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Army 14. 1 Small Business Innovation Research (sbir) Proposal Submission Instructions

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Traditional structured query language databases follow a “one-size-fits-all” approach to data storage and retrieval. High performance applications can be tuned for efficiency using non-relational approaches to database design. Algorithmic research is required to apply non-relational database techniques to virtual environment based trainers, therefore allowing for an increased amount of simultaneous training participants and/or increased level of complexity in the virtual training environment. After the analysis of the simulation based training prototype is complete and the non-relational database is constructed for it, a generalized version of that database and the analysis approach can then be used and applied to other simulation based training applications. The impact of this SBIR technology directly affects the virtual training application’s ability to scale the number of participants and the ability to increase complexity of the scenes.
Non-relational database approaches have not yet been widely adopted by simulation based training architectures. Non-relational database architectures are still in their infancy compared to traditional database approaches. Early experimentation in academia shows great promise for efficiency gains with their proper employment. The commercial game engines that virtual training systems are based on have different data requirements as they are proprietary and do not share a common code base. The non-relational databases rely on specific tuning for each application making the prospect of a migration to a non-relational database risky for them. A generalized approach that shows significant increases in performance and efficiency would be extremely attractive to industry. Possible areas of optimization include scene graph management and object sorting. When dealing with extremely large data sets or many simultaneous uses, both of these applications would benefit from increases in efficiency and translate to increased performance.
Scalability can be examined in three different categories: size of operational area, number of entities in the environment, and the complexity of the environment. The next generation of training applications need to handle more human users, more complex objects such as vehicles and non-player characters and larger operational areas to create realistic scenarios in new operational environments. The application of next generation database technology to existing prototypes will help achieve increases in scalability.
PHASE I: The offeror will be provided with a prototype virtual training environment currently in use by the US ARL/STTC. The offeror will analyze and provide conceptual designs for alternative database deployment for the virtual environment provided to them by the US ARL/STTC. The alternate database designs will include non-relational distributed clusters intended to maximize scalability and availability for use with large quantities of data. The offeror will provide estimates of differences in performance. This effort will determine the feasibility of applying the non-relational distributed model to the problem of database scalability.
PHASE II: The offeror will conduct a comparative study of the latest research in non-relational distributed databases with the current relational database deployments. The offeror will design performance tests which are representative of real-world usage and report the results of the testing. The offerer will develop, test, and demonstrate an implementation of the highest performing database approach in a relevant Army training environment, such as the ARL Simulation and Training Technology Center (STTC) Military Open Simulator Enterprise Strategy (MOSES) effort and the Training and Doctrine Command’s (TRADOC) Enhanced Dynamic GeoSocial Environment (EDGE) effort. Although the approach may be demonstrated in a specific environment (e.g., MOSES)t, the design will not be tied to this environment. The outcome of this research is to provide designs and guidance for current simulation based training environments to inspect for possible migration to a next generation non-relational distributed database. Phase II deliverables will include a comparative study of the various non-relational database designs, test results, a working prototype of the highest performing implementation.
PHASE III: The offeror will work to apply this approach for large scale (mission command to boots on the virtual ground) operations during mission rehearsal exercises. A simulated mission rehearsal training exercise will support thousands of human users simultaneously with an accurately represented operational area. Beyond US Army use, the offeror will also work to commercialize the results of the application research and resulting algorithms as a solution in the entertainment industry to provide more realistic gaming experiences, with higher fidelity and larger numbers of simultaneous players.

1. Burtica, R., Mocanu, E. M., Andreica, M. I., & Tapus, N. (2012). Practical application and evaluation of no-SQL databases in Cloud Computing. IEEE International Systems Conference, 1–6.

2. Li, Qingchun, Wen, Xiaolong, Gao, Yun, Zhang, Xuejie, and Gu, Gengiang. (2012). An Overview of Newly Open-source Cloud Storage Platforms. IEEE International Conference on Granular Computing, 142–147.
3. Boicea, A., Radulescu, F., & Agapin, L. I. (2012). MongoDB vs Oracle -- Database Comparison. 2012 Third International Conference on Emerging Intelligent Data and Web Technologies, 330–335. doi:10.1109/EIDWT.2012.32
4. Liu, Y., Wang, Y., & Jin, Y. (2012). Research on the improvement of MongoDB Auto-Sharding in cloud environment. 2012 7th International Conference on Computer Science & Education (ICCSE), (Iccse), 851–854. doi:10.1109/ICCSE.2012.6295203
5. Hecht, R., & Jablonski, S. (2011). NoSQL evaluation: A use case oriented survey. 2011 International Conference on Cloud and Service Computing, 336–341. doi:10.1109/CSC.2011.6138544
6. Silva, L. P. D. P. D., & Dissanayake, M. (2006). Optimal Design of Distributed Databases. International Conference on Information and Automation, 00, 1–6.
7. Xiang, P., Hou, R., & Zhou, Z. (2010). Cache and consistency in NOSQL. 2010 3rd International Conference on Computer Science and Information Technology, 117–120. doi:10.1109/ICCSIT.2010.5563525
8. Dai, X., & Xie, Q. (2011). An approach to build high-performance OPC-XML DA server system based on MONGODB and Comet. 2011 International Conference on Electrical and Control Engineering, 2881–2884. doi:10.1109/ICECENG.2011.6057412
KEYWORDS: Scalability, non-relational database, distributed database, noSQL, high performance computing, grid, cluster, cloud, xml database, multidimensional database, multivalue database

A14-028 TITLE: Interference Cancellation for Mobile Force Protection Jamming

ACQUISITION PROGRAM: PEO Intelligence, Electronic Warfare and Sensors
The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), which controls the export and import of defense-related material and services. Offerors must disclose any proposed use of foreign nationals, their country of origin, and what tasks each would accomplish in the statement of work in accordance with section 5.4.c.(8) of the solicitation.
OBJECTIVE: Demonstrate an approach for canceling cosite interference on dismounted soldiers as a result of collocated communications and an electronic warfare (EW) system without a physical connection between the EW and communications system.
DESCRIPTION: Dismounted soldiers carry various Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR) equipment associated with various mission profiles. The Army continues to strive to bring more information and extend the tactical network all the way down to the individual rifleman. This requires dismounted soldiers to carry various wireless communications equipment which is spectrum dependent. In addition to communicating over the Network, some missions necessitate the need for soldiers to carry electronic warfare (EW) for force protection. Soldiers carrying both radios and EW systems become at risk for electromagnetic fratricide between the systems. This can lead to degraded communications and the inability to deliver timely situational awareness and mission command in a friendly force EW environment.
Solving this issue for dismounted soldiers introduces unique challenges as the size, weight, and power (SWaP) associated with equipment must be conducive to dismounted soldier operations. Adding additional cabling between the EW and communications systems introduces additional weight, snag hazards, and mobility issues. The Army is seeking an innovative solution to the cosite interference issue with minimal increase to soldier load. The solution shall consist of an appliqué that is integrated onto the communications radio. Such an approach may consist of a "sleeve" for a handheld radio that provides the interference cancellation capability. The size of the appliqué shall not increase the size of the integrated system (handheld + appliqué) to more than 50% of the standalone handheld radio. The target platform for this appliqué is the AN/PRC-154 "Rifleman Radio". The appliqué shall be battery powered with interconnections that are compatible with the Rifleman Radio.
The approach must address in-band (within the modulation bandwidth of the primary communications signal) interference generated by the EW system. This interference can be a result of products of intermodulation, harmonics, spurious emissions, and an elevated noise floor generated by the EW system. The approach shall also address out of band interference generated by the aforementioned effects as well as interference generated by the primary EW signal(s). It is assumed that the communications signal is not assigned to a targeted EW frequency. The proposed approach cannot change the output of the EW system. The approach shall also consider the effects of near-by EW systems operating on adjacent soldiers in close proximity. The approach shall also consider the desensitation effects that occur within the communications system as described in [1].
The solution shall address the Soldier Radio Waveform (SRW) operating over a tuning range of 225 MHz – 2 GHz. The solution shall provide at least 25 decibels (dB) of interference reduction, with a target of 40 dB, within the modulation bandwidth of the SRW channel. This shall be accomplished without a priori knowledge of the EW signal (i.e. reference signal).
PHASE I: The Phase I effort shall include feasibility study outlining problem considerations and potential solutions. An analysis of theoretical limits of the various technical approaches shall be presented in additional to practical limitations. The Phase I effort will identify the best approach and provide a recommendation for Phase II implementation. The Phase I deliverable will be a report documenting the results of the Phase I effort.
PHASE II: The Phase II effort shall construct and demonstrate the operation of a prototype that will cancel cosite interference on the Soldier Radio Waveform. The prototype shall cover the 225 MHz – 450 MHz military Ultra High Frequency (UHF) communications band. The effort shall include power considerations with respect to battery life associated with the developed hardware. The Phase II prototype will be tested at a government facility in an operationally representative environment and shall demonstrate at least 25 dB restoration in receiver sensitivity in the presence of the EW system. The prototype shall be delivered to the government with an associated user manual, interconnect diagram, and a report documenting the results of the Phase II effort.
PHASE III: Phase III efforts will focus on reducing the size, weight, and power of the Phase II prototype and integrating into Army Program of Record SRW radios. This work will include extending the Phase II prototype to cover additional frequency bands. The Phase III work may also target additional commercial off the shelf (COTS) SRW radios that are demonstrated during the Army’s Network Integration Events (NIE) which currently occur twice a year. The technology developed under Phase II may also be modified and transitioned to the commercial cellular for use in mitigating the strong interferers in Code Division Multiple Access (CDMA) systems.

1] Jacob Gavan, Elya Joffe, Non Linear Radio Mutual Interference Main Effect Analysis and Mitigation Methods, General Assembly and Scientific Symposium, p. 1-4, 2011.

2] Keith Allsebrook, Chris Ribble, VHF Cosite Interference Challenges and Solutions for the United States Marine Corps’ Expeditionary Fighting Vehicle Program, Proceedings of the 2004 IEEE Military Communications Conference, p. 548-554, 2004.
3] Y. Bar-Ness, et. Al, Adaptive Interference Cancellation and Signal Separation Techniques for Multiuser Systems, RL-TR-96-207 Final Technical Report, 1996.
4] Hendrik Schoeneich, Peter Hoeher, Single Antenna Interference Cancellation: Iterative Semi-Blind Algorithm and Performance Bound for Joint Maximum-Likelihood Interference Cancellation, Proceedings of the IEEE Global Telecommunications Conference, vol.3, p 1716-1720, 2003.
KEYWORDS: Cosite interference, interference cancellation, adaptive filtering, active cancellation, communications, electronic warfare, multipath, dismounted

A14-029 TITLE: Cyber War Gaming

TECHNOLOGY AREAS: Information Systems
The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), which controls the export and import of defense-related material and services. Offerors must disclose any proposed use of foreign nationals, their country of origin, and what tasks each would accomplish in the statement of work in accordance with section 5.4.c.(8) of the solicitation.
OBJECTIVE: An innovative HW/SW solution will be developed to map a computer host/network, run attack scenarios without disrupting the host/network and develop actionable courses of action to counter real cyber-attacks.
DESCRIPTION: The Cyber domain represents an elusive environment that the Army must defend. Over the past two decades, our cyber defenses have been largely ineffective, because our information systems are relatively static while our cyber defenses have remained reactive in nature, making it fairly easy for adversaries to explore, map, track, and then to launch decisive attacks. Cyber-attack threat vectors are increasing in quantity and quality. Their level of sophistication and their ability to evade and circumvent our defensive cyber capabilities has grown by leaps and bounds. Cyber adversarial reasoning capabilities that can inform our defensive sensors about adversarial intent and thus improve attack prediction on the host platform and network are greatly needed. Using this solution will deliver timely mission command & tactical intelligence to provide cyber threat situational awareness in all environments.
Today’s cyber battle space has seen attack threat vectors increasing in quantity and quality. From Internet connected televisions, to SCADA systems hastily connected to the Internet; the number of Internet connected devices have skyrocketed in recent years, resulting in an exponential increase in the attack surface. Nation state funded cyber espionage has brought highly skilled professional hackers in the cyber battle space. Hacktivism in the mainstream adds a new dimension to cyber warfare in that their actions have many unforeseen consequences. While the motivations between hacktivism and nation state based espionage differ; the result is the same. Someone always wants access to the data you are trying to protect, or disrupt the network you rely on. In an effort to understand this new battle space, the capabilities to analyze cyber adversarial reasoning and predict attacks on the host and network are needed for both strategic and tactical networks. The tactical network brings additional cyber issues due to a communications and networking architecture that is resource constrained and ad-hoc in nature where both the infrastructure and end hosts have the ability to roam on the battlefield. The goal of this effort is to investigate the use of host and network based sensor agents to develop accurate host and network maps, analyze the data for potential threat vectors and provide aggregated threat prediction with 85% confidence. This data can then be provided to an automated war gaming engine to run scenarios through the host and network in real-time to determine the most likely attack vectors on the network. This approach will aide in an overall plan to understand the adversaries strategies and tactics in order to build real-time adaptive software/network protection systems that will be developed and run in this environment. This solution will Increase network resilience and provide the building blocks for proactive cyber defenses. This will enable better network planning and stronger configurations of strategic and tactical networks.
PHASE I: 1) Develop algorithms that can reason on adversarial intent and that can improve cyber attack prediction. 2) Develop an automated war gaming engine to run scenarios in real-time on the host and network. 3) Show overall feasibility of concept, with demonstration software on representative hardware. Concept/approach should emphasize its scalability to an enterprise network and a minimum of 100 nodes. The concept/approach should also use major standards to ensure use in a common operating environment to the greatest extent possible. 4) Produce a detailed research report outlining the design and architecture of the system, as well as the advantages and disadvantages of the proposed approach.
PHASE II: 1) Based on the results from Phase I; design and implement a fully functioning prototype solution for both cyber adversarial reasoning and attack prediction and war gaming engine. 2) Provide test and evaluation results that demonstrate the effectiveness and accuracy of capabilities of the solution to reason on adversarial intent and perform attack prediction on the host and network. 3) Develop a final report describing the strategy, architecture, design, and development of cyber adversarial reasoning and attack prediction and war gaming engine techniques.
PHASE III: Phase III Dual Use Application: Further develop prototype into a transitional product with necessary documentation for a Program of record such as the Warfighter Information Network - Tactical (WIN-T) or Program Execution Office Enterprise Information Systems (PEO EIS) for integration into their infrastructure. This capability could be incorporated into commercial host/network security applications to enhance the usability and improve commercial security protection.

1. Zheng Bu, Toralv Dirro, Paula Greve, David Marcus, François Paget, Ryan Permeh, Craig Schmugar, , Jimmy Shah, Peter Szor, Guilherme Venere, and Adam Wosotowsky of McAfee Labs. “MacAfee Labs 2012 Threat Predictions”

2. P. Barford, M. Dacier, T. G. Dietterich, M. Fredrikson, J. Giffin, S. Jajodia, S. Jha, J. Li, P. Liu, P. Ning, X. Ou, D. Song, L. Strater, V. Swarup, G. Tadda, C. Wang, and J. Yen “ Cyber SA: Situational Awareness for Cyber Defense”
3. Dan Shen, Genshe Chen Intelligent Automation, Inc Rockville, MD 20855, Erik Blasch AFRL/SNAA WPAFB, OH 45433, George Tadda AFRL/IFEA Rome, NY 13441. “Adaptive Markov Game Theoretic Data Fusion Approach for Cyber Network Defense”
KEYWORDS: Game theory, Cyber security, adversarial reasoning, Cyber attack prediction, attack threat vectors

A14-030 TITLE: Fragmented Spectrum Efficiency Manager

TECHNOLOGY AREAS: Information Systems
OBJECTIVE: Research and develop programmable RF transceiver technology, including software, hardware and documentation capable of fragmenting one RF transmission into multiple RF fragments and reassembling fragments post reception to original composite.
DESCRIPTION: This Fragmented Spectrum Efficiency Manager (FSEM) system effort is intended to provide communications capability to deliver detection-resistant timely mission command & tactical intelligence and situational awareness in all environments. Use of Commercial Off The Shelf (COTS) products are important but not to the extent of restricting research. The solution must demonstrate coherent processing in the fragmenting of a transmission and distribution of fragments of spectrum to four or more geosynchronous satellite paths. The solution must also demonstrate the aggregation of the fragments post satellite transmission. Therefore this effort requires at least two independent hardware elements to operate geographically separated. The FSEM fragmenting and aggregating will function in the frequency range of 1 to 2 GHz (L-Band). The FSEM must however interface to a frequency conversion component for transport and address satellite communications latencies associated frequencies from C through Ka band. The FSEM must also interface with satellite modems in the same frequency range as the frequency conversion interface. The solution can utilize overhead framing techniques but efficiency must be great enough to stay below the 5% bandwidth overhead utilization threshold. Lastly, the energy per bit, commonly referenced to a 0 dB noise figure (written in the industry as Eb/N0) can grossly impact data throughput. Power levels from path to path will vary. Satellite link performances can range from unusable to completely error free within 2 dB Eb/N0. The energy per bit performance at the receive part of the modem will be a strong metric in terms of receive quality of a satellite link in assessing FSEM aggregation performance. The solution should incorporate leveling and phase techniques to enable optimized aggregation. The means to fragment a spectrum transmission into 4 or more segments and re-aggregating fragments successfully recovering the original data error free within the original composite post aggregation with low bandwidth cost is the technical risk associated with this effort.
The FSEM fragmenting processor will have only one L-Band carrier SMA input and at least four L-Band SMA fragment outputs. As a threshold requirement, each input and output of the FSEM fragmenting processor must handle a single carrier bandwidth range of 38.4 KHz through 40 MHz with an objective requirement of 150 Hz (or lower) through 1 GHz (or higher). The FSEM fragmenting processor number of outputs must be programmable by the user to distribute fragments between 0-100 percent of the original composite input up to at least four spectrum outputs, including: replicating 100% to one channel; 100% to each channel; and non-replicated, uneven distribution. As an example, the FSEM fragmenting distribution non-replicated across four channels can be: 10%, 25%, 30% and 35%. Interfacing signal levels range from +5 to -40 dBm.
The FSEM aggregating processor will be completely independent of the FSEM fragmenting processor and have a corresponding minimum of four L-Band SMA inputs and one L-Band SMA output. Each FSEM aggregating processor channel input/output carrier bandwidth requirements are the same as the FSEM fragmenting processor. Interfacing signal levels range from -25 to -70 dBm.
PHASE I: In Phase I, the contractor shall develop the architecture and the design approach for the programmable Fragmented Spectrum Efficiency Manager (FSEM) system. The architecture and design should, at a minimum, meet the threshold requirements identified in the Description paragraph, above. Existing technologies such as inverse multiplexing and packetizing are referenced as known architectures and will not be acceptable as a Phase I deliverable due to the circuit dependencies requiring feedback, bit stuffing and packetizing which are inefficient methods in a SATCOM environment. The design must be state of the art and reflect agility in programmatically replicating in a non-symmetrical fashion fragments of bandwidth across four or more channels in a single direction and be re-aggregated at the receiving end using overhead costs within the threshold cited above. The design must show the encoding and preamble methods used on the transmission disassembly that provides a method of recovery at the receiver with means to handle the varied latencies and levels encountered in the reassembly process.
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