Army 18. 1 Small Business Innovation Research (sbir) Proposal Submission Instructions

PHASE II: The offeror will demonstrate the capability of developed techniques and processes that will integrate into existing Army analytics, conceptualize the methodology from Phase I, and apply capability of concepts to support the development of refining data. The produced methodologies and applications must be verified, and specifications for implementation with the government should be articulated.

PHASE III DUAL USE APPLICATIONS: The innovation developed under this topic will then be taken from the theoretical science developed and be applied to practical applications involving Army and industry data. Proof of concept application will be used with multiple data sources from the Army, or similar industry. For Army applications, the offeror must have a full understanding of the Army Data & Data Rights (D&DR) Guide. The data refinery would provide data used for prognostics/diagnostics, smart line replaceable units, and other tools. The expectation is that the government would use this innovation to support Army data analytics and future advanced sustainment programs.

REFERENCES:

1. Silver, N. (2012). “The signal and the noise: Why most predictions fail but some don’t”. New York, N.Y: The Penguin Press.

2. “Data Reduction”, https://en.wikipedia.org/wiki/Data_reduction, Accessed 29 June 2017, webpage.

3. “Army Data & Data Rights (D&DR) Guide: A reference for planning and performing”, http://www.acq.osd.mil/dpap/cpic/cp/docs/Army_Data_and_Data_Rights_Guide_1st_Edition_4_Aug_2015.pdf

KEYWORDS: analytics, noisy data, signal-to-noise, data refinery, data reduction, log data, sustainment, process improvement

A18-005

TITLE: Dynamic Near-Field Radar Target Modeling in Scene Generator Systems

TECHNOLOGY AREA(S): Weapons

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), which controls the export and import of defense-related material and services. Offerors must disclose any proposed use of foreign nationals, their country of origin, and what tasks each would accomplish in the statement of work in accordance with section 5.4.c.(8) of the Announcement.

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 DUAL USE APPLICATIONS: 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.

REFERENCES:

1. D. L. Mensa, High Resolution Radar Imaging. Norwood, MA: Artech House, 1982.

2. N. C. Curie, Radar Reflectivity Measurement. Norwood, MA: Artech House, 1989.

3. D. G. Falconer, "Extrapolation of Near-Field RCS Measurement to the Far Zone", IEEE Transactions on Antennas and Propagation, vol. 36, pp. 822-829, June 1988.

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

TECHNOLOGY AREA(S): Electronics

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), which controls the export and import of defense-related material and services. Offerors must disclose any proposed use of foreign nationals, their country of origin, and what tasks each would accomplish in the statement of work in accordance with section 5.4.c.(8) of the Announcement.

OBJECTIVE: Develop a system that can be integrated and deployed in a class 1 or class 2 Unmanned Aerial System (UAS) to automatically Detect, Recognize, Classify, Identify (DRCI) and target personnel and ground platforms or other targets of interest. The system should implement learning algorithms that provide operational flexibility by allowing the target set and DRCI taxonomy to be quickly adjusted and to operate in different environments.

DESCRIPTION: The use of UASs in military applications is an area of increasing interest and growth. This coupled with the ongoing resurgence in the research, development, and implementation of different types of learning algorithms such as Artificial Neural Networks (ANNs) provide the potential to develop small, rugged, low cost, and flexible systems capable of Automatic Target Recognition (ATR) and other DRCI capabilities that can be integrated in class 1 or class 2 UASs. Implementation of a solution is expected to potentially require independent development in the areas of sensors, communication systems, and algorithms for DRCI and data integration. Additional development in the areas of payload integration and Human-Machine Interface (HMI) may be required to develop a complete system solution. One of the desired characteristics of the system is to use the flexibility afforded by the learning algorithms to allow for the quick adjustment of the target set or the taxonomy of the target set DRCI categories or classes. This could allow for the expansion of the system into a Homeland Security environment.

PHASE I: Conduct an assessment of the key components of a complete objective payload system constrained by the Size Weight and Power (SWAP) payload restrictions of a class 1 or class 2 UAS. Systems Engineering concepts and methodologies may be incorporated in this assessment. It is anticipated that this will require, at a minimum, an assessment of the sensor suite, learning algorithms, and communications system. The assessment should define requirements for the complete system and flow down those requirements to the sub-component level. Conduct a laboratory demonstration of the learning algorithms for the DRCI of the target set and the ability to quickly adjust to target set changes or to operator-selected DRCI taxonomy.

PHASE II: Demonstrate a complete payload system at a Technology Readiness Level (TRL) 5 or higher operating in real time. On-flight operation can be simulated. Complete a feasibility assessment addressing all engineering and integration issues related to the development of the objective system fully integrated in a UAS capable of detecting, recognizing, classifying, identifying and providing targeting data to lethality systems. Conduct a sensitivity analysis of the system capabilities against the payload SWAP restrictions to inform decisions on matching payloads to specific UAS platforms and missions.

PHASE III DUAL USE APPLICATIONS: Develop, integrate and demonstrate a payload operating in real time while on-flight in a number of different environmental conditions and providing functionality at tactically relevant ranges to a TRL 7. Demonstrate the ability to quickly adjust the target set and DRCI taxonomy as selected by the operator. Demonstrate a single operator interface to command-and-control the payload. Demonstrate the potential to use in military and homeland defense missions and environments.

REFERENCES:

1. John P. Abizaid and Rosa Brooks, Recommendations and Report of the Task Force on US Drone Policy (Washington, DC: The Stimson Center, 2014).

2. Y. Bengio, “Springtime for AI: the rise of deep learning,” Scientific American, June 2016.

3. Department of Defense, Joint Operational Access Concept ( JOAC), Department of Defense website, 17 January 2012.

4. M. T. Hagan, H. B. Demuth, M. Hudson Beale and O. De Jesus, Neural Networks Design, 2nd ed., Lexington, KY, published by Martin Hagan, 2016.

5. J. Heaton, Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks, St. Louis, MO, Heaton Research, Inc, 2015.

6. S. Samarasinghe, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition,” Boca Raton, FL, Auerbach Publications, 2007.

7. Yasmin Tadjdeh, “Small UAV Demand by U.S. Army Ebbs as Overseas Market Surging,” National Defense Magazine website, September 2013.

8. D. S. Touretzky and D. A. Pomerlau, “What’s hidden in the hidden layers?” BYTE Magazine, pp. 227-233, August 1989.

9. Robert O. Work and Shawn Brimley, 20YY: Preparing for War in the Robotic Age (Washington DC: Center for a New American Security, January 2014), 7.

10. Tedesco, Matthew T. “Countering the Unmanned Aircraft Systems Threat”, Military Review, November-December 2015, http://usacac.army.mil/CAC2/MilitaryReview/Archives/English/MilitaryReview_20151231_art012.pdf

KEYWORDS: Learning Algorithms, Artificial Neural Networks (ANNs), Automatic Target Recognition (ATR), Target Detection, Target Classification, Target Identification, Unmanned Air System (UAS), Targeting

TECHNOLOGY AREA(S): Weapons

OBJECTIVE: The objective is to develop a novel energy system that can meet munition power requirements for a period of 30 days and that could provide high-pulsed power on demand over this period. Candidate systems must be highly miniaturized, must integrate energy management functions to minimize energy consumption during the required 30 days power budget, must be fully compatible with currently available manufacturing processes and must be gun hardened.

DESCRIPTION: This effort seeks proposals for the development of novel energy management systems that can provide relatively low electrical power of the order of 10mW to munitions for a period of 30 days while being capable of provide a number of pulses for no longer than 1 sec each and no more than 500ma. The power system may use appropriate electrochemistry that can provide energy at the required levels for a period of 30 days. The energy system must be capable of being gun hardened and have a minimum shelf life of 20 years. The primary application is emplaced munitions which may have to satisfy the shock survivability requirement, such as gun launch and must be capable of withstanding launch accelerations of 100,000 Gs and preferably higher during launch. The energy management system must be capable of monitoring, regulating, informing the onboard information system within the munition of the energy expanded and the energy available for the mission and the number of pulses available in the system energy management system, at any point within the 30 day mission. The Novel Energy Management System must meet all military operational and storage temperature requirements of -65 deg. F to 165 degF and safety are of great importance.

The proposed energy management system concepts are expected to take full advantage of the highly developed battery and other power source technologies to develop a novel energy management system that can satisfy a wide range of military munitions, including gun-fired and emplaced munitions. The proposed energy management system must be capable of being miniaturized while the total system weight should also be considered. The system must also be highly reliable and safe for use in munitions. It is also highly desirable that in all applications the proposed energy management system concepts provide a high level of conformability to the available munitions space and its geometry. Manufacturability and the potential use of mass production processes developed for commercial applications to achieve low cost and high reliability is also of great importance.

PHASE I: Conduct a systematic feasibility study of the proposed energy management system concepts by computer modeling and simulation as well as basic laboratory testing to determine if they have the potential of meeting the desired power and energy requirements, high shock survivability, military shelf life, military operational and storage temperature requirements. Manufacturability of the proposed concepts and compatibility with mass production technologies used in similar commercial applications to achieve low cost and highly reliable gun hardened energy management systems must also be addressed. The Phase I effort must also address shelf life and safety issues and provide a detailed plan for the development of the energy management system concepts, along with their prototyping and testing during the project Phase II period. A successful phase I needs to include a trade study on 20 year shelf life cost drivers and recommend trade-offs that may reduce shelf life but significantly reduces life cycle cost.

PHASE II: Design and fabricate full-scale gun hardened energy management system prototypes of the selected concepts and test such prototypes in the laboratory and in relevant environments, including in shock loading machines and in air guns. Demonstrate that such prototypes can survive in operational environments while providing the designed power and voltages under simulated load conditions within the entire indicated operational temperature range. Prototypes must be subjected to laboratory tests and must include full operating cycles under simulated load conditions. The Phase II period must also include the fabrication and delivery of final prototypes of each selected design for the selected munitions applications.

PHASE III DUAL USE APPLICATIONS: The proposed gun hardened energy management system concepts would apply to gun fired munitions, weapon based platforms and emplaced munitions applications. Commercial uses for such technology could include application to the electric vehicle industry and also for energy recapture in industrial settings where renewable energy sources from machinery could provide huge cost savings.

REFERENCES:

1. Encyclopedia of Electrochemical Power Sources, C.K. Dyer et al, Elsevier Science (2010).

2. Handbook of Batteries - Linden, McGraw-Hill, “Technology Roadmap for Power Sources: Requirements Assessment for Primary, Secondary and Reserve Batteries”, dated 1 December 2007, DoD Power Sources Working Group.

3. Macmahan, W., “RDECOM Power & Energy IPT Thermal Battery Workshop – Overview, Findings, and Recommendations,” Redstone Arsenal, U.S. Army, Huntsville, AL, April 30 (2004).

4. Linden, D., “Handbook of Batteries,” 2nd Ed., McGraw-Hill, New York, NY (1998).

5. Guidotti, R. A., Reinhardt, F. W., Reisner, J. D., and Reisner, D. E., “Preparation and Characterization of Nanostructured FeS2 and CoS2 for High-Temperature Batteries,” to be published in proceedings of MRS meeting, San Francisco, CA, April 1-4, 2002.

6. Warner, J., “The Handbook of Lithium-Ion Battery Pack Design - Chemistry, Components, Types and Terminology”, Elsevier Science (2017).

KEYWORDS: energy management system, electrical power system, electrical power sources, mechanical shock and vibration, low temperature performance

TECHNOLOGY AREA(S): Weapons

OBJECTIVE: Develop and demonstrate innovative methods for determining vulnerability of munitions components and integrated munitions to electromagnetic interference (EMI) and pulse (EMP) and innovative and feasible packaging concepts for significantly reducing these vulnerabilities.

DESCRIPTION: Exposure of munitions and its components to high levels of electromagnetic interference (EMI) and electromagnetic pulse (EMP) is one of the threats that can result in catastrophic consequences. The increasingly pervasive use of electronics of all forms represents the greatest source of vulnerability to EMI and EMP. Shielding of electrical and electronic devices and systems from catastrophic effects of high levels of EMI and EMP radiation presents an ongoing challenge. This problem is exacerbated by the wide range of EMP devices ranging from hand-held, operating from battery packs to much larger systems capable of rendering havoc over many city blocks. Additionally, the broad emission frequency spectrum makes a single technology solution unattainable. Currently used EMI and EMP shields are effective for the protection of electrical components under small levels of such electromagnetic illumination. In theory, ferro-magnetic cages can provide adequate protection but are mostly impractical for most munitions applications due to the volume constraints and the need for sensory and other device exposure to outside world. We are seeking innovative solutions, which represent revolutionary departure from current thinking of the day. Today, shield technologies are concerned with minimizing the electromagnetic radiation coupling through access ports or wires connecting to the outside. Under certain simplifications, closed form theoretical solutions are aiding in understanding the effectiveness of these structures. Realistic solutions are tractable using finite integration techniques (FIT), method of moments (MoM) and finite-difference time-domain (FDTD). Such solutions may also be extensible to wearable EMI and EMP shields for protection of military personnel and weapon platforms. Responsive proposals will describe novel approaches to minimizing damage to strong EMI and EMP exposure. Some of these techniques will include the use of meta-materials, composite material with nano-structures that minimize transmission through a combination of scattering, guiding and absorption. A clear path to validation, which does not require a strong EMI and EMP sources, is expected. Additionally, the efficacy of the structures needs verification through numerical solvers, based on physical models for EM propagation and interaction. Use of anechoic chambers for experimental characterization is encouraged. The developed technologies must add minimal volume to existing component.