Department of the navy (don) 18. 1 Small Business Innovation Research (sbir) Proposal Submission Instructions introduction

REFERENCES:

1. Henniger, H. & Wilfert, O. “An Introduction to Free-space Optical Communications.” RadioEngineering, Vol. 19, No. 2, June 2010. https://www.radioeng.cz/fulltexts/2010/10_02_203_212.pdf

2. Sullivan, M. “Synopsis of: Risley Prism Beam Pointer.” Lockheed Martin Space Systems, November 13, 2006.https://wp.optics.arizona.edu/optomech/wp-content/uploads/sites/53/2016/10/SullivanReport1.pdf

KEYWORDS: Laser Communication; Free Space Optical (FSO); Risley Prism; Low Cost Low SWaP Lasers; RF Denied Communications; Low Probability of Interception and Detection (LPI/LPD) Communication; No-RF Tactical Communications

N181-028

TITLE: Precision Machining of Composite Structures

TECHNOLOGY AREA(S): Air Platform, Materials/Processes

ACQUISITION PROGRAM: PMA 261 H-53 Heavy Lift Helicopters

OBJECTIVE: Develop an innovative machining process that can effectively and precisely machine holes in composite structures while preventing induced damage.

DESCRIPTION: Fiber reinforced polymer (FRP) composites are a key enabling material in several U.S. military aircraft. Composite materials are used in primary load bearing structures, as well as secondary non-load bearing structures and skins. The size and complexity of composite components is constantly increasing as the desire for reduced weight drives the replacement of metallic components with low-density FRP.

FRP materials are currently being machined using techniques adapted from traditional metalworking, however the unique material properties of FRPs present several difficulties in the drilling of a simple fastener hole, of which there may be several dozen on a single aircraft component. Additionally, fastener holes often require precision countersinks. The highly abrasive nature of carbon, glass, and aramid fibers reduces tool life of traditional tungsten carbide drill bits, necessitating their frequent changing, and also affecting hole diameter as the drill bit is abraded by the material. The frictional heat generated by the drill bit can cause severe damage to the polymer matrix, resulting in a loss of strength that can be extremely difficult to detect. Lastly, FRP materials are prone to delamination in several situations due to improper drilling technique.

The Navy needs a tool to create a finished precision fastener hole with countersink using an innovative precision machining technique. The technique should provide precise replication of high-quality holes with high placement accuracy while reducing the amount of consumable tooling required, such as drill bits, when compared to traditional machining techniques through the same composite material. This precision fastener hole and countersink machining process should remove material without inducing damage to, or contamination of, the actual part. Precision within the specified hole diameter +0.006 in max, having a surface roughness height rating of 250 or less, and no breakout plies on the exit side. For thickness greater than 0.100 in, no delamination 0.010 in deep from edge of hole or into the part from hole. No splintering allowed beyond 0.010 in deep at entrance/exit of hole. For placement, the precision machining technique does not require a pilot hole be present to maintain dimensional accuracy. Automation can be leveraged to increase hole and countersink precision and placement. Careful control of any applied or induced heat must be demonstrated to not cause damage to the composite material. The temperature limit at the location of the final diameter size should not exceed 50° F below the glass transition temperature for the composite material during the machining operation.

PHASE I: Develop an innovative approach for a precision machining tool to machine fastener holes of relevant diameter and depth in either a carbon fiber or glass- based composite material with polymer reinforcement representative of those materials used in military aircraft today. Demonstrate feasibility of the developed approach for producing holes in selected composite material and model the temperature profile of the resulting fastener holes using commercially available analytical tools. The Phase I effort will include the development of prototype plans for Phase II.

PHASE II: Fully develop a prototype precision machining tool; demonstrate the precision fastener hole capability developed in Phase I; and expand the capability to include countersink in a laminate that contains both carbon and glass fibers. Demonstrate the ability to machine a finished precision fastener hole in a 1” thick composite sandwich structure with relevant face sheet and core materials. Validate the quality of the hole with traditional Non-Destructive Inspection (NDI) techniques and show that the quality is at least equivalent to that which is currently achievable with traditional drilling through similarly produced composite material. Validate the predicted heat distribution of the material and the associated material properties around the hole experimentally.

PHASE III DUAL USE APPLICATIONS: Benchmark the precision machining system to machine and countersink fastener holes in composite structures for aircraft components. Transition the technology to provide an efficient and effective tool to produce countersunk fastener holes in carbon and glass fiber laminate composite materials used for military air platforms, as well as civilian air vehicle components and other industrial applications. The technology can be an effective and efficient machining and cutting tool for various components in both the military and commercial sectors such as aerospace, automobile, and marine.

REFERENCES:

1. El-Sonbaty, I., Khashaba, U. & Machaly, T. “Factors affecting the machinability of GFR/epoxy composites.” Composite structures 2004, 63, no. 3: 329-338. http://journals.sagepub.com/doi/abs/10.1177/0021998312451609

2. Li, Z. L. Zheng, H. Lim, G. Chu, P. & Li, L. “Study on UV laser machining quality of carbon fibre reinforced composites.” Composites Part A: Applied Science and Manufacturing 2010, 41, no. 10: 1403-1408. http://laser.mace.manchester.ac.uk/uploads/tx_neofileshare/2011-10-20_15-46-59_UV_Laser_machining.pdf

3. Piquet, R. Ferret, B. Lachaud, F. & Swider, P. “Experimental analysis of drilling damage in thin carbon/epoxy plate using special drills.” Composites Part A: Applied Science and Manufacturing 2000, 31, no. 10: 1107-1115. http://www.sciencedirect.com/science/article/pii/S1359835X00000695

KEYWORDS: Composite Structure; Drilling; Temperature Profile; Precision Machining; Heat Distribution; Fastener Hole

N181-029

TITLE: Maritime Target Automatic Target Recognition from Inverse Synthetic Aperture Radar (ISAR) Utilizing Machine Learning

TECHNOLOGY AREA(S): Weapons

ACQUISITION PROGRAM: PMA 280 Tomahawk Weapons 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 Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

OBJECTIVE: Develop an innovative automatic target recognition (ATR) system that leverages state-of-the-art machine learning technology to automatically find and extract a ship’s salient features from its inverse synthetic aperture radar (ISAR) images for high-speed weapons applications.

DESCRIPTION: Despite significant DoD investments in radar target recognition over the past 30 years, very few radar automatic target recognition (ATR) systems have transitioned into widespread use in weapon applications. The reasons are many including: (1) they are too computationally complex for weapon platforms (e.g., template matching); (2) legacy ATR algorithms cannot achieve the required recognition update rate of 5Hz or higher; (3) poor false-alarm performance resulting in inadequacy to sort among a complex scene in time for correct target engagement while avoiding collateral damage to non-targets; and (4) they cannot or have difficulty in evolving to learn to recognize new targets. This requires significant time and data resources (e.g., significant amounts of measured training data and/or high-fidelity models). Due to the nature of the problem and the technology sought, significant in this context, data sets could vary from tens of thousands to hundreds of thousands. Another aspect of this problem is the need to determine the amount of data necessary to effectively train the algorithm for the task.

Recent results in machine learning, as applied to synthetic aperture radar (SAR) images of land targets, show that it is possible to reduce run-time storage and computational complexity of new recognition algorithms by 50- to 100-fold compared to conventional recognition algorithms. Deep learning algorithms have successfully been used to adaptively determine the salient features of a target by researchers. Furthermore, these salient features can be used to uniquely represent a target thereby increasing the utility of this capability.

The problem with current maritime ATR and maritime classification aide algorithms is the reliance on heuristic features, which are not sufficient for classifying vessels beyond using vessel length. Heuristic image processing techniques that attempt to extract features (e.g., superstructures) break down when the ISAR image quality degrades. The reasons for the degradation vary but include: low Signal to Interference plus Noise Ratio (SINR), poor viewing geometry, poor environmental conditions, and insufficient observation time. Moreover, as new data is observed in different operating conditions heuristic image processing techniques will require constant manual retraining to discover the required features. Furthermore, heuristic approaches require identification of the features a priori with development of the image processing technique necessary to extract the features. Finally, in choosing these features and developing the image processing technique first, the hardware is limited to relying on these features. Ultimately, we need to identify the other features that technology can leverage to differentiate similar vessels. By using advanced machine learning technology to develop an innovative approach that automatically finds the target’s salient features, we may find that we can differentiate similar vessels. The technology must be robust to deliver image quality without reliance on heuristic image processing approaches. The technology must also ensure that as the image quality degrades the algorithm can still function well to perform its classification task.

The end-product should be a weapon flight-tested hardware implementation of the ATR algorithms running in real-time on a low-power processor that meets the Government performance objectives. The algorithm design must be modular and adaptable to adding new ships and target classes on a frequent schedule. Once the core training is completed, that solution will be adequate for a physical ship design until the ship class is decommissioned. Assessment of the ATR algorithm will be a function of ship type, operational environment (e.g., sea state, wind condition, etc.), radar parameters (e.g., bandwidth, frequency, etc.), and ISAR image quality obtained from different missile trajectories.

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by DoD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Security Service (DSS). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this project as set forth by DSS and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advanced phases of this contract.

PHASE I: Leverage machine learning concepts and technology to automatically identify and extract key ship salient features from simulated and/or unclassified ISAR imagery and use these features to demonstrate robust automatic target recognition of the vessels. Assessment of the algorithms will be performed against simulated and test radar data to identify the performance expectation against the ISAR image quality obtained from a missile platform. Develop prototype plans for Phase II.

PHASE II: Further develop and demonstrate the performance of the prototype ATR system against collected radar data of maritime targets from applicable/relevant weapon radar seekers. Perform ATR performance assessment as a function of ship type, operational environment (e.g., sea state, wind condition, etc.), radar parameters (e.g., bandwidth, frequency, etc.), and ISAR image quality obtained from different missile trajectories. Once demonstrated, a key task is to develop real-time embedded software code of the ATR system and map the processing requirements for candidate processors selected by the PMA for flight testing demonstration.

It is probable that the work under this effort will be classified under Phase II (see Description section for details).

PHASE III DUAL USE APPLICATIONS: Finalize the hardware testing for the software code of the ATR system for the candidate processors. Support integration on available weapon hardware that will be ready for integration with software. The testing will include Hardware-in-the-Loop (HWIL) testing with synthetic target scene generation and a flight test to verify and validate performance. The ATR algorithm would be beneficial to the Coast Guard for maritime target recognition at range in addition to any other applications that would require target recognition using ISAR imaging in a maritime environment, including military targeting and sensor aircraft. This technology could also benefit those who need to track shipments and could provide improvements to facial recognition via algorithm discovery.

REFERENCES:

1. Michie D., Spiegelhalter, D., & Taylor C. (eds). Machine Learning: Neural and Statistical Classification, 1994. http://www1.maths.leeds.ac.uk/~charles/statlog/

2. Chen V. and Martorella M. Inverse Synthetic Aperture Radar Imaging: Principles, Algorithms, and Applications, 2014. https://books.google.com/books/about/Inverse_Synthetic_Aperture_Radar_Imaging.html?id=xWmABAAAQBAJ

3. Barth K., Bruggenwirth S., and Wagner S. “A Deep Learning SAR ATR System Using Regularization and Prioritized Classes.” IEEE Radar Conference, 2017. http://ieeexplore.ieee.org/document/7944307/

4. Li J., Mei X. and Prokhorov D. “Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene.” IEEE Transactions on Neural Networks and Learning Systems, Vol 28, Issue 3, March 2017. http://ieeexplore.ieee.org/document/7407673/

5. Ji K., Kang M., Leng X., et al. “Deep Convolutional Highway Unit Network for SAR Target Classification with Limited Labeled Training Data.” IEEE Geoscience and Remote Sensing Letters, Vol PP, Issue 99. http://ieeexplore.ieee.org/document/7926358/

6. Nguyen A., Xu J. and Yang Z. “A Bio-inspired Redundant Sensing Architecture.” 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain. https://papers.nips.cc/paper/6564-a-bio-inspired-redundant-sensing-architecture.pdf

7. Cheng Y., Lu W., Zhai S., et al. “Doubly Convolutional Neural Networks.” Advances in Neural Information Processing Systems 29 (NIPS 2016). http://papers.nips.cc/paper/6340-doubly-convolutional-neural-networks

KEYWORDS: ISAR; Automatic Target Recognition (ATR); Machine Learning; Maritime; Deep Learning; Image Processing

N181-030

TITLE: Compiler Monitor System (CMS-2Y) Software Language Operation in X86 Linux Computing Environments

TECHNOLOGY AREA(S): Battlespace, Electronics, Sensors

ACQUISITION PROGRAM: Program Executive Office Integrated Warfare System (PEO IWS) 1.0 – AEGIS Combat System

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 Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

OBJECTIVE: Develop a method for executing legacy Compiler Monitor System (CMS-2Y) tactical code on an X86 computer running the Linux operating system to validate critical software updates in the AEGIS Test Bed (ATB).

DESCRIPTION: CMS-2Y computer software language for tactical operations was developed for Fleet Computer Programming Center - Pacific (FCPCPAC) to support Naval Tactical Data Systems (NTDS) operations. The language continued to be developed, eventually supporting a number of combat system computers including the AN/UYK-43 which became the standard 32-bit computer of the United States Navy for surface ship and submarine platforms. AEGIS platforms have AN/UYK-43 units that are currently in service. The UYK-43 is being replaced by the AN/UYQ-70 and commercial off-the-shelf (COTS) systems on the majority of AEGIS Combat System platforms, with the exception of AEGIS Baselines (BL) 3.6, 5.3, and 5.4.

CMS-2Y is currently run on AEGIS BL3.6, BL5.3, and BL5.4. The current ATB resides on an X86 computer system that utilizes Linux operating systems. The ATB is a simulation environment that contains representative functions for the AEGIS Combat System (ACS) such as sensors, radars, digital data links, and weapons systems which comprise the ACS. CMS-2Y software language code is not compatible with the X86 COTS hardware and the Linux operating system which hosts the ATB. This incompatibility prevents these legacy baselines from being thoroughly tested in the current ATB environment. CMS-2Y tactical software code needs to run in the ATB hosted environment on an X86 computer system running the Linux operating system to ensure that the ships running CMS-2Y software are accurately and thoroughly tested. A method is required to translate CMS-2Y tactical code to support its evaluation on the ATB.

The solution will develop emulation, virtualization, and/or compilation using open-source code to facilitate testing critical updates of AEGIS ships operating with CMS-2Y tactical code. The translator must execute the 32-bit CMS-2Y tactical code on COTS X86 computer system running the Linux operating system in the ATB environment. The translated CMS-2Y code must process data at variable speeds (real-time, faster than real-time, and slower than real-time) to support various test requirements and scenarios in the ATB. Translation results will be compared to Navy land-based test facility operational data to verify and validate emulator functionality.

The Phase II effort will likely require secure access, and NAVSEA will process the DD254 to support the contractor for personnel and facility certification for secure access. The Phase I effort will not require access to classified information. If need be, data of the same level of complexity as secured data will be provided to support Phase I work.

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. Owned and Operated with no Foreign Influence as defined by DOD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Security Service (DSS). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this contract as set forth by DSS and NAVSEA in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advance phases of this contract.

PHASE I: Develop a concept for a translator to execute CMS-2Y legacy tactical code to run on an X86 using the Linux operating system environment as identified in the Description section of this document. Also develop a preliminary Plan of Action and Milestones (POA&M) to design, develop, test and integrate the proposed emulator into the AEGIS Test Bed. Prove the concept is feasible through comparison to Navy land-based test facility operational data to verify functionality. The Government will provide the facility for the comparison. The Phase I Option, if awarded, will include the initial design specifications and capabilities description to build a prototype in Phase II. Develop a Phase II plan.

PHASE II: Based upon the results of Phase I and the Phase II Statement of Work (SOW), develop and deliver a prototype software application. Implement the software into the ATB, which represents the combat system test environment. The prototype must be capable of demonstrating the ability to thoroughly exercise CMS-2Y-based AEGIS BL in the ATB environment. The demonstration will take place in a Government-provided facility. The company will provide software design documentation, test plans, and procedures to document and demonstrate the product meets the attributes described in the Description section of this document. Prepare a Phase III development plan to transition the technology for Navy use and Program of Record.

It is probable that the work under this effort will be classified under Phase II (see Description section for details).

PHASE III DUAL USE APPLICATIONS: Support PEO IWS 1.0 in system integration of the developed software application from Phase II. This will be accomplished by incorporation of the software into the ATB. This will consist of integrating into a BL definition, incorporation of the BL existing and new threat capabilities, validation testing, and combat system certification.

This development can be used in many of the operating systems for computers. A development that can translate code from one computer to another will be invaluable in the industry for companies that have varying and different computer systems. It would make using the different systems unique in that they can port code from one system to another and not have to worry about which computer or operating system it was developed on.

REFERENCES:

1. “Reference Manual for AN UKY-7 and AN UYK-43 Computers.” Archive.org. October 1986. 29 March 2017. https://archive.org/details/bitsavers_univacmilimmersReferenceManualfortheANUYK7andANUYK_23389579