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

PHASE II: Fabricate receiver prototype to test, demonstrate and validate the feasibility of the concept under simulated laboratory conditions. Demonstrate GPS-independent synchronization of networked receivers and pinpoint the locations of multiple emitters. Deliver the final report, TRL 5 networked receiver prototypes (four units), its description and operation guide, and laboratory test reports.

PHASE III DUAL USE APPLICATIONS: Implement the demonstrated algorithm in GPS P(Y) and M codes. Develop a small size, weight, and power (SWAP) system applicable to mounted or dismounted platforms. Other military applications could include unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and other robotic platforms. This technology is transitioned to the Army Assured Positioning, Navigation, and Timing (PNT) Program.

REFERENCES:

1. M. L. Psiaki and T. E. Humphreys, "Protecting GPS from spoofers is critical to the future of navigation," IEEE Spectrum, Vol. 53-8, pp. 26-53, August 2016.

2. K. C. Ho and Y. T. Chan, "Solution and performance analysis of geolocation by TODA," IEEE Tr. Aerospace and Electronics Systems, Vol. 29, No. 4, pp.1311-1322, 1993.

KEYWORDS: GPS anti-spoofing, Detection and localization of signal emitters, Time difference of arrival.

A18-036

TITLE: Increased Capacity Retention of Silicon Anodes for Lithium Batteries

TECHNOLOGY AREA(S): Materials/Processes

OBJECTIVE: The objective of this topic is to develop advanced silicon anode cells with increased capacity retention, coulombic efficiency, and electronic conductivity.

DESCRIPTION: The U.S. Army TRADOC has identified a need for reliable, high-energy power sources to support soldier, squad, and platoon operational requirements, especially in austere environments where power source availability is limited. The integration of silicon anodes, with their high theoretical specific capacity (4.2 Ah/g), into cells and subsequent battery packs will assist in extending mission endurance in support of dominating the electromagnetic spectrum, commanding the operation, and more directly enabling decisive effects.

The introduction of silicon as an anode in lithium-ion rechargeable batteries can greatly increase their energy density, especially in comparison to carbon. However, silicon is plagued by poor capacity retention as a result of the volume expansion that occurs during lithiation and delithiation while cycling. This volume expansion results in particle fractures across the anode. Anode fracturing will then have a detrimental effect on the cell’s capacity, capacity retention, coulombic efficiency, and performance at high rates. This topic desires to mitigate or eliminate some of these detrimental effects in order to improve capacity retention and coulombic efficiency. Target cell-level requirements include a high specific capacity of 750 mAh/g with at least 25 wt% silicon content, capacity retention of 224 cycles to 80% original capacity at a rate of 1 mA/cm2. Cells must be able to operate from -30 °C to 55 °C. Developmental cells must also demonstrate the ability to handle high rate loads effectively, with minimal impact to capacity. Prototype cells must deliver at least 400 Wh/kg at the cell level, targeting 300-600 Wh/kg at the battery level. The final battery shall weigh less than or equal to 2.6 lbs.

PHASE I: Explore and define cell materials or half cells demonstrating improvements that mitigate or eliminate detrimental effects of Si anodes in order to improve electrochemical performance. Demonstrate pathway for reaching target requirements outlined in this topic.

PHASE II: Refine and optimize materials chosen in Phase I and develop prototype pouch cells to meet target performance requirements in the specified temperature range outlined in this topic.

PHASE III DUAL USE APPLICATIONS: Transition technology to the U.S. Army. Integrate this technology into portable consumer or military devices that require high energy density power sources.

REFERENCES:

1. Chief of Staff of the Army Priority #1

2. Army Warfighter Challenge #16

3. Xiuxia Zuo, Jin Zhu, Peter Müller-Buschbaum, Ya-Jun Cheng. "Silicon based lithium-ion battery anodes: A chronicle perspective review." Nano Energy, Volume 31, January 2017, Pages 113-143.

4. François Ozanam, Michel Rosso. "Silicon as anode material for Li-ion batteries." Materials Science and Engineering: B, Volume 213, November 2016, Pages 2-11

5. Choa Kim, Deepak Verma, Dong Ho Nam, Wonyoung Chang, Jaehoon Kim. "Conformal carbon layer coating on well-dispersed Si nanoparticles on graphene oxide and the enhanced electrochemical performance." Journal of Industrial and Engineering Chemistry, Volume 52, August 2017, Pages 260-269.

KEYWORDS: Silicon anode, rechargeable, lithium batteries, capacity retention, coulombic efficiency, dismounted Soldier power

A18-037

TITLE: Machine Learning Techniques for Tactical Mission Command

TECHNOLOGY AREA(S): Information Systems

OBJECTIVE: Perform research into Machine Learning and its applicability to Mission Command in tactical environments. Improve Mission Command which includes tools, processes, and personnel across echelons involved in all phases of operations. Develop a study that considers operational environments, soldier needs and tasks, existing systems, availability of data, and the feasibility to apply Machine Learning in the Mission Command domain.

DESCRIPTION: Human reaction time is just too slow during critical Military operations and decision making. An autonomous learned system (i.e. Machine Learning) can understand large amounts of data, manage the results, and react faster to cyber defense, electronic warfare, and large raid attacks.

The Army wants to assess the potential costs, benefits, and risks of applying Machine Learning to Mission Command, the operations process, and decision making. Machine Learning relies on models that consume real-time operational data to provide predictions, alerts, and recommendations. The ultimate goal of this SBIR is to develop strategic insights into the incorporation of Machine Learning techniques to enhance human performance in the processing of information management and knowledge management in the exercise of Mission Command. Ultimately, an analysis of the cost, benefits, and risks in applying specific Machine Learning techniques to specific tasks across the planning and operations phases is required.

The domain of the research is the tactical environment, specifically at the Brigade and lower levels. Machine Learning techniques applicable to Mission Command and soldiers in specific echelons should be studied, with a consideration of the tasks and data that can drive Machine Learning models. The study should also assess aspects of Machine Learning and its associated models that may be appropriate for Mission Command cross-echelon collaboration and problem solving.

Fundamental to this study are (1) a deep understanding of Machine Learning techniques, (2) an understanding of peculiar Mission Command tasks in the tactical environment, and (3) a consideration of data availability. Specific data types and sources to drive the Machine Learning techniques should be delineated.

PHASE I: This Phase will develop a methodology to assess the applicability of specific Machine Learning techniques to various Mission Command planning and operational tasks in specific echelons and environments. This should include insights into the costs and benefits of specific Mission Command task / Machine Learning combinations, and begin to highlight opportunities for possible development, as well as gaps for future research.

Machine Learning Techniques - Review of Techniques, Priorities, and Justification:

The contractor shall analyze Machine Learning techniques and tools by assessing their applicability to data environments and solider needs such as those found at the Brigade and lower echelons. The contractor is expected to bring a deep and broad body of Machine Learning knowledge to the research tasks. While this is not intended to be comprehensive, the contractor should build confidence in his ability to consider enterprise-level Machine Learning techniques for the less data-rich environment.

Mission Command Tasks - Review of Tasks, Priorities, and Justification:

The contractor shall explore the differences in Mission Command tasks by warfighting function, both within and across echelons. An assessment of the data and information that could drive Machine Learning in notional Mission Command tasks is desired. Identifying the right types of Machine Learning tasks for the various data environments across the lower echelons is key.

Methodology to Predict Cost, Benefits, and Risk for Each Machine Learning Technique vs. Mission Command Task:

The contractor shall develop a methodology for assessing the applicability of individual Machine Learning Techniques vs. individual Mission Command tasks / goals. Reasonable ways to assess or measure potential costs and benefits for a combination should be presented, and a way to assess risks for a specific combination should also be explored.

Methodology for Validation of Cost, Benefits, and Risk Predictions for Each Machine Learning Technique vs. Mission Command Task:

The contractor shall develop a detailed approach to validate the methodology for measuring costs/benefits/risks in the preceding paragraph. There may be different techniques for doing this ranging from Subject Matter Expert Review to development and use of a data-driven Machine Learning model by a prototypical user. The contractor should build confidence that the analytical approach for task and technique is sound.

PHASE II: This Phase will develop a cross-walk of Mission Learning techniques and key Mission Command tasks in specific echelons based upon the approach and conclusions from Phase I. Using insights gained from Phase I, as well as government oversight, the contractor is expected to highlight promising Mission Command task / Machine Learning technique combinations. The Government may exercise a subset of the task/technique combinations identified in Phase I with representative data sets to develop models appropriate to specific task support. The contractor will work with the Government to validate the approach and conclusions using the methodologies from Phase I. These methodologies will be refined and matured as part of the validation.

The end goal of Phase II to integrate Machine Learning into the Army tactical environment. The contractor will develop a concept demonstrator based on a set of recommendations and technical guidance to validate the integration.

PHASE III DUAL USE APPLICATIONS: During Phase III of the SBIR, the contractor will mature and develop concept demonstrator(s) for integration into the Command Post and Mounted Computing Environment systems of record. Additionally, the contractor must identify potential commercial applications for the Machine Learning techniques.

REFERENCES:

1. ADP 6-0, Mission Command, May 2012

2. ADP 5-0 The Operations Process, May 2012

3. ADRP 3-0 Unified Land Operations, May 2012

4. ADRP 6-0 Mission Command, May 2012

5. Twenty-First Century Information Warfare and the third offset Strategy (page 16 of the Joint Force Quarterly issue 82 3rd Quarter 2016
6. Pellerin, Cheryl, Learning Systems, Autonomy and Human-Machine Teaming, DoD News, Defense Media Activity, Nov. 13, 2015 (uploaded in SITIS on 1/4/18).
7. Common Operating Environment (COE), US Army, April 2017, 28 pages (uploaded in SITIS on 1/4/18).

KEYWORDS: Machine Learning, Mission Command, autonomous, learned, decision making, models, information management, human performance, knowledge management, planning, operational, tasks

TECHNOLOGY AREA(S): Information Systems

OBJECTIVE: Develop an emulation system capable of deploying a full Long Term Evolution (LTE) Fifth Generation (5G) architecture, including all subcomponents, utilizing open source software, into self-contained commodity personal computer hardware. Utilize exclusively open source shared code components to construct LTE software and standardized interfaces with an emulated physical layer utilizing an environment such as Extendable Mobile Ad-hoc Network Emulator (EMANE) to provide a low cost capability to rapidly conduct research and experimentation on utilizing LTE for tactical comms and the performance of tactical sensors and other mission command systems under varying scenario configurations and conditions.

DESCRIPTION: As various U.S. Army units and program offices consider utilizing cellular Long Term Evolution (LTE) capabilities on the battlefield, there is a need to research and study the possible use cases and potential performance in order to feed the planning of the proposed LTE solution. A lab based, open source LTE emulation will provide the ability to deploy and full LTE ecosystem including but not limited to base station eNodeB, Enhanced Packet Core (EPC) and User Equipment (UE) within a cost effective set of commodity PC hardware. The LTE emulation, when integrated with other tactical network emulations, will allow analysis of the performance of various mission command applications and intelligence sensors and software while utilizing a tactical LTE solution. Additionally, the system will allow study of techniques for integrating LTE infrastructure into the existing tactical comms network architecture including interoperability with other tactical radios and electronic warfare systems.

PHASE I: Study the full ecosystem of Long Term Evolution (LTE) including eNodeB, Enhanced Packet Core (EPC) and User Equipment (UE) and provide a whitepaper detailing the design of an open source full LTE deployment “in-a-box” including physical layer emulation and upper layer software. The preliminary design must include interfacing UE upper layer software with an emulated physical layer provided by an open source environment such as Extendable Mobile Ad-hoc Network Emulator (EMANE) including user and control signaling and data. The design must also include non-proprietary implementations of all the infrastructure components and features of the latest LTE release and the ability to successfully communicate inter and intra-cell. The system must accurately represent all LTE components as defined by the 3rd Generation Partnership Project (3GPP) specification. Considerations for scaling up to multiple eNodeB base stations with hundreds of UEs per base station must be included.

PHASE II: Implement the design provided in PHASE I by developing a prototype open source Long Term Evolution (LTE) emulation which can be integrated into a larger system of systems emulation. System must allow for instantiating all open source software and tools required to provide a LTE capability on commodity hardware. Implementation must include at least a single Enhanced Packet Core (EPC)/EnodeB and 100 User Equipment (UE) devices.

PHASE III DUAL USE APPLICATIONS: Scale production of Long Term Evolution (LTE) emulation to multiple EnodeB/Enhanced Packet Core (EPC) base stations as well and hundreds of User Equipment (UE) devices.

REFERENCES:

1. R. Chertov, J. Kim, J. Chen, “LTE Emulation over Wired Ethernet,” Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 44)

2. R. Wang, Y. Peng, H. Qu, W. Li, H. Zhao, B. Wu, “OpenAirInterface-an effective emulation platform for LTE and LTE-Advanced,” 2014 Sixth International Conference on Ubiquitous and Future Networks (ICUFN)

3. T. Molloy, Z. Yuan, G. Muntean, “Real time emulation of an LTE network using NS-3,” Irish Signals & Systems Conference 2014 and 2014 China-Ireland International Conference on Information and Communications Technologies (ISSC 2014/CIICT 2014). 25th IET

4. L. Veytser, B. Cheng, R. Charland, “Integrating radio-to-router protocols into EMANE,” 10.1109/MILCOM.2012.6415571

5. K. Jain, A. Roy-Chowdhry, K. K. Somasundaram, B. Wang, J. Baras, “Studying real-time traffic in multi-hop networks using the EMANE emulator: capabilities and limitations,” SIMUTools '11 Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques, pp. 93-95, Barcelona, Spain, March 21 - 25, 2011

KEYWORDS: 3rd Generation Partnership Project, Long-Term Evolution, evolved NodeB, User Equipment, Extendable Mobile Ad-hoc Network Emulator, emulation, modeling & simulation, model

TECHNOLOGY AREA(S): Electronics

OBJECTIVE: Improve the performance of and yield of III-V antimonide-based dual band infrared detector focal plane arrays through the development of a high aspect ratio mesa delineation enabling improved optical fill factor, modulation transfer function (MTF), and uniformity.

DESCRIPTION: III-V antimonide (Sb) - based compound semiconductors and superlattices are of great interest for high performance detectors in the entire infrared spectrum. They have tunable bandgaps, offer significant cost benefits, and can be made into very large formats. Additionally, they have flexible band structures. Using bandgap engineering, the potential for a wide range of applications exists including decreased Auger and tunneling rates, as well as suppressed generation-recombination and surface currents. However technical challenges still need to be addressed in order to fully realize the potential benefits of III-V antimonide based infrared detector materials.

Dual-band focal plane arrays (FPAs) built with antimonide based Strained Layer Superlattice (SLS) materials are typically produced using partial delineation of the detector elements. Partial delineation has advantages in that the material is exposed to the high energy plasma for a shorter period of time affording a lower risk of plasma induced damage, and less material is removed leading to a higher overall optical fill factor. However, testing has shown that partial reticulation of SLS material has a negative impact on the MTF (a metric of detector performance) because of lateral diffusion of charge carriers. Full delineation of the detector mesas will prevent lateral diffusion improving the detector MTF, but is likely to reduce the optical fill factor and lower the quantum efficiency. A low temperature high aspect ratio delineation process compatible with standard photoresist mask formulation will be required to keep a high optical fill factor, prevent damage during delineation, and maintain high process yield.

Achieving full delineation while maintaining high optical fill factor will likely require the investigation of high density plasma techniques such as inductively coupled plasma (ICP) for mesa delineation.1 Consideration of the impact of trench shape/profile on the optical fill factor will be a critical aspect in the development of a delineation process. A trench with a “v” shaped groove is desirable to incorporate total internal reflection and further increase the optical fill factor. The surface composition and morphology following the mesa delineation is another critical aspect. The etched surfaces must be smooth to avoid light scattering and loss of quantum efficiency. Many plasma based delineation techniques require a wet chemical clean up following the plasma etch due to deleterious deposits formed by the plasma. The ideal plasma delineation process would leave a clean smooth surface and not require a wet chemical clean.2,3

PHASE I: Identify low temperature mesa delineation processes with high etch selectivity between the lithography mask and III-V semiconductor material capable of producing narrow high aspect ratio trenches between detector mesas. Demonstrate deep, > 10 m, delineation process on bulk semiconductor materials such as GaSb, InAs, and InSb or on dual band superlattice detector structures with top trench widths no greater than 4 m across.

Partnering with a commercial FPA manufacturer is strongly encouraged to support the potential commercialization of the developed process.

PHASE II: A detailed experimental study of delineation process parameters including surface morphology, composition, and profile. Apply the developed delineation process and demonstrate the performance on a dualband superlattice FPA with a U.S. Army relevant format of 512 x 512 or greater and pixel pitch between 8 and 15 m.

PHASE III DUAL USE APPLICATIONS: The contractor shall pursue commercialization of the various technologies and EO/IR components developed in Phase II for potential commercial uses in such diverse fields as law enforcement, rescue and recovery operations, maritime and aviation collision avoidance sensors, medical uses, homeland defense, and other infrared detection and imaging applications.

REFERENCES:

1. J. Nguyen, A. Soibel, D. Z.-Y. Ting, C. J. Hill, M. C. Lee, S. D. Gunapala, Appl. Phys. Lett. 97, 051108, (2010).

2. M. Razeghi, A. Haddadi, A. M. Hoang, R. Chevallier, S. Adhikary, A. Dehzangi, Proc. SPIE 9819, Infrared Technology and Applications XLII, 981909 (May 20, 2016)

3. E. A. Plis, T. Schuler-Sandy, D. A. Ramirez, S. Myers, S. Krishna, Electron. Lett., 51, 2009-2010 (2015)

KEYWORDS: strain layer superlattice (SLS), infrared detector, dual band, mesa delineation, focal plane arrays, antimonide based materials, plasma etch

A18-040

TITLE: Low-cost Imager for Heavily Degraded Visual Environments

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 compact, lightweight, low power, and low cost imager, capable of sensing through heavily degraded environments, that will augment long wave infrared (LWIR) imagery. The proposed system is only required to provide a rough outline of targets which, when fused with LWIR imagery, will provide enough context for maneuvering ground vehicles at 16 km/h in heavy dust.

DESCRIPTION: Degraded Visual Environments represent a significant challenge for the Army. They limit Soldier effectiveness by slowing down ground vehicle movement and increasing the chances for collision and injury. LWIR imagers have been used extensively by the military as they can operate day and night and under some degraded environments (e.g., light dust clouds). However, they show poor performance under heavy dust, fog, or smoke. Other sensing modalities show better penetration, but normally come with some caveats that prevent their practical implementation. Examples of these modalities are automotive radars and millimeter-wave imagers, which are virtually unaffected by airborne obscurants, but their resolution is poor. An enhanced imaging system consisting of a LWIR camera, coupled with a low-resolution imager that possesses better penetration, will provide enough cues to the driver to avoid obstacles and other hazards on the road. The objective is to be able to detect, but not necessarily identify those targets.