data points by volume. 
   In another aspect, an article includes a machine-readable medium that stores executable instructions to reduce scattering centers (SC). The instructions cause a machine to receive a set of SC data points associated with an object in three-dimensional space, partition the SC data points into a plurality of volumes, aggregate the SC data points within each volume based on an aggregate threshold and combine the aggregated SC data points associated with each volume to form a reduced set of SC data points. The instructions also cause a machine to compare the reduced set of SC data points with the received set of SC data points to determine if the reduced set of SC data points meets a set of comparison metrics and if the reduced set of SC data points meets the set of comparison metrics, increase the size of the volumes and performing another iteration of reducing the SC data points by volume. 
   In a further aspect, an apparatus includes circuitry to receive a set of SC data points associated with an object in three-dimensional space, partition the SC data points into a plurality of volumes, aggregate the SC data points within each volume based on an aggregate threshold and combine the aggregated SC data points associated with each volume to form a reduced set of SC data points. The apparatus further includes circuitry to compare the reduced set of SC data points with the received set of SC data points to determine if the reduced set of SC data points meets a set of comparison metrics and if the reduced set of SC data points meets the set of comparison metrics, increase the size of the volumes and performing another iteration of reducing the SC data points by volume. 
   In a still further aspect, a method to reduce scattering centers (SC) includes receiving from a radar signature modeling tool a set of SC data points associated with an object in three-dimensional space, partitioning the SC data points into a plurality of volumes, aggregating the SC data points within each volume based on an aggregate threshold and combining the aggregated SC data points associated with each volume to form a reduced set of SC data points. The method also includes comparing the reduced set of SC data points with the received set of SC data points to determine if the reduced set of data points meets a set of comparison metrics based on a set of comparison metrics including a similarity metric, a maximum amplitude metric, a length metric and a relative maximum amplitude metric. The method further includes if the reduced set of SC data points meets the set of comparison metrics, increasing the size of the volumes and performing another iteration of reducing the SC data points by volume. 

   
     DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a diagram of a radar simulation system. 
       FIG. 2  is a graph depicting features used in comparing original scattering center (SC) data with reduced SC data. 
       FIGS. 3A and 3B  are flow charts of a process to reduce SC data. 
       FIG. 4  is a graph depicting ranges. 
       FIGS. 5A and 5B  are graphs depicting the original SC Data and the reduced SC data. 
       FIG. 6  is a block diagram of an example of a computer on which the processes of  FIGS. 3A and 3B  may be implemented. 
   

   DETAILED DESCRIPTION 
   Prior attempts to reduce scattering centers (SC) for objects generated from 3D Inverse Synthetic Aperture Radar (ISAR) imageries were based on the magnitude of the SC data points for an entire object. For example, the SC data points having the stronger intensity (e.g., having the stronger radar cross section (RCS) value) were kept while the weaker intensity SC data points were discarded. As a consequence, often key features such as delay returns and/or low amplitude base returns were excluded from the SC data and thus the complexity of the object was lost. For example, after performing these prior art techniques, a missile did not appear like a missile after a SC data reduction. Moreover, current and near-future real-time radar return injection systems used in simulation do not have the processing capability to support a large number of SC data points generated by the radar signature modeling tools (e.g., XPATCH®). For example, to emulate an object, the radar signature modeling tools generated thousands of SC data points while the radar return injection systems can only effectively process a few hundred SC data points per object. Described herein are techniques to reduce the number of SC data points for an object provided by the radar signature modeling tools by roughly an order of magnitude smaller for use in current simulation applications while retaining the radar signature complexity of the object. 
   Referring to  FIG. 1 , a simulator system  10  includes a data generation system  12  such as a radar signature modeling tool, for example, a data reduction processing system  16  and a simulator  22 . The data generation system  12  provides an original SC data set that includes a radar object that is being emulated. The data reduction processing system  16  reduces the original SC data set provided by the data generation system  12  and provides a reduced SC data set to the simulator  22  for radar simulations. In one example, the data reduction processing system  16  provides SC data to the simulator  12  that is a non-tactical simulator which does not perform real-time processing of the SC data. In another example, the data reduction processing system  16  further reduces the SC data to provide SC data to the simulator  12  that is a tactical simulator which does perform real-time processing of the data. 
   The data reduction processing system  16  partitions the SC data into multiple volumes associated with a three-dimension space. For example, the 3-D space is represented by cubes. Within each volume, the data reduction processing system  16  aggregates the SC data from hundreds of SC data points, for example, down to an aggregate threshold. 
   One of ordinary skill in the art recognizes that there may be a number of different aggregation algorithms that may used to aggregate the SC data points in a volume. In one example, an aggregated SC data point (or points) may be determined through coherent integration of the SC data points in a volume. The coherent integration includes identifying a peak location (e.g., a location in the volume having the highest RCS value) among the SC data points in the volume. The coherent integration also includes converting a value of each SC data point in a spatial domain to a value in a frequency domain, adding all values for all frequencies and converting the values into spatial domain, and determining a peak value (e.g., the highest RCS value). Thus, an aggregated SC data point will be a complex value (from the peak value) located at the peak location. 
   For each volume, the data reduction processing system  16  compares the aggregated SC points to the original SC data points for the respective volume using a set of metrics to ensure that the aggregated data is representative of the complexity for that volume. For example, if the volume does not meet the criteria for that volume, then the aggregate threshold is increased again for that volume. The aggregate threshold is increased iteratively until the volume may be represented by SC data points that meet the criteria. 
   By aggregating the SC data of the object into multiple volumes, the process ensures that the SC data from all areas in space will be represented after the aggregation and not just those areas in space having SC data with relatively higher intensity values (e.g. higher RCS values) than those of other areas. 
   After the aggregated SC data for each volume has been determined, the data reduction processing system  16  sums the aggregated SC data to form a set of reduced SC data points. The reduced SC data set is compared to the original SC data set received. 
   Since a goal of reducing the number of SC data points is to maintain the complexity of the original SC data in the reduced SC data, the reduced SC data is compared against the original SC data at every reduction using another set of comparison metrics. If the differences between the original SC data and the reduced SC data are not outside pre-defined bounds defined by a set of comparison metrics, then the reduction process is executed again with size of the volume increases proportionately. The aggregation and reduction cycle is repeated until a reduced SC data set that meets the comparison metrics is generated. 
   Referring to  FIG. 2 , in one example, the comparison metrics evaluate features of a radar return range profile from the reduced SC data compared to a radar return range profile from the original SC data. In particular,  FIG. 2  illustrates representative wide-band (WB) range profiles (RCS values vs. relative range) for an original SC data set  30  and for a reduced SC data set  40 . 
   In one example, the comparison metrics include a similarity metric, a length metric, a maximum amplitude metric and a relative maximum amplitude metric. The original SC data is compared to the reduced SC data for all SC data above a threshold intensity value to avoid corruption by secondary data. 
   The similarity metric is used to determine an acceptable similarity between the original SC data set and the reduced SC data set. The similarity is equal to 
             1   -              ∑       A   ref     ⁡     (     &gt;   THD     )         -     ∑     A   ⁡     (     &gt;   THD     )                    ∑       A   ref     ⁡     (     &gt;   THD     )         +     ∑     A   ⁡     (     &gt;   THD     )               ,         
where A is the area (e.g., in meters squared) of the wide band profile of the reduced SC data, A ref  is the area (e.g., in meters squared) of the wide band profile of the original SC data and THD is the threshold intensity value used to avoid corruption by secondary data. Similarity values range from 0 to 1 where 1 is the most similar. In one example, a similarity metric indicates that a similarity value above 0.6 is acceptable for object complexity.
 
   The length metric is used to determine an acceptable length of the object in the reduced SC data. For example, in  FIG. 2 , a length is the length of the object that corresponds to the length of the pulse along the relative range axis. A length  32  corresponds to a length in the original SC data set and a length  42  corresponds to a length in the reduced SC data set. In one example, a length metric indicates that if the length  42  of the object in the profile for the reduced SC data set is within 80% of the length  32  of the object in the profile for the original SC data, then the length  42  is acceptable for object complexity. In another example, the length metric indicates that the difference in length between the length  42  and the length  32  is no greater than a predetermined length is acceptable for object complexity (e.g., if the object is a missile, if the difference between the length  42  and the length  32  is 0.5 meters, for example). 
   The maximum amplitude metric is used to determine an acceptable maximum amplitude (maximum peak) in the reduced SC data. The maximum amplitude is the highest magnitude intensity of the WB pulse. For example, a maximum amplitude  34  is the highest magnitude of intensity (e.g., RCS value) for the original SC data set and a maximum amplitude  44  is the highest magnitude of intensity (e.g., RCS value) for the reduced SC data set. In one example, a maximum amplitude metric indicates that if the maximum amplitude  44  of the object in the profile for the reduced SC data set is within 2 dB of the maximum amplitude  34  of the object in the profile for the original SC data, then the maximum amplitude  34  is acceptable for object complexity. 
   The relative maximum amplitude metric is used to determine an acceptable relative maximum amplitude (relative maximum peak) in the reduced SC data. For example, in  FIG. 2 , an example of a relative maximum amplitude  34  is the highest magnitude of intensity (RCS) for the original SC data set and a maximum amplitude  44  is the highest magnitude of intensity (RCS) for the reduced SC data set. In one example, a relative maximum amplitude metric indicates that if the relative maximum amplitude  46  of the object in the profile for the reduced SC data set is within 2 dB of the maximum amplitude  36  of the object in the profile for the original SC data, then the relative maximum amplitude  34  is acceptable for object complexity. 
   In one example, the relative maximum amplitude metric comparison is performed at a first peak  52 . In another example, the relative maximum amplitude metric comparison is performed at a last peak  54 . In other examples, more than one relative maximum amplitude metric comparison may be performed for multiple relative maximum amplitudes (e.g., using any combination of the first peak  52 , the last peak  54 , and intermediary peaks (e.g. a peak  62  and a peak  64 )). 
   While  FIG. 2  is directed to determining the complexity of the reduced SC data with the original SC data, the same techniques may be used to determine if a volume conforms to another set of metrics. For example, for each volume, a set of metrics may include a similarity metric, a length metric and a maximum amplitude metric. 
   Referring to  FIG. 3A , in one example, a process to reduce SC data for uses by simulator  22  that is a non-tactical simulator is a process  100 . The data reduction processing system  16  receives SC data associated with an object from the data generation system  12  ( 102 ). The data reduction processing system  16  partitions the SC data into multiple volumes ( 108 ). For example, the SC data for the object is partitioned into cube volumes. For each volume, the data reduction processing system  16  aggregated the SC data ( 112 ). For example, the hundreds of SC data point in a volume may be reduced to 0, 1, 2 or 3 SC points for each column. For each volume, the data reduction processing system  16  determines whether the aggregation conform to the comparison metrics ( 122 ). The data reduction processing system  16  combines the reduced SC data from each volume ( 122 ). The data reduction processing system  16  determines if the reduced data meets the criteria ( 132 ). For example, the reduced SC data is compared with the original SC data to determine if the reduced SC data meets a set of comparison metrics as described in  FIG. 2 . If the reduced data meets the criteria, the data reduction processing system  16  increases the volume size ( 134 ) and starts another aggregation reduction cycle ( 112 ). If the reduced SC data does not meet the criteria, the data reduction processing system  16  uses the reduced SC data prior to the last reduction ( 136 ). 
   Referring to  FIG. 3B , in one example, a process to further reduce SC data for use by the simulator  22  that is a tactical simulator is a process  150 . The process  150  includes the processing blocks of process  100 , for example, processing blocks  102 - 136 . The data reduction processing system  16  partitions the SC data into ranges ( 152 ). For each range, the SC data for the object is reduced based on the magnitude of intensity (e.g., RCS value) of the SC data points ( 156 ). In one example, reduction may include reducing the SC data by a percentage using the RCS values of each data point. For example, for each SC type by range, the SC data points having the lowest 10 percent RCS value are eliminated. 
   Referring to  FIG. 4 , ranges  172   a - 172   g  are perpendicular to a line of site (LOS) axis  174  directed from a radar  170  to an object  171 . In one example, the range  172   a  is from zero to 1 meter, the range  172   b  is from 1 meter to 2 meters, range  172   c  is from 2 meters to 3 meters and so forth. 
   Referring to  FIGS. 5A and 5B , using the process  150 , for example, an original SC data set  182  for a missile may be reduced from 640 SC data points to a reduced data set  184  of 74 SC data points.  FIGS. 5A and 5B  each represent SC data points in 3-D but for simplicity only two dimension are shown. 
   Referring to  FIG. 6 , data reduction processing system  16  may be configured as a data reduction processing system  16 ′, for example. The data reduction processing system  16 ′ includes a processor  202 , a volatile memory  204  and a non-volatile memory  206  (e.g., hard disk). The non-volatile memory  226  stores computer instructions  214 , an operating system  210  and data  212  including SC data  222  and comparison metrics  224 . In one example, the computer instructions  214  are executed by the processor  202  out of volatile memory  204  to perform all or part of the process  100  or the process  150 . 
   Processes  100  and  150  are not limited to use with the hardware and software of  FIG. 6 ; it may find applicability in any computing or processing environment and with any type of machine or set of machines that is capable of running a computer program. Processes  100  and  150  may be implemented in hardware, software, or a combination of the two. Processes  100  and  150  may be implemented in computer programs executed on programmable computers/machines that each includes a processor, a storage medium or other article of manufacture that is readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and one or more output devices. Program code may be applied to data entered using an input device to perform processes  100  and  150  and to generate output information. 
   The system may be implemented, at least in part, via a computer program product, (e.g., in a machine-readable storage device), for execution by, or to control the operation of, data processing apparatus (e.g., a programmable processor, a computer, or multiple computers)). Each such program may be implemented in a high level procedural or object-oriented programming language to communicate with a computer system. However, the programs may be implemented in assembly or machine language. The language may be a compiled or an interpreted language and it may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program may be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network. A computer program may be stored on a storage medium or device (e.g., CD-ROM, hard disk, or magnetic diskette) that is readable by a general or special purpose programmable computer for configuring and operating the computer when the storage medium or device is read by the computer to perform processes  100  and  150 . Processes  100  and  150  may also be implemented as a machine-readable storage medium, configured with a computer program, where upon execution, instructions in the computer program cause the computer to operate in accordance with process  100 . 
   The processes described herein are not limited to the specific embodiments described. For example, the processes  100  and  150  are not limited to the specific processing order of  FIGS. 3A and 3B , respectively. Rather, any of the processing blocks of  FIGS. 3A and 3B  may be re-ordered, combined or removed, performed in parallel or in serial, as necessary, to achieve the results set forth above. 
   The processing blocks in  FIGS. 3A and 3B  associated with implementing the system may be performed by one or more programmable processors executing one or more computer programs to perform the functions of the system. All or part of the system may be implemented as, special purpose logic circuitry (e.g., an FPGA (field programmable gate array) and/or an ASIC (application-specific integrated circuit)). 
   Elements of different embodiments described herein may be combined to form other embodiments not specifically set forth above. Other embodiments not specifically described herein are also within the scope of the following claims.