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Dynamic Near-Field Radar Target Modeling in Scene Generator Systems



OBJECTIVE: Develop a method and supporting tools for dynamic correction and application of radar near field effects on target models and associated radar signatures within existing scene generation capabilities. 

DESCRIPTION: Radar sensors, seekers, and algorithms are tested and evaluated by the U.S. Army through the use of a wide array of environments and scene generators to include all-digital, signal injection, and hardware-in-the-loop simulations. These simulations and associated scene generators run in open or closed-loop as well as real-time and near-real-time and include the Army’s Common Scene Generator (CSG) simulation as well as RF3, MSS-1, and MSS-2 HWIL facilities at the Army Missile Research Development Engineering Center (AMRDEC) Advanced Simulation Center (ASC). High fidelity scene generators utilize radar target models for signal generation over the entire flight profile of a weapon system; consequently, the slant range condition and increasing near-field engagement changes with every sensor dwell or time-step as the simulation progresses to the endgame state. Near-field engagement of a target can produce significantly different radar signatures, as compared to far-field signatures, that influence radar system and algorithm performance. As such, the Army has a need to develop methods and tools for the dynamic correction and application of radar near field effects on target models and radar signatures in scene generation frameworks to ensure high fidelity target model characterizations throughout the entire flight scenario. Endgame scenarios result in shorter and shorter slant ranges between the target and weapon system resulting in significant and changing wave front curvature from the illuminating radar. This curvature produces range dependent changes in location and amplitude of radar scattering from target bodies. However, radar signatures and associated target models are generally derived from a set of empirical or predictive radar data with a single, discrete slant range condition. In the case of turntable measurement data typically used in full scale ground or aerial targets, this discrete slant range may be near-field condition on the order of several hundred meters for a fixed tower and pedestal geometry or an effective far-field condition given illumination by a collimating reflector. Likewise, many predictive data solutions for target modeling applications assume a far-field collimated wave front in synthetic data production. For each elevation or roll angle of target data, a full 360 degree azimuth cut of data is obtained for a specific slant range to target. As such, radar data is measured or predicted with a singular distance to the target that is governed by a specific slant range relative to a measurement setup or predictive data scenario. Consequently, the signature effects of this single slant range condition, near or far-field, are generally embedded in resultant radar target models that support scene generator signal generation. As scene generation capability and weapon system algorithm requirements advance for aimpoint refinement, hit to kill, fuzing, and smart munition requirements, near-field effects on target radar signatures must be considered in high fidelity simulation environments. The scene generator must accurately present these effects to ensure signature fidelity and appropriate scattering phenomena for performance assessments of weapon systems throughout the endgame scenario. In addition, target modeling methodologies must support the dynamic change and update of the near field condition. Not only do underlying range effects in target data and associated targets models need to be accounted and corrected for in base models, the continuous sampling of the target model throughout the simulation scenario results in continuum of range or distance dependent models that are not feasibly satisfied by discrete instantiations of a target model. As such, this task will investigate and identify innovative methodologies and techniques to provide dynamic target models that present and modify target scatterers and output signatures as a function of the near field condition. Metrics should be identified and tested at the Radar Cross Section (RCS) and Inverse Synthetic Aperture Radar (ISAR) image levels to measure performance of the near field transform implementation to quantify accuracy. In addition, methodologies should utilize and consider data available from standard DoD measurement ranges and predictive data sources as well existing target model databases currently utilized in scene generation and simulation environments. Given a dynamic near-field modeling approach, near-field enabled target models should integrate with existing scene generation capabilities in all-digital and HWIL environments. 

PHASE I: Identify an approach and demonstrate the feasibility of selected methodology for a creation of a dynamic, near-field target model for use with empirical and predictive data sources as well as existing Ka-band target model databases. Define requirements for integration with government owned scene generator systems. Derive metrics at the RCS and ISAR image level. Test and/or progress metrics to measure, test transforms and quantify performance. Recommend a method to validate proposed algorithms and methodologies. 

PHASE II: Develop corresponding algorithms and processes for creation and integration of dynamic, near-field target model solutions. Demonstrate near-field, Ka-band target model creation for both ground and aerial target samples with dynamic model comparison to measured and predictive data. Use derived metrics from Phase I to evaluate implementation. 

PHASE III: Integrate the application into existing scene generation software applications used by the Army for all-digital and HWIL simulation environments. Conduct a thorough demonstration of dynamic near-field modeling capabilities within scene generation framework. Conduct validation of near-field target model and scene generation approach. 


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4:  A. Broquetas, J. Palau, L. Jofre, A. Cardama, "Spherical Wave Near-Field Imaging and Radar Cross-Section Measurement," IEEE Transactions on Antennas and Propagation, vol. 46, pp. 730-735, May 1998.

5:  J. Fortuny, "An Efficient 3-D Near-Field ISAR Algorithm," IEEE Transactions on Aerospace and Electronic Systems, vol. 34, pp 1261-1270, October 1998.

KEYWORDS: Scene Generation, Radar, Near-field Wave Far-field Wave, Scattering Models, Ka-band, HWIL 


Lawrence Smith 

(256) 842-3272 

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