Abstract:
A method to reduce scattering centers (SC) includes receiving a set of SC data points representing an object. The method also includes reducing SC data points associated with a first region based on magnitudes of intensity of the SC data points associated with the first region, reducing SC data points associated with a second region based on magnitudes of intensity of the SC data points associated with the second region, combining the reduced SC data points associated with the first region and the second region to form a reduced set of SC data points, 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, performing another iteration of the reducing.

Description:
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®. 
   The radar signature modeling tools produce scattering center (SC) data associated with a radar object. The SC data includes Physical Optic (PO) SC data and Diffraction (DF) SC data. The PO SC data is associated with a surface of the radar object. The DF SC data is associated with the edges of the radar object. The SC data may be further categorized between single bounce (SB) and multiple bounce (MB). The SB SC data is associated with SC data indicative of a single bounce off of the object (direct reflection). The MB SC data is associated with SC data from multiple bounces (or indirect reflections) from the object. 
   SUMMARY 
   In one aspect, a method to reduce scattering centers (SC) includes receiving a set of data points representing an object. The object includes a first region and a second region. The method also includes reducing SC data points associated with the first region based on magnitudes of intensity of the SC data points associated with the first region, reducing SC data points associated with the second region based on magnitudes of intensity of the SC data points associated with the second region and combining the reduced SC data points associated with the first region and the second region to form a reduced set of SC data points. The method further 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, performing another iteration of reducing the SC data points by region based on the magnitudes of intensity of the SC data points for each region. 
   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 representing an object. The object includes a first region and a second region. The instruction also cause a machine to reduce SC data points associated with the first region based on magnitudes of intensity of the SC data points associated with the first region, reduce SC data points associated with the second region based on magnitudes of intensity of the SC data points associated with the second region and combine the reduced SC data points associated with the first region and the second region to form a reduced set of SC data points. The instructions further 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, perform another iteration of reducing the SC data points by region based on the magnitudes of intensity of the SC data points for each region. 
   In a further aspect, an apparatus includes circuitry to receive a set of SC data points representing an object from a radar signature modeling tool. The object includes a first region and a second region. The apparatus also includes circuitry to reduce SC data points associated with the first region based on magnitudes of intensity of the SC data points associated with the first region, reduce SC data points associated with the second region based on magnitudes of intensity of the SC data points associated with the second region and combine the reduced SC data points associated with the first region and the second region 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, perform another iteration of reducing the SC data points by region based on the magnitudes of intensity of the SC data points for each region. 
   In a still further aspect, a method to reduce scattering centers (SC) includes receiving a set of SC data points representing an object from a radar signature modeling tool, reducing SC data points associated with a first region based on magnitudes of intensity of the SC data points associated with the first region, reducing SC data points associated with a second region based on magnitudes of intensity of the SC data points associated with the second region and combining the reduced SC data points associated with the first region and the second region to form a reduced set of SC data points. The method further 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 including a similarity metric, a maximum amplitude metric, a length metric and a relative maximum amplitude metric, and if the reduced set of SC data points meets the set of comparison metrics, performing another iteration of reducing the SC data points by region based on the magnitudes of intensity of the SC data points for each region. 

   
     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. 
       FIG. 3  is a flow chart of a process to reduce SC data. 
       FIG. 4  is a block diagram of an example of a computer on which the process of  FIG. 3  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 example, 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 for a radar object. 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. 
   The data reduction processing system  16  identifies the components of an object and groups them into regions. For example, the SC data of a missile is compartmentalized into clustered regions by grouping together SC data based on components. The regions may include the nose of the missile, the body of the missile, the tail of the missile and so forth. In one example, a region may include one or more components. In one example, the number of regions is determined (e.g., by a user) depending on a size and shape of the object. 
   By segregating the object into smaller discrete regions, the data reduction processing system  16  ensures that the SC data from all of the regions of the object will be represented after the reduction and not just those regions having SC data with relatively higher intensity values (e.g. higher RCS values) than those of other regions of the object. 
   Within each region, the data reduction processing system  16  segregates the SC data into SC types. For example, the SC types include physical optic (PO) Single Bounce (SB), diffraction (DF) SB and PO multi-bounce (MB). SC data belonging to each SC type are separately reduced by region to form a reduced SC data set representing the SC type by region. In one example, reduction may include reducing the SC data by a percentage using the RCS values of each SC data point. For example, for each SC type by component, the data points having the lowest 10 percent RCS value are eliminated. 
   The reduced SC data sets representing their respective SC type and region are added together to form a reduced SC data set for the object. The reduced SC data set for the object is compared to the original SC data set received. 
   The data reduction processing system  16  performs the reduction of the SC data incrementally and checks the reduced SC data after each increment to determine as to whether the complexity of the object is being maintained. 
   In one particular example, the original SC data set provided by the data generation system  12  includes fields (e.g., flags) for each SC data point that include material identification (ID), component ID and bounce information (e.g., SB and MB). The material ID identifies the material and the component ID identifies the component associated with an SC data point. 
   In one example, the SC type may be determined based on one or more of the material identification (ID), the component ID and the bounce information. In particular, one or more of the material identification (ID), the component ID and the bounce information is compared with a SC determination table (e.g., a SC determination table  226 ) to determine the associated SC type. 
   In another example, other fields may be included in the original SC data set that identifies the SC type. The data reduction processing system  16  may use the fields to partition the original SC data set by region and to segregate the SC data set by SC type for each region. 
   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 a 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. The 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 are used to 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, the difference 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 )). 
   Referring to  FIG. 3 , in one example, a process to reduce SC data is a process  100 . The data reduction processing system  16  receives SC data associated with an object from the data generation system  12 , for example ( 102 ). The data reduction processing system  16  partitions the SC data into regions ( 108 ). For example, the SC data is partitioned into regions of the object based on one or more component IDs. For example, if the object is a missile, the regions include a nose of the missile, a body of the missile and a tail of the missile. In one example, a missile includes nine regions. 
   The data reduction processing system  16  segregates the SC data by region into SC types ( 112 ). For example, if an object is a missile, the regions may be a tail, a body, a nose and so forth. In one example, the regions are determined by identifying fields in the SC data. The data reduction processing system  16  reduces the SC data by SC type and region ( 118 ). The data reduction processing system  16  combines the remaining SC data from each SC type for all regions to form a reduced SC data set ( 122 ). The data reduction processing system  16  determines if the reduced SC data meets the criteria ( 128 ). 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 , for example. If the reduced data meets the criteria, the data reduction processing system  16  starts another reduction cycle ( 118 ). 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 ( 132 ). 
   Referring to  FIG. 4 , 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 , comparison metrics  224 , the SC determination Table. In one example, the computer instructions  214  are executed by the processor  202  out of volatile memory  204  to perform the process  100 . 
   Process  100  is not limited to use with the hardware and software of  FIG. 4 ; 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. Process  100  may be implemented in hardware, software, or a combination of the two. Process  100  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 process  100  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 process  100 . Process  100  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 process  100  is not limited to the specific processing order of  FIG. 3 , respectively. Rather, any of the processing blocks of  FIG. 3  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  FIG. 3  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.