Air force 16. 1 Small Business Innovation Research (sbir) Proposal Submission Instructions



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PHASE II: Fabricate a prototype WHTS. Develop a test plan. Evaluate the prototype in a laboratory environment. Demonstrate WHTS mechanical and electrical interfaces for integration into the BAO Kit. Provide special test equipment, support operator testing, and refine prototype performance based on feedback. Deliver prototype WHTS optimized for SWaP performance, reliability, and ruggedization consistent with dismounted warfighter operations. Create a roadmap to mature the technology.

PHASE III DUAL USE APPLICATIONS: Develop WHTS pre-production product and integrate with the BAO Kit and an HMD that requires head-tracking information. Provide a pre-production WHTS bill of materials. By the end of Phase III, the WHTS should be capable of all-weather operation worldwide. Develop commercial applications.

REFERENCES:

1. Fact Sheets: (a) Combat Controllers (18 Aug 2010); (b) Guardian Angel (18 Mar 2013). Available at http://www.af.mil/AboutUs/FactSheets.

2. DARPA Chip-Scale Combinatorial Atomic Navigation (C-SCAN) Program: (a) NEW SENSOR SOUGHT TO ENABLE MILITARY MISSIONS IN GPS-DENIED AREAS (16 Apr 2012); (b) DARPA-Funded Atomic Clock Sets Record for Stability (29 Aug 2013). Available at www.darpa.mil.

3. Computer vision research, technologies, and applications: (a) review available at “Computer vision,” http://en.wikipedia.org/wiki/Computer_vision (accessed 20 Mar 2014); (b) David C. Roberts, Stephen Snarski, Todd Sherrill, et al., "Soldier-worn augmented reality system for tactical icon.visualization", Proc. SPIE 8383, paper 838305 (2012); © David C. Roberts, Alberico Menozzi, James Cook, et al., "Testing and evaluation of a wearable augmented reality system for natural outdoor environments," Proc. SPIE 8735, paper 87350A (2013); and (d) Visual Navigation, Ed. Yiannis Aloimonos, 432 pages (Psychology Press, 2013).

4. Review of Quick Response Code (QR code) research, technologies, and applications is available at “QR code,” http://en.wikipedia.org/wiki/QR_code (accessed 20 Mar 2014).

5. D. N. Jarrett, Cockpit Engineering, 410 pp (2005); (b) Fred F. Mulholland, "Helmet-mounted display accuracy in the aircraft cockpit," Proc. SPIE 4711, pp 145- (2002).

KEYWORDS: Wearable Head Tracker System, WHTS, digital vision, head-up display, see-through display, night vision goggles, dismounted operators, Battlefield Air Operations, BAO, Special Operations Forces





AF161-041

TITLE: Software Architecture Evaluation Tool for Evaluating Offeror Proposals

TECHNOLOGY AREA(S): Information Systems

OBJECTIVE: Develop, validate and demonstrate a tool to analyze software architecture to understand propagation cost and core size of the software. Such insights will enable acquisition managers to mitigate risk and improve financial and operational performance.

DESCRIPTION: Members of the acquisitions corps must often make source selection decisions, sometimes for multi-decade, multi-billion dollar systems, based solely on written information provided by offerors about their software (in this context software is weapon systems agnostic, could be for avionics, IT, simulators, etc.). For example, many offerors assert their software to be easily sustainable because it employs an open or modular architecture. However, there is no easy way for these assertions to be verified. Consequently, this current method of software analysis and selection is highly fallible because there is no means to validate the veracity of the offeror’s claims or to identify potential problems within software.

A growing body of research demonstrates that a software code base is analyzable without the burden of a priori operational knowledge of the entire code base[1–3]. At the most basic form, this method employs a commercial-off-the-shelf (COTS) software call extractor to identify the dependencies within lines of code, which are then arranged in an adjacency matrix or a design structure matrix for further analysis. Once direct dependencies are identified within a matrix, indirect dependencies are calculated through matrix multiplication until full transitive closure of dependencies is understood. This fully extended matrix is then clustered according to a matrix partitioning algorithm. In this final form of the software architecture, it is possible to understand both the propagation cost and core size of the examined software[4]. Research has shown that these two software metrics can shed light on the cost of long-term sustainability of the software (in terms of both cost to sustain and time) and the veracity of the offeror’s original architecture claims.

PHASE I: Develop a prototype software tool capable of analyzing software architecture to: understand both the propagation cost and core size of the software; provide insights to the sustainability cost; and enable improvements to risk mitigation, financial planning and operational performance. Demonstrate viability by performing an analysis on an open-source Department of Defense code bases.

PHASE II: Finalize and validate the software tool developed in Phase I and demonstrate using an Air Force provided code base. Demonstrate the finalized solution meets security requirements for fielding on the Department of Defense’s Non-secure Internet Protocol Router (NIPR) network.

PHASE III DUAL USE APPLICATIONS: A licensable software tool or package capable of being fielded and operated throughout Air Force Materiel Command. This is a dual-use technology with applications to both military and commercial software.

REFERENCES:

1. A. MacCormack, J. Rusnak, and C. Y. Baldwin, “Exploring the Structure of Complex Software Designs: An Empirical Study of Open Source and Proprietary Code,” Management Science, vol. 52, no. 7, pp. 1015–1030, Jul. 2006.

2. C. Y. Baldwin, A. MacCormack, and J. Rusnak, “Hidden Structure: Using Network Methods to Map System Architecture,” Harvard Business School Working Paper Series 13-093, Oct. 2014.

3. A. MacCormack, J. Rusnak, and C. Y. Baldwin, “The Impact of Component Modularity on Design Evolution: Evidence from the Software Industry,” Harvard Business School Working Paper Series 08-038, 2007.

4. D. J. Sturtevant, “System Design and the Cost of Architectural Complexity,” Massachusetts Institute of Technology PhD Thesis, 2013.

KEYWORDS: software, architecture, acquisitions, source selection





AF161-042

TITLE: Simplified Aero Model Development and Validation Environment

TECHNOLOGY AREA(S): Human Systems

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 5.4.c.(8) of the solicitation and within the AF Component-specific instructions. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. Please direct questions to the AF SBIR/STTR Contracting Officer, Ms. Gail Nyikon, gail.nyikon@us.af.mil.

OBJECTIVE: Create a high-fidelity, instrumented environment for rapidly developing and verifying models to improve simulator fidelity.

DESCRIPTION: Increasing simulator fidelity, coupled with advancements in networking technology, has enabled more aircrew training objectives to be “offloaded” from having to be performed on aircraft and onto simulators. One area where these advances have the potential to generate a large cost savings is in the area of aerial refueling as an example development case.

Today aircrews are trained to perform aerial refueling using real aircraft. This continues to be very expensive as a way to do this kind of training. In the live aircraft training, both a tanker and a receiver aircraft must be scheduled along with their crews. This large commitment of resources is expensive and requires close coordination between different organizations in order to maximally utilize the air time. Classically, there are two ways to train aircrew. The first method is to provide a highly realistic environment where the aircrew member. The aircrew member learns though repeat exposure to the realistic environment while performing the task. For on aircraft aerial refueling training, this is typically done through the use of repeated dry hook ups and actual refueling events. A second method for improving aircrew performance is to provide training in a manner that is more stressing yet analogous to the actual task. An example of this type of training is strength conditioning for runners where they carry additional weight on their backs during training. This makes the training somewhat unrealistic (and more difficult), yet yields performance benefits during an actual race. Anecdotally, C-17 aerial refueling training in the legacy Air Refueling Procedure Part Task Trainer was akin to this method.

Aircrews did not find the training device realistic but they found that it developed aircrew skills in such a way that made actual aerial refueling seem less difficult.

A virtual aerial refueling (VAR) capability has the potential to greatly reduce cost and increase training opportunities. Simulators are at least an order of magnitude less expensive than aircraft to operate. Aircrew can be sourced from across the network to accomplish training, removing the need to geographically meet as is needed for on aircraft refueling training. However one of the biggest hurdles in moving to a virtual training capability is the relative fidelity of the models of the aircraft in the simulation used for the training. Today the aero models for all the players must be developed and validated using the real aircraft for this as well. One key reason today’s simulation environments are not used more directly for this model development for training is that the fidelity of the simulations as a mechanism for such developing and validating models is not sufficient. Further, they are not instrumented in a way to make this model development and validation more accurate, expeditious, and cost effective compared to real aircraft validation.

This effort will develop a high-fidelity, instrumented environment for aero model development and validation in order to improve performance on a specific task, such as aerial refueling. Providing a highly realistic virtual environment is what is currently being pursued in the virtual world to support VAR training. Developing a less expensive aero model development capability is preferred. However, all options should be considered for their efficacy and cost effectiveness.

PHASE I: Identify and evaluate alternative approaches to non-live aircraft aero model development, improvement and validation. Based on the evaluation develop a design specification for a model development environment and develop an initial environment exemplar for a proof-of-concept demonstration.

PHASE II: Extend and validate the exemplar and conduct iterative model development and validation demonstrations. Integrate and evaluate models developed in the exemplar environment into actual simulation environments and conduct training and model effectiveness evaluations and comparative evaluations of models with existing flight test data.

PHASE III DUAL USE APPLICATIONS: This effort will further refined the exemplar and will provide a prototype to an operationally relevant domain for extensive test and evaluation. The results of this effort will provide a commercialized solution to eliminate the necessity for aeromodel development and validation.

REFERENCES:

1. Business Case Analysis: MAF Distributed Mission Operations: C-17/KC-10/KC-135 (Revision 2.0), Mar 2012.

2. MAF DMO Aerial Refueling Study (MARS) Phase 1 Final Report, 17 Sep 2012.

KEYWORDS: aero model verification and validation, aerial refueling, simulators, fidelity, training devices, model development environments





AF161-043

TITLE: PED Operational Domain (POD)

TECHNOLOGY AREA(S): Human Systems

OBJECTIVE: Leverage technologies to create efficiencies in Phase I processing, exploitation, and dissemination (PED) of full-motion video (FMV). Increase PED capabilities at current manning or maintain capabilities with less manning.

DESCRIPTION: Aligned with AFSOC’s PED 2020 vision and roadmap, AFSOC would like to engage with research and development organizations to develop prototypes and/or production systems to improve the human performance of FMV analysts. Today’s technology landscape is providing enhanced capabilities in touch screen, voice recognition, human gesture control, object recognition, data fusion, and collaboration. An analyst surrounded by these technologies can take charge of their environment by focusing on high priority analytical reasoning tasks and allow the system to assist them in tasks that are routine and secondary to the primary task of analysis. Systems within the intelligence community are increasingly providing more and more data for analysis.

Innovative selection and integration of technologies can create efficiencies within the PED Phase 1 process. Most of the technologies are readily available but lack integration into a human to computer environment. It’s operationally sound to develop an integrated human to computer environment and pursue additional technologies that will enhance our PED integrated capabilities. All candidates should consider all layers of the Open Systems Interconnection (OSI) model. In laymen’s terms, consider each component of the solution from the hardware, cabling, network, systems, communications, ergonomics, and security when designing and building this capability. Components should be state of the art, leading edge. While considering current fielded capabilities, the POD should be an open architecture “build to” environment allowing for use of current and future plug and play systems and applications.

Each POD should have a suite of collaboration tools. These tools should allow for communications with RPA sensor operators and forward ITC analysts. Collaboration would be in the form of radio communications, telestration, virtual presence, chat, groupware, and electronic meetings.

The goal of this POD would be to improve processes and reduce the number of tasks performed by human intervention and enable rapid correlation/manipulation of data for decision making.

Example Use Case: With their eyes analysts can change focus to the screen or window of interest and with their hands move and manage the workspace. Voice commands will optimize and expedite processes by executing activities normally done from a keyboard or mouse. An example would be verbalizing callouts without having to type or make log entries. Each analyst should sit in their own POD surrounded by easily assessable technologies to improve time-in-motion and situation awareness.

PHASE I: Gather POD requirements from AFSOC and develop a POD systems design with schematics, drawings, and specifications. Work with AFSOC to develop use cases on how the analyst would interact with the POD when performing routine computer tasks such as logins, applications launching, screen management, etc. Provide an estimated Phase II schedule and/or project plan.

PHASE II: Develop an initial prototype system or subsystem to demonstrate the human to computer interface capabilities of the POD using a targeted set of FMV and office applications. Technical assessment will be performed by AFSOC. Provide an estimated Phase III schedule and/or project plan.

PHASE III DUAL USE APPLICATIONS: Transition the POD to an AFSOC FMV PED environment. The POD has the potential to evolve into an Intelligence, Surveillance, and Recognizance (ISR) cockpit for use beyond Phase I PED, as well as by other intelligence analysts within the Joint Forces community.

REFERENCES:

1. AFI 14-135, 22 May 2014, Intelligence.es.

2. FPD 14-2, Intelligence Rules and Procedure.

3. AFSOCCI 14-2 DCGS-SOF Volume 1-3, Intelligence.

4. Open Systems Interconnection Model, ISO/IEC 7498-1.

5. Walker, Geoff (August 2012). "A review of technologies for sensing contact location on the surface of a display." Journal of the Society for Information Display.

KEYWORDS: computer, video, FMV, screen, voice, collaboration, biometrics, telestration, chat, analyst, ITC, PED, ISR, cockpit, COTS, systems, network, cyber, touch screen, voice control, voice recognition, OSI Model, virtual presence, groupware, MAAS, AF-DCGS





AF161-044

TITLE: Finite Element Model of the F-35 Ejection Seat

TECHNOLOGY AREA(S): Human Systems

OBJECTIVE: Development of a finite element (FE) computer model of an F-35 ejection seat with human occupant for prediction of spinal injury risk to the full range of pilots during a wide range of ejection conditions.

DESCRIPTION: The F-35 aircraft has a wide range of capabilities and an expanded flight envelop which could lead to pilots ejecting under extreme conditions. The US16E ejection seat has been selected for the F-35, and has undergone extensive rocket sled testing with instrumented manikins. However, conducting rocket sled qualification testing of ejection seats with instrumented manikins cannot accurately simulate human response in some critical areas, such as pre-ejection bracing and neck flexion. Also, current spinal injury models were designed for legacy ejection seats and current neck injury criteria are based on automotive injury curves, so these also do not provide adequate prediction of ejection injury under conditions that will be experienced by the F-35. Therefore, a validated FE model of a human occupant in an actual ejection seat is needed to address these deficiencies since this model could provide simulations of various ejection conditions not simulated by manikin rocket sled qualification tests, and not predicted by current injury criteria. Research has narrowed the factors that increase injury rates to a few primary causes; however, due to limited resources, the dependencies and coupling of the factors are unknown. FE modeling has potential to provide insight into this complex problem by identifying the critical variables in the F-35 seat that contribute to increased injury rates under a wide range of ejection conditions that are not addressed in qualification tests.

The proposed effort will focus on development of a Finite Element computer model using off-the-shelf, non-proprietary modeling software (e.g., LS Dyna), that can simulate the dynamic response of a human crewmember ejecting in the F-35 ejection seat. The model should incorporate small female (approximately 103 lbs), mid-male (approximately 170 lbs), and large male (approximately 245 lbs) variations. The Air Force will provide biodynamic response data collected from both human and instrumented manikin tests conducted on a Vertical Deceleration Tower (VDT) and Horizontal Impulse Accelerator (HIA) in an actual US16E ejection seat which can be used to validate methodologies, as well as code from human response and ejection models originally developed by AFRL (e.g., ATB, Easy5) for legacy platforms. The model development will also include a graphical user interface to allow the user to set the initial seat and environmental parameters of a simulated ejection.

PHASE I: Design the concept of developing a FE computer model and graphical user interface capable of simulating the dynamic response of a small female, mid-male, and large male pilot ejecting from an F-35 aircraft. Give rationale for selection of the modeling software and identify what is needed to validate the model. Provide an illustration of the proposed graphical user interface.

PHASE II: Develop the computer model and graphical user interface (GUI) described in Phase I. Demonstrate with simulations how the model can simulate the dynamic response of a small female, mid-male, and large male during an F-35 ejection. Show how the GUI is used to set the pre-ejection parameters (e.g., restraint tensions, headrest position, seat cushion type, etc.). Provide details on software structure, file architecture, model run times, system requirements, and licensing.

PHASE III DUAL USE APPLICATIONS: Validate the computer model with injury statistics of ejections with similar seats provided by the Air Force, and conduct a comparison to F-35 rocket sled tests conducted with instrumented manikins. Identify practical areas of model customization not identified by GUI parameters.

REFERENCES:

1. Lewis M. Survivability and Injuries from Use of Rocket-Assisted Ejection Seats: Analysis of 232 Cases. Aviation, Space, and Environmental Medicine, 77(9), Sep 2006.

2. Brinkley J.W., 1985, Acceleration Exposure Limits for Escape System Advanced Development, SAFE Journal, Vol. 25, No.2, 1985.

3. Nichols J.P. Overview of Ejection Neck Injury Criteria, Proceedings of the 44th Annual SAFE Symposium, pp. 159-171, Reno NV, Oct 2006.

4. Doczy E., Buhrman J., and Mosher S. The Effects of Variable Helmet Weight and Subject Bracing on Neck Loading During Frontal -Gx Impact, Proceedings of the 43rd Annual SAFE Symposium, Salt Lake City UT, Sep 2004.

5. Cheng H., Mosher S.E., and Buhrman J.R. Development and Use of the Biodynamics Data Bank and its Web Interface. Armstrong Laboratory Technical Report AFRL-HE-WP-TR-2004-0147, Oct 2004.

KEYWORDS: FE model, GUI, spinal injury, F-35 seat, aircraft ejection, human response



AF161-045

TITLE: Information Fusion to Enable Shared Perception between Humans and Machines

TECHNOLOGY AREA(S): Human Systems

OBJECTIVE: Establish an approach to interface design that incorporates how humans fuse information in order to create shared perception and shared understanding between humans and machines.

DESCRIPTION: In autonomous systems, humans and machines require common understanding and shared perception to maximize benefits of the human-machine team. In data fusion systems, machines are relied upon to integrate information from multiple sources and time periods through various statistical and mathematical techniques to obtain meaningful information. They rely on algorithms that assess and associate different pieces of data based on the likelihood that they represent the same or related events, people, and objects[1]. In the fusion process, one of the human’s main roles is to perform as a hybrid computer, supporting automated reasoning techniques by using visual and aural pattern recognition and semantic reasoning[2]. A complete data fusion system consists of computers that combine information from multiple sources, an interface to present the combined information along with additional information, and a human who must perceive, encode, and interpret the information to make a decision. To optimize this human-machine team, the machine should represent the information in an optimal way to enable the human to form associations, reason, and make effective decisions. An approach is needed for interface design that incorporates the understanding of how both machines and humans fuse information in a way that allows the human and machine to form a shared perception and shared understanding of the environment.

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