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

This effort requires an assessment of the feasibility of a receiver that can take in radio frequency (RF) radar return signals and output baseband I and Q in real-time with a relatively low noise figure. High Frequency and high dynamic range WB ADCs are inhibited by analog circuitry (principally down-conversion stages) in the receive chain. For example, mixers in down-conversion stages can introduce harmonics and nonlinearities. Conversion from the radio frequency (RF) domain directly to the digital domain eliminates most of these problems. Fortunately, advances in high-speed ADCs make this possible. Consequently, high-speed direct ADC presents an attractive means for high frequency and high bandwidth receivers. The WB ADC should improve on the performance of currently available ADCs.

Cost, performance, and reliability are the major factors driving development of the direct digital sampler. Evidence of design optimization of these parameters, as well as a comparison between model predictions and measured performance are expected. The direct Wideband (WB) Analog-to Digital Converter (ADC) should include filtering, as required, to eliminate spurious noise. Proposed technologies should highlight innovation in the areas of frequency bandwidth, downconversion methods, SWaP, cost, reliability, and sustainability.

A successful implementation of Wideband ADC for radar should reduce the cost and complexity of radar systems.

PHASE I: The company will define and develop a concept for a Direct Wideband Analog/Digital Converter (ADC) Digital Down converter that meets the requirements as stated in the topic description. The company will demonstrate the feasibility of the concept in meeting Army needs and will establish that the concept can be developed into a useful product. Material testing and analytical modeling will establish feasibility. The concept development effort should assess the importance of several factors, such as instantaneous bandwidth, dynamic range, and sampling rates. Evidence of design optimization of these parameters, as well as a comparison between model predictions and measured performance are required. Plans for implementing the Direct WB ADC will be included as an output of Phase I, along with estimated performance. The Direct WB ADC will initially be designed to operate at L band frequencies, but demonstration at higher bands will also be desired. Bandwidths on the order of 500 MHz or greater will also be demonstrated. Dynamic Range of the ADC should also be greater than 16 bit.

PHASE II: Based on the results of Phase I, the company will develop a prototype L-band Direct WB ADC, with a bandwidth of at least 500 MHz, for evaluation. The prototype will be evaluated to determine the capability in meeting performance goals and Army requirements. System performance will be demonstrated through prototype evaluation and modeling or analytical methods over the required range of parameters. Evaluation results will be used to refine the prototype into a design that will meet Army requirements. The system should include filtering as required to reduce potential alias input. Documentation should include analysis comparing sampling rates, bandwidths, analog downconversion, noise figure, calculation of data throughput and recommendations for data handling/reduction. The company will prepare a Phase III development plan to transition the technology to Army field use.

PHASE III DUAL USE APPLICATIONS: The company will support the Army in transitioning the technology for Army field use. The company will develop a Direct WB ADC/Digital Downconverter system according to the Phase III development plan for evaluation to determine its effectiveness in an operationally relevant environment. The company will support the Army for test and validation to certify and qualify the system for Army use and transition the Direct WB ADC to its intended platform. PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: Direct digital downconversion has application to the commercial radar market, as well as additional military applications. The proliferation of small solid-state radars for remote sensing and navigation benefits from cost-saving digital technologies that drive affordability and consequently expand the market even further. The commercial market is typically quick to adopt technology that enhances performance while controlling cost. The technology developed under this effort will facilitate a shift from expensive RF analog receiver circuitry to receivers based on commercial microprocessor technology. Even complex commercial radars such as weather radar can benefit from this technology, as digital processing is inherently scalable, allowing radars of various size and complexity to achieve improved performance at reduced cost.

REFERENCES:

1. Skolnik, M. RADAR Handbook. New York: McGraw-Hill 2008.

2. Tseng, Ching-Hsiang, Chou, Sun-Chung. "Direct Downconversion of Multiple RF signals Using Bandpass Sampling". IEEE Paper, 0-7803-7802, April, 2003.

KEYWORDS: Direct Digital to Analog, Radar

TECHNOLOGY AREA(S): Human Systems

OBJECTIVE: Develop a service-oriented architecture that permits the use of measures from unobtrusive COTS sensors, training data, and other measures of health and wellbeing to understand, manage, and optimize the wellbeing and performance of Army enlistees in an initial entry training environment.

DESCRIPTION: The proliferation of low cost fitness sensors provides opportunities for individuals to experiment with diet, exercise, and lifestyle to optimize key indicators of health. These devices typically link to a smartphone and allow individuals to log their own data and, at least in theory, track improvements in fitness over time. By tracking exercise, diet and fluid intake, sleep cycles, and other behaviors, users can create fitness plans and receive automatic notifications and incentives to follow that plan. Over time, it is expected that better and more diverse COTS sensors will be available and will potentially have utility for Army training environments.

Current commercial fitness devices provide feedback, goal monitoring, and a range of other services to the individual consumer (Sullivan & Lachman, 2016). While some devices have open standard protocols, others are closed proprietary systems (Gay & Leijdekker, 2015; Nijeweme-d'Hollosy, van Velsen, Huygens, & Hermens, 2015).

Research into strategies to change fitness behavior has shown that factors such as goal setting, feedback and rewards, coaching, and social factors are all potential avenues for effecting change. Commercial fitness trackers typically employ several of these strategies to facilitate increases healthy behavior; however, research into the benefit of fitness devices is still very new and the evidence supporting their effectiveness is somewhat mixed (Sullivan & Lachman, 2016).

Perhaps in part because privacy concerns, there is not currently a market for services that aggregate fitness data across individuals with the goal of managing health and fitness at a group level. While the individual fitness market may not support this need, a training organization like the Army could potentially reap a huge benefit from this capability. Managing the physical fitness of service members has always been a core Army mission that is integral to unit readiness.

The management of fitness, health, wellness, and training performance is no more important than in the basic training environment, one that is unique in a Soldier’s career. During basic training, enlistees eat, sleep, train, and live under the close supervision of their leaders on an Army post. Trainee data from fitness sensors, training events, and other behavioral and psychological measures are critical for instructors and leaders overseeing this key period of training. What is needed is an architecture to easily collect and aggregate that data across groups, to analyze, visualize, and understand it, and to effectively use it to manage outcomes by providing tailored feedback to each individual (TRADOC PAM 525-8-2).

For example, suppose data revealed that consumption of high-fat foods, poor sleep patterns, and long heart rate recovery times following physical activity predict a higher likelihood of a failing Army Physical Fitness Test (APFT) score. Once this relationship is discovered, a number of interventions would be possible to behaviorally alter some of the predictors using proven methods such as reward, coaching, and goal-setting. Over time, machine learning techniques could be applied to identify which strategies are most effective at attenuating this risk.

Proposals should describe your approach for designing and developing an open-standard, service oriented architecture for aggregating data from COTS sensors, training events, and other measures of health and wellbeing, and providing access to those data by tools to mine the data to discover associations among measures, and tools for designing, delivering and evaluating interventions to attempt to accentuate positive outcomes and attenuate negative outcomes. The system should provide a plug and play capability both for input devices and for analytical and intervention tools. Finally, the system should provide protocols to facilitate things like data entry, quality control and security.

PHASE I: Determine the feasibility/approach for developing an open standard architecture for aggregating data across individuals from COTS sensors, training events, etc., so that they can be used by trainees, instructors, and leaders for understanding the relationships among measures and for designing and evaluating interventions such as personalized get-well plans.

Work in this phase should include a user needs analysis to become familiar with the basic training environment and the instructors, leaders, and course managers who are involved in delivering the training. The government will insure access to the necessary user groups for this analysis. This analysis will also help the vendor to improve strategies for reducing technical risk.

The phase 1 deliverable will be a design to establish the technical merit, feasibility, and commercial potential of the proposed R&D effort. The design and associated feasibility analysis should demonstrate support for the following capabilities:

1. Open standard service oriented architecture: The core architecture should enable the collection and storage of sensor and other data into a non-proprietary, open-standard format such as the experience application programming interface (xAPI) standard in use by the DoD. Additionally, the core architecture should enable third-party developers to create a variety of tools for data collection, analysis, and visualization as well as tools for developing, creating, and evaluating interventions for unit members.

2. Data collection tools and processes: An important goal of the research in the SBIR is to identify a set of potential measures and to analyze the feasibility of collecting those measures using available COTS devices. Measures may include those typically found on fitness devices as well as psychometric measures and other verbal report measures that might be collected on a mobile device. Finally a means of incorporating key training performance metrics will need to be evaluated.

3. Data mining tools and processes: Users will not have a background in data analysis and so tools need to be developed that automatically analyze and present data using visualizations that are intuitive and that address the questions that those users are most likely to have. The user needs analysis will be critical in determining the user requirements/use cases for the proposed data mining tool(s).

4. Intervention tools and processes: When relationships are found that predict good or poor outcomes (e.g., improved/worsening PT scores), intervention tools will be needed to implement behavioral modification programs to improve the likelihood of desired outcomes. Interventions should be based on proven methods of behavioral change and should also automatically assess their effectiveness. For this proposal the vendor should focus on the following outcomes: PT scores and Record Fire scores. Desirable outcomes would be improvements in performance.

5. Data integrity: Processes, technologies, and tools are needed to insure data integrity. Data integrity may be compromised by a range of issues including faulty sensors and human error. Detection and correction of data errors is an essential capability and the feasibility analysis should address how to best mitigate errors in data sets.

6. Ability to function in a training environment: The basic training environment includes everything from classroom training to field training. Training sites may have limited or no access to cellular networks and/or power supplies (for re-charging batteries). Trainees crawl, walk, and run through various types of terrain in all manner of weather at daytime and night. The analysis and design solution should address any consequences or limitations created by the training environment.

7. Intuitive user interface: As already mentioned, the user needs analysis should feed the design of the user interface. The technology solution will succeed or fail based on the design of the user interface. A system that adds to instructor workload will not be accepted by users. The user interface must insure that the benefit of the system far outweighs the cost from the user point of view.

PHASE II: This phase will consist of the development, demonstration, and delivery of a working prototype. It is expected that an iterative design and development of components of the system will be needed. To insure good acceptance by the user community, the government will insure that the necessary users are available for evaluation of prototype interfaces etc. The Army’s IRB will need to approve any human subjects research. To facilitate approval, no PII needs to be collected for the demonstration.

Determining the potential for this system to be commercially viable requires that the system’s ability to deliver the seven capabilities described above (see phase 1) be adequately demonstrated. In this phase the vendor will have to provide a plan for demonstrating each of these capabilities along with criteria for success or failure for approval by the government. Given the time frame, it will probably not be possible to demonstrate the effectiveness of interventions. The “operational” environment in this case is the basic training environment. Participants will be available as needed for this demonstration.

Phase II deliverables include full system design and specifications to include executable and source code. It is expected that the final deliverable will be at a technology readiness level (TRL) 6 (System/ subsystem model or prototype demonstration in a relevant environment). As this prototype is a software architecture utilizing COTS hardware, achieving TRL 6 demonstration should be feasible.

PHASE III DUAL USE APPLICATIONS: Follow on activities are expected to be aggressively pursued by the offeror to seek opportunities to integrate the hardware, software, and protocols into Army personnel and training management systems. Commercial benefits include applications of the same capability in private businesses that have wellness programs for their employees as well as to expand and apply these capabilities outside of the basic training environment in the Army.

REFERENCES:

1. Gay, V., & Leijdekker, P. (2015, Nov). Bringing health and fitness data together for connected health care: Mobile apps as enablers of interoperability. Journal of Medical Internet Research, 17(11). Doi 10.2196/jmir.5094. Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4704968/

2. Nijeweme-d'Hollosy, W.O. van Velsen, L., Huygens, M., & Hermens, H. (2015). Requirements for and barriers towards interoperable eHealth technology in primary care. IEEE Internet Computing;19(4),10–19. doi: 10.1109/MIC.2015.53.

3. Sullivan, A.N., & Lachman, M.E. (2016). Behavior change with fitness technology in sedentary adults: A review of the evidence for increasing physical activity. Frontiers in Pulblic Health, 4, 1-16. doi: 10.3389/fpubh.2016.00289. Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5225122/pdf/fpubh-04-00289.pdf

4. U.S. Army (2017). The U.S. Army Learning Concept for Training and Education: 2020-2040. TRADOC Pamphlet 525-8-2. Retrieved from: http://www.tradoc.army.mil/tpubs/pams/tp525-8-2.pdf

KEYWORDS: Data analytics, data visualization, data mining, machine learning, basic combat training, fitness tracking, comprehensive soldier fitness

TECHNOLOGY AREA(S): Materials/Processes

OBJECTIVE: Development of low cost casting methodology (or significant improvement of existing casting methods) for the production of magnesium alloys with significantly improved microstructural and mechanical properties/performance

DESCRIPTION: Due to their low density and high specific properties, magnesium (Mg) alloys are often considered for applications in which weight savings are an important selection factor. Typically, the alloys used in these situations are in the wrought condition, as they display significantly higher strengths relative to cast alloys. However, there are numerous components on US Army platforms that would benefit from improved performance and weight savings of higher strength cast Mg alloys (transmission casings, structural members, non load bearing part, etc.). Despite these potential opportunities, the use of as-cast magnesium alloys is still hampered by their lower strengths in the as processed condition despite the advent of alloys that contain an appreciable amount of precipitates (such as the LPSO containing Mg-Y-Zn and Mg-Al-Gd alloys and/or rare-earth containing alloys, e.g., Mg-Gd-X-Y). Oftentimes, the precipitates in these alloys suffer from a lack of homogeneous and uniform distribution in the matrix. In addition, in some cases, the precipitates can actually reduce strength properties as they serve to nucleate cracks during loading (e.g., Mn-rich precipitates). Thus, in order for Mg castings to become more widely accepted/used in the as-cast state, it is imperative that the strength and ductility of these materials be significantly improved over current high strength cast alloys (e.g., WE43, Mg-Gd(Y)-Zn, etc.). (An approximate 20% or more improvement in tensile performance over average values for high strength Mg alloy castings of ~375 MPa and 5-7% elongation is desired.)

In an attempt to overcome the above limitation, the US Army is interested in the development of a low cost, highly robust (e.g., consistent) casting method that will produce magnesium alloys with significant property improvements over current methods. In addition to defense related applications, the development of higher strength Mg alloys would readily find applications in automotive, aerospace, and other industries currently faced with increasing demand for higher strength, lighter weight materials solutions.

It is desired that this method will use readily available elemental additions (e.g., four 9s purity or better) – and the method may or may not utilize the application of electromagnetic fields. The alloy should contain a significantly refined grain structure with uniform distribution of precipitates which could be coherent, semi-coherent, or incoherent with the matrix. The precipitates may either form through in-situ reactions during the casting process or may be added (for example, nano-oxides) during the melting/stirring/casting process. Furthermore, it is desired that the precipitates should be present over a relatively broad size range (from nanosized oxide particles to 10-20 micron precipitates) in order to maximize strengthening effects. Modifications of existing Mg alloy compositions (such as well known AZ or ZK series) as well as novel ones developed specifically for this topic may be used. For further insight on potential reinforcements, the reader is referred to the series of papers by JF Nie (Monash University) on the desired combination of precipitate size, shape and distribution in Mg alloys. Those interested in the use of oxide nanoparticles are referred to the work by M Gupta (National University of Singapore).

PHASE I: Select a standard (baseline) commercially available alloy chemistry and develop an improved fabrication methodology for successful ingot melting and casting. In addition to delivering sample materials which demonstrate the fidelity of the methodology, quantify the alloy material according the following requirements:

- Deliver one (1) ingot casting with dimensions of 7 inches x 7 inches x 4 inch.

-Downselect a second Mg-based alloy composition, provide the reasoning for its selection (e.g., ease of fabrication, cost of raw materials, strengthening mechanisms, etc.) and identify the relevant processing protocols for the successful fabrication.

PHASE II: Demonstrate feasibility of scaling the fabrication methodology, identified and developed in Phase I for both the baseline and second alloy compositions. Furthermore, demonstrate repeatability of the process and construct an up-scaled pilot-scale facility that is suitable for batch, semi-continuous or continuous production of alloy material. In addition to delivering sample materials which demonstrate the fidelity of the methodology, quantify larger-scale alloy materials according the following requirements:

- Construct a pilot-scale melting and casting system, capable of producing batch-mode and semi-continuous castings, and develop a manufacturing operations and commercialization plan