Department of the navy (don) 17. 1 Small Business Innovation Research (sbir) Proposal Submission Instructions introduction

PHASE I: Define and develop conceptual approaches and architectures to implement an open, modular, extensible and updateable cybersecurity framework that addresses attributes identified in the description section of this document. Feasibility for the selected approach will be established through modeling and algorithms that show their ability to allow for upgrades or modifications across multiple combat system architectures. The Phase I Option, if awarded, should include the initial layout of the capabilities description.

PHASE II: Based upon the results of Phase I, a software-based prototype of the cybersecurity framework will be developed and delivered. The prototype must be capable of demonstrating the integration of various non-vendor specific cybersecurity capabilities, the ability to manage those capabilities, a secure (authenticated and validated) ability to add new and update existing capabilities, and have a simple and intuitive user interface that visualizes the cyber health status of the system. All of these capabilities need to be executable with little to no impact to the performance of the combat system. The prototype will be validated through the company, coordinating test event(s) with each identified Navy combat systems test director and test team via a LBTS (Land Based Test Site) used for testing those systems. The company will develop a Plan of Action and Milestones (POA&M) to design, develop, test and integrate the proposed architecture into both the AEGIS and SSDS combat system environments and preliminary cost estimates for each identified approach.

The company shall provide requirements and interface description documentation, test plans and procedures to demonstrate the product meets the attributes described in the description section of this document, and a transition plan for Phase III.

PHASE III DUAL USE APPLICATIONS: Support both PEO IWS 1.0 and 10.0 in the integration of the developed cybersecurity framework from Phase II. This will be done by incorporation of the framework into each combat systems (AEGIS and SSDS) baseline modernization process. This will consist of integrating into a baseline definition, incorporation of the existing baselines and new cybersecurity capabilities, validation testing, and combat system certification. Private Sector Commercial Potential: This framework can support any environment that has challenges with integrating, updating, and managing various vendor cybersecurity capabilities due to the cost associated with required and frequent updates and the inability to update quickly. Examples of those industries could be those that use control systems such as nuclear facilities or the Federal Aviation Administration (FAA).

REFERENCES:

1. Department of the Navy Chief Information Officer. “DOD Instruction 8500.1 Cybersecurity.” DTIC. 14 March 2014. http://dtic.mil/whs/directives/corres/pdf/850001_2014.pdf.

2. “Host Based Security Systems.” Defense Information Systems Agency (DISA). 15 April 2016. http://disa.mil/cybersecurity/network-defense/hbss.-

KEYWORDS: Combat systems; Cybersecurity Challenges; Cybersecurity; Vendor-lock; Defense-In-Depth approach; Cybersecurity framework; Host Based Security System

Questions may also be submitted through DoD SBIR/STTR SITIS website.

TECHNOLOGY AREA(S): Battlespace, Electronics, Sensors

ACQUISITION PROGRAM: Program Executive Office Integrated Warfare Systems (PEO IWS) 2, Above Water Sensors, AN/SPS-49 Radar

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 advanced direct digital exciter software and associated hardware technology for direct synthesis of radar waveforms to improve performance and reduce cost.

DESCRIPTION: The Navy requires modern direct digital exciter technology for use with frequency-agile, rotating, air surveillance radar systems. Existing radar systems, designed 30 to 40 years ago, utilize two or three analog frequency up-conversion stages with multiple local oscillators, mixers, adders, dividers, and switches to create pseudo-frequency-agile radar waveforms. State-of-the-art in their time, these systems do not have the complete waveform agility to maintain optimum performance under future electronic attack. The only means for implementing advanced, robust, truly frequency-agile waveforms is to replace the entire radar exciter subsystem. Furthermore, as existing systems age and require technical updating (tech refresh), an opportunity to simplify the system, reduce the cost of future tech refresh, and allow for software generated waveform upgrades becomes available. Technology refresh for radar systems covering all transmitter and receiver electronics is actively being pursued. Maintaining operational availability for older radar systems is increasingly difficult and maintenance costs are increasing due to parts obsolescence. Consequently, advanced direct digital exciters will realize continuing cost savings as well as performance enhancements in legacy radars throughout their remaining service lives.

Digital technology presents the radar system designer with a multitude of options and allows arbitrarily transmitted waveforms to be synthesized on a pulse-to-pulse basis (Ref. 1). Digital signal synthesis (akin to arbitrary waveform generation) has matured rapidly over the past two decades with the state of the art now defined by, among other applications, software defined radio. However, radar requirements present unique challenges in the art of signal design, signal transmission, reception, and signal processing. This is especially so in the face of modern electronic attack threats. In order to remain effective, legacy radars require signal agility with high quality. Signal quality is defined by frequency stability; the absence of noise, spurs and harmonics; and highly precise timing. True agility begins with the ability to implement arbitrary intra-pulse modulations, continuously and instantly variable pulse widths, and non-periodic pulse repetition.

Both signal quality and agility are inhibited by analog circuitry (principally up-conversion stages) in the transmit chain. For example, mixers in up-conversion stages can introduce harmonics and nonlinearities. Conversion from the digital domain directly to the radio frequency (RF) domain (i.e., at the transmitted frequency) eliminates most of these problems (Ref. 2). Fortunately, advances in high-speed digital to analog converters (DACs) make this possible (Ref. 3). Consequently, high-speed direct digital synthesis (DDS) presents an attractive means for high bandwidth frequency synthesis of radar waveforms because it features sub-hertz frequency resolution, fast settling time, continuous phase switching response and low phase noise (Ref. 4).

The Navy seeks an innovative software solution for advanced, affordable, and agile direct digital exciter technology for use with existing, rotating air surveillance radar systems. The initial targeted frequency is the upper UHF band (specifically 500-1000 MHz). However, the exciter architecture and core technologies should allow extension to higher bands (particularly S-Band). Affordability not only implies initial acquisition cost, but the cost of implementing new waveforms as well as the ability to introduce future hardware upgrades. Consequently, the exciter architecture should be modular, open, and expandable (for example, the architecture should accommodate future memory and processor upgrades). The exciter function is largely expected to be software defined. Therefore, the exciter software should be easily supportable for modification, testing, performance verification, validation, and information assurance certification. Likewise, the ability to quickly update executable software, configuration files, and libraries in order to deploy new capabilities, while underway, is desired. In addition, the Navy is already developing direct digital bandpass receiver technology for the same band; therefore, the exciter must provide an interface that allows synchronization with the receiver.

Cost, performance, and reliability are the major factors driving development of the direct digital exciter. As a target, the cost of the exciter, once in production, should be less than $250K and a 25-year service life should be anticipated. Evidence of design optimization of these parameters as well as a comparison between model predictions and measured performance are expected. The exciter system should include filtering, as required, to eliminate spurious output and should be immune to shipboard prime power system noise and fluctuations. Size, weight, and power consumption (SWaP) are subordinate, although still important, considerations. Current analog exciter technology typically takes up an entire radar cabinet (19-inch wide rack, approximately 60 inches high and 24 inches deep). As a goal, the digital exciter should consume only one quarter of this space. Proposed technologies should highlight innovation in the areas of frequency resolution, frequency-switching speed, modulation capabilities, pulse-to-pulse agility, suppression of spur and harmonic generation, SWaP, cost, reliability, and sustainability.

PHASE I: The company will develop a concept for advanced direct digital exciter software and associated hardware for direct digital synthesis of radar waveforms in the upper UHF band. The company will demonstrate the feasibility of its concept in meeting Navy needs and demonstrate that the concept can be implemented, feasibly and affordably in legacy rotating radars. Feasibility will be demonstrated by some combination of modeling and analysis. Affordability will be established by analysis of the proposed architecture, major assemblies, and required software and by comparison to systems of comparable complexity.

PHASE II: Based on the Phase I results and the Phase II Statement of Work (SOW), the company will produce and deliver a prototype direct digital exciter for radar waveform generation in the upper UHF band. Evaluation will primarily be accomplished by laboratory testing of complex radar waveform scenarios accompanied by appropriate data analysis and modeling. Testing will also include demonstration of the software update capability. The company will perform testing in consultation with Government subject matter experts in order to define realistic waveforms of interest. Affordability will be addressed by refining the affordability analysis performed in Phase I to reflect the knowledge gained during Phase II execution. The company will prepare a Phase III development plan to transition the technology for Navy use.

PHASE III DUAL USE APPLICATIONS: The company will be expected to support the Navy in transitioning the direct digital exciter for radar waveform generation in the upper UHF band technology to Navy use. The company will further refine direct digital exciters according to the Phase III development plan for evaluation in actual radar systems in order to determine their effectiveness and reliability in an operationally relevant environment. The company will perform operational testing and validation to certify and qualify initial production units for Navy use. The final product will be produced by the company (or under license) and transition to the Government either directly or through its prime contractors. Private Sector Commercial Potential: The direct digital exciter, in its productized form, will have limited potential for dual use. However, the core technology has multiple applications to the areas of electronic warfare and communications (both military and civilian).

REFERENCES:

1. Glascott-Jones, A., et al. "Direct Conversion Techniques for Radar Systems.” 14th International Radar Symposium (IRS 2013), Dresden, 19-21 June 2013: pp. 288-295.

2. Bore, Francois, et al. "3GS/s 7 GHz BW 12 Bit MuxDAC for Direct Microwave Signal Generation over L, S or C Bands.” 2011 IEEE Int. Conf. Microwaves, Communications, Antennas and Electronic Systems (COMACS), Tel Aviv, 7-9 Nov. 2011: pp. 1-8.

3. Wingender, Marc and Chantier, Nicolas. "3 GS/s S-Band 10 Bit ADC and 12 Bit DAC on SiGeC Technology.” 2009 Int. Radar Conf. Surveillance for a Safer World (RADAR 2009), Bordeaux, 12-16 Oct. 2009: pp. 1-8.

4. Glascott-Jones, A., et al. "Further Results from a 4.5GSps Digital to Analog Converter of Direct Microwave Synthesis of Radar Signal up to X band.” 2015 IEEE Radar Conference, Johannesburg, 27-30 Oct. 2015: pp. 312-316.

KEYWORDS: Direct Digital Synthesis; Arbitrary Waveform Generation; Digital to Analog Converters; Digital Exciter; Radar; Waveform Agility.

Questions may also be submitted through DoD SBIR/STTR SITIS website.

TECHNOLOGY AREA(S): Information Systems

ACQUISITION PROGRAM: Program Executive Office Integrated Warfare Systems (PEO IWS 1.0) – AEGIS Combat System; PEO IWS 10.0 – Ship Self Defense System (SSDS)

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 learning algorithm behind the “Internet of Things” and “Big Data” analytics to more accurately predict ship equipment health conditions subsequent asset operational availability.

DESCRIPTION: The operational availability (Ao) demand on the surface fleet continues to increase. Currently, each ship is assessed independently and there is not a way to pull data from multiple platforms into a single repository to learn and draw conclusions on Ao. Use of Big Data principles would allow the fleet to pull larger disparate sets of data and apply Internet of Things algorithms and principles to better define Ao. The algorithms would allow the fleet to see data in a different way and draw conclusions across multiple platforms. It would discourage stovepipe initiatives while improving Ao and allow the technical community to see how systems are used. The Big Data models could reduce logistics delay time and total ownership cost by targeting the necessary parts to procure. Big Data could provide new trends and potentially negate a fleet-wide issue.

The importance of advanced analytics, or “Big Data,” is that larger datasets used during analysis can provide more statistically significant findings. The cost, however, is that new approaches and algorithms must be developed that provide on-line forecasts while managing large disparate structured and unstructured datasets. By combining access to large amounts of disparate data with advanced learning algorithms, the entire life cycle of shipboard equipment can be better understood and managed, especially within the context of a ship’s operating environment. The Navy seeks innovative approaches to applying advanced analytics to the understanding of shipboard equipment. The analysis inputs include traditional equipment condition with new sources of data such as ship’s operations, environment, mission, and personnel. Traditionally, corrective and preventive maintenance is performed to meet operational availability requirements.

Preventative Maintenance Schedules (PMS) are typically informed through historical data analysis, which does not include the context of what was happening when an equipment reading was made. Because corrective and preventive maintenance encompasses a significant portion of the total ownership costs for Navy weapon systems, it is necessary to find new forms of analysis that can facilitate a better understanding of what is driving maintenance actions. Unnecessary maintenance associated with preventive maintenance currently contributes to inflated ownership costs and reduced readiness for deployable assets. The goal is to advance the science of ship analytics based on large data sets and time constraints. The outcome will be that by using advanced learning algorithms, the Navy is positioned to plan and execute maintenance actions at the point of performance and gain visibility to both individual ship and battlegroup Ao.

The level of data available, whether on ship or shore, makes accurate analysis of Ao extremely difficult because the number and complexity of systems is increasing, the volume and velocity of data is increasing, and the burden carried by technology is increasing. A software solution is needed to normalize disparate sources of data and determine the amount of data available for analytical purposes. Within the context of data capture, a targeted area of the ship or specific equipment will be chosen as the area of research and development. Currently, equipment health data is stored and often not fully utilized, especially in real-time onboard ship. The data often ends up in repositories, completely disconnected and thus unable to contribute to the full picture and the Navy’s greater vision of Condition Based Maintenance Plus (CBM+). A centralized approach to analyzing all relevant equipment or sensor data is required to overcome the current disparity of analytical functions. Ideally, the data used would be available for real-time analysis to inform maintainers of maintenance actions at the point of performance. Additional data (e.g. volume and velocity) will result in statistically significant findings and a greater opportunity to invoke the premises of the Internet of Things. Proposed solutions must also address the challenge of real-time maintenance planning with an understanding of its impact on mission readiness while handling and processing large data sets. Traditionally, isolated data sets are eventually analyzed on shore. This shore-based analysis often ignores the context of the moment, providing an incomplete situational picture. A solutions ability to analyze large disparate data sets accurately and quickly, decision-making becomes far more effectual. Aggregating disparate data and performing statistical analysis is necessary to more efficiently and effectively plan and perform maintenance. Detailed test planning should be provided to demonstrate the proposed solution meets the intent of the aforementioned CBM+ philosophies.

PHASE I: The company will define and develop a concept for a software solution with the ability to analyze large data sets in the use of advanced analytics to better define Ao using targeted data sources, both structured and unstructured, currently available onboard Navy ships. Feasibility will be established by defining an approach for data capture and aggregation, and analytical approaches, which combines the data for use in maintenance planning. The Phase I Option, if awarded, will include the initial design specifications and capabilities description to build a prototype solution in Phase II.

PHASE II: Based on the Phase I results and the Phase II Statement of Work (SOW), the company will design, develop, and deliver a prototype solution with the ability to analyze large data sets. The prototype must be capable of successfully demonstrating an in-depth analysis resulting in new approaches to maintenance planning. The results of this analysis will be validated both by Government Subject Matter Experts in Ao and logistics management and planning. The company will provide a detailed test plan to demonstrate that the deliverable meets the intent of advanced Condition Based Maintenance Plus (CBM+) efforts. A Phase III qualification and transition plan will also be provided at the end of Phase II.

PHASE III DUAL USE APPLICATIONS: The company will be expected to support the Navy in transitioning the technology to Navy use during Phase III. The company will support the Navy in the system integration and qualification testing for the software technology developed in Phase II. This will be accomplished through land-based and ship integration and test events managed by PEO IWS to transition the technology into the CBM+ efforts for AEGIS class ships. Private Sector Commercial Potential: Many private sector organizations are working to utilize Big Data and the Internet of Things in order to implement CBM+ as a means for reducing operating costs and increasing uptime. Markets such as manufacturing and transportation will be able to exploit the results of this Topic.

REFERENCES:

1. Manyika, J., Chui, M., Brown, B., Bughin, J., et.al. McKinsey Global Institute. “Big data: The next frontier for innovation, competition, and productivity.” May 2011. April 2016. http://www.mckinsey.com/business-functions/business-technology/our-insigh

2. Collum, P. H. “OPNAVINST 4790.16B Condition Based Maintenance and Condition Based Maintenance+ Policy.”, 01 Oct 2015, 19 April 2016. https://doni.documentservices.dla.mil/Directives/04000%20Logistical%20Support%20and%20Services/04-700%20General%20Maintenance%20and%20Construction%20Support/4790.16B.pdf

3. Chui, M., Löffler, M., Roberts, R. McKinsey Global Institute. “The Internet of Things.” March 2010. April 2016. http://www.mckinsey.com/industries/high-tech/our-insights/the-internet-of-things -

KEYWORDS: Big Data Analysis; Internet of Things Advanced Analytics; Operational Availability; Equipment or Sensor Data Analysis; Disparate Sources of Data; Systems Data Analysis for Maintenance Evaluation and Planning

Questions may also be submitted through DoD SBIR/STTR SITIS website.

TECHNOLOGY AREA(S): Battlespace, Electronics, Sensors

ACQUISITION PROGRAM: Littoral Combat Ships (PEO LCS), PMS406, Unmanned Influence Sweep System (UISS) Program. FY17 FNC for Autonomous Unmanned Surface Vehicles for Mine Warfare (MIW); FY18 Manned Unmanned Mission Planning; Common Control System (CCS)

OBJECTIVE: Develop an acoustic detection and identification system to provide automatic situational awareness capabilities for the Navy’s Unmanned Surface Vessels (USVs).

DESCRIPTION: The Navy needs better situational awareness for USVs (via an onboard automated “lookout”) to avoid collisions during missions. Navigation Rules COMDTINST M16672.2D requires all vessels to have a proper “look-out” to avoid the risk of collision in any condition of visibility as quoted “every vessel shall at all times maintain a proper look-out by sight and hearing as well as by all available means appropriate in the prevailing circumstances and conditions so as to make a full appraisal of the situation and of the risk of collision.” Maritime sound is a very important and often overlooked aspect of unmanned vessel safe navigation. Sound signals are used to designate maneuvering intent in conditions of uncertainty or restricted visibility and provide a secondary, very reliable source of information for mariners to determine whether sufficient action is being taken to avoid collision.