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

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.

Mission critical information can be gained from a sensitive acoustic listening device on an unmanned vessel. The prime range of regard for acoustic listening is approximately one mile out from a small vessel, 360 degrees in azimuth. A solution may increase the situational awareness and provide safer navigation of the unmanned vehicle by successful identification, classification and localization of a sound source.

This topic seeks to develop an innovative solution for an acoustic perception system that will aid in the detection and characterization of typical maritime sounds. The solution should mimic similar sound isolation, differentiation, and localization capabilities inherently found in the human ear and auditory cortex. The solution should be able to help identify the direction and generalize the location of sound sources; provide notification to a remote operator; and, in the future, cue the on-board autonomy system. This system should identify the frequency, duration, and intensity of audible maritime sound sources. The audio perception system should have an on-board processing capability to filter extraneous noise caused by the environment and own-ship operations.

The final acoustic perception system should be packaged in a compact footprint, be light weight and power efficient such that it minimizes space, weight and power impact of the vessel. All internal and external hardware components of the audio perception system must be able to withstand harsh maritime environments with respect to water ingress, and single and repetitive wave slammed induced accelerations.

General:
Electromagnetic Interference: ISO 11452
Power (maximum): 24VDC, 30 amps

Topside Components:

Moisture: Watertight to IP67

Temperature: -4 to 110 degree Fahrenheit Shock, Single Event: 10gs sawtooth for 23ms on the vertical and two horizontal axes Shock, Repetitive: 800 repetitive shocks at 3Gs sawtooth for 23ms on the vertical and two horizontal axes.

Weight (maximum): 20 pounds

Volume (maximum): 2 cubic feet

Moisture: Water resistant to IP65

Temperature: -4 to 154 degree Fahrenheit Shock, Single Event: 10gs sawtooth for 23ms on the vertical and two horizontal axes Shock, Repetitive: 800 repetitive shocks at 3Gs sawtooth for 23ms on the vertical and two horizontal axes.

Weight (maximum): 10 pounds

Volume (maximum): 2 cubic feet

The system should utilize distributed data storage architecture, and adhere to commercially accepted, standard, non-proprietary interfaces and software protocols. The operator should be able to easily adjust auditory levels and sensitivity while controlling the system remotely through the user interface.

The operator should have the ability to focus the acoustic detection device (such as a microphone) to a particular area, isolate the sound(s), and receive a transmission of the signal in near real-time. The current state and health status of the system should be able to be monitored remotely by one or more operators. Continuous and on-command feedback transmitted to the operator should be primarily auditory, but have secondary visual cuing options available to support loud environments. These enhancements to acoustic perception would afford an enhanced operator “look-out” capability to fully appraise the remote situation and take appropriate action when necessary.

An automated audio detection system has the ability to reduce the likelihood of a collision mishap by providing additional lookout capability. Maritime sounds are integral for safe navigation and communicating with other vessels in restricted visibility conditions. Such technology also supports reduced manning and cognitive workload through use of an automated detector.

PHASE I: Develop a concept feasible for automated airborne acoustic detection and identification. Required Phase I deliverables will be a preliminary conceptual technical performance specification and design description including risk areas and possible mitigations. The Phase I Option, if awarded, will refine the concept to a detailed design producing a technical specification, drawings, performance goals and risks.

PHASE II: Based on the Phase I results and the Phase II Statement of Work (SOW), a prototype version of the Automatic Acoustic Detection and Identification system shall be fabricated, demonstrated, and delivered. The system shall be tested to compare performance to design values. Phase II Option I, if necessary and if awarded, shall allow the refinement of the design to improve any size, weight, hardening, and performance issues noted in the Phase II base testing. The new design shall be built and tested to verify design objectives. Testing in both Phase II and Phase II Option II shall be conducted underway on the water in an operationally relevant environment.

PHASE III DUAL USE APPLICATIONS: The company will be expected to support the Navy in transitioning the Automatic Acoustic Detection and Identification technology for Navy and potential commercial use. Phase III shall focus on the transition to the Unmanned Influence Sweep System and the Common USV development program that are being managed by PMS 406. It is expected that the final system will consist of the sensors and processing software that will allow the incorporation into these systems. This product would also be available to be incorporated into integrated bridge systems. Private Sector Commercial Potential: Both military and commercial manned vessels with enclosed bridges can benefit from this technology. Being in an enclosed bridge decreases a mariner’s ability to hear navigational signals and be aware of audible sounds, which may be mission critical. During reduced manning operations, this sensory deprivation is amplified due to cognitive workload. This acoustic perception system can augment the use and efficiency of typical camera systems installed on-board unmanned vessels. Instead of a slow sweeping motion, the camera can be controlled to automatically focus on audible signals or unidentifiable audible object sounds (such as engine noise of an approaching vessel, nearby explosions or gunfire) that could be undetectable on radar.

REFERENCES:

1. COMDTINST M16672.2D, Navigation Rules, International – Inland, U.S. Department of Transportation, United States Coast Guard. http://www.navcen.uscg.gov/pdf/navrules/comdtinst_m16672_2d_navrules_as_published.pdf

2. American Technology Corporation. “Long Range Acoustic Device.” Defense Update, Issue 1, 2005. http://defense-update.com/products/l/LRAD.htm.

3. Rudd, K. and Hinders, M. “Simulation of Incident Nonlinear Sound Beam 3D Scattering from Complex Targets.” J. Computational Acoustics, Vol 16, #3, 2008, pp. 427-445.

4. The Unmanned Influence Sweep System (UISS) – ACAT III SRD Rev. A (Dated 03 JUN 2014)

5. NSW C2 09-04 Improved SA for NSW Operators, NSW ISR 09-06 and NECC BA ISR 2.1-5 Specific Entity Identification provide documented needs for this capability.

KEYWORDS: Maritime Airborne Acoustic Detection Systems; Sound Detection, Identification and Localization; Maritime Situational Awareness for USVs; Automated sound processing and detection algorithms; USV Collision and Avoidance Systems; shipboard acoustic navigation sensors

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

N171-054

TITLE: Cyber Threat Insertion and Evaluation Technology for Navy Ship Control Systems

TECHNOLOGY AREA(S): Ground/Sea Vehicles, Information Systems

ACQUISITION PROGRAM: PEO Ships AM, Acquisition Management

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 system to evaluate the effectiveness of cyber defense technology for Navy ship control systems.

DESCRIPTION: The Navy needs to test the effectiveness of cyber defense methodologies for embedded devices, assess the benefit of different combinations of defense methods, develop the capability to inject classes of cyber vulnerabilities onto the device under test and have the capability to do this quickly and in a controlled test environment.

The software that embodies the logic of a process controller or closed loop control system typically deployed on an embedded system such as a programmable logic controller represents a very small percentage of the actual software that is running on a device. The rest of the software is in the operating system and firmware that allows the device to bootstrap it and to interpret the logic that the control system developer deploys on the device. This software is typically identical from device to device. Commercial-off-the-Shelf (COTS) hardware devices are often used in military designs to incur cost savings and benefit from established logistical support and supply chains. A drawback to this acquisition method is that an adversary can procure the same devices and reverse engineer them in order to develop attacks against military systems. As a result, a growing number of host-based cyber protection/defense products, methods, and techniques are being developed. It is understood that no one product, method, or technique will be the silver bullet that defends against all classes of attacks. Some defenses rely on the effectiveness of an intrusion detection method, often employing the strategy of slowing the adversary down, or causing the adversary to have to compute more than he otherwise would have based on the theory that this will make the adversary noisier and easier to detect.

The desired product of this effort shall enable the introduction of Trojan-like code at multiple entry points during design coding and lay out of a Field Programmable Gate Array (FPGA). The product shall have the capability to allow the user to specify the number of affected gates and specify the location of Trojan code on FPGA and in addition, allow code to be randomly placed on the FPGA.

The Navy seeks to develop an appliance (combined hardware and software solution) that will allow testers to quickly and easily make changes in the operating system and firmware of embedded devices so that they can effectively run a large number of test cases using different combinations of defense methodologies. The initial target platform will be an FPGA or a combination of FPGAs with microprocessors.