15. 1 Small Business Innovation Research (sbir) Proposal Submission Instructions Revised Closing Date: February 25, 2015, at 6: 00 a m. Et
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| PHASE III: Further refine the collision avoidance system design to improve performance robustness for practical operation scenarios. Further miniaturization and low cost manufacturability of the capability may be required. Transition to military and commercial applications.
PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: Commercial applications include potential use for both commercial UAS and commercial and private small aircraft. Initially the capability can be used to provide added safety in the use of UAS for first responders in a variety of civil applications. These include firefighting, crowd monitoring, damage assessment, search and rescue, and other emergencies where UAS would enhance the mission effectiveness. REFERENCES: 1. International Civil Aviation Organization (ICAO). (2006). Airborne Collision Avoidance System (ACAS) Manual. Retrieved from http://www.icao.int/Meetings/anconf12/Document%20Archive/9863_cons_en.pdf 2. Richards, W. R., O’Brien, K., & Miller, D. C. New Air Traffic Surveillance Technology. Aero Quarterly, ATR 02 – 10, Article 02. Retrieved from http://www.boeing.com/commercial/aeromagazine/articles/qtr_02_10/pdfs/AERO_Q2-10_article02.pdf 3. Insitu. Integrator UAS System Description. Retrieved from http://www.insitu.com/systems/integrator 4. AeroVironment. PUMA AE Technical Specifications. Retrieved from http://www.avinc.com/downloads/AV_PUMAAE_V10109.pdf 5. FAA. (2012). Automatic Dependent System Broadcast (ADS-B) Operations Advisory Circular, AC No. 90-114. Retrieved from http://www.faa.gov/documentLibrary/media/Advisory_Circular/AC%2090-114.pdf KEYWORDS: Collision Avoidance; ADS-B; Small Tactical UAS; Non-cooperative; self separation; autonomous operation Questions may also be submitted through DoD SBIR/STTR SITIS website. N151-027 TITLE: Condition Based Monitoring Computational Processes TECHNOLOGY AREAS: Sensors, Electronics, Battlespace ACQUISITION PROGRAM: PMS 450, VIRGINIA Class Program Office. OBJECTIVE: The objective is to develop computational processes that employ both deterministic and stochastic processes for correlating known transient functions with operational values to access an accurate state of conditions for monitoring health and predicting life-cycle. DESCRIPTION: The US Navy has a current need for in situ non-destructive sensing and condition monitoring at the node for component system characteristics in the time, frequency or modal domains. A cost effective method for monitoring health is having an early warning system for machine failure. Algorithm based detection systems suited for machinery failure modes beginning at high frequencies can be triggered as a result of operational transients. To enhance prediction of failure modes, algorithms for tracking damage accumulation with continuous monitoring from acceleration disturbances provide essential information for helping to improve component design and scheduling of maintenance functions. Limitations on hardware storage and power efficiency sometimes prevent health monitoring sampling rates to be consistent and enabling high bandwidth transient capture is problematic given the available technology. Many approaches trending towards the use of statistical methods is highly recommended to solve missing data issues where gap filling is needed when predictive analysis is necessary for measuring the conditions at the node. Detection is necessary in situations where there are machine failures in real situation such as unexpected resonance conditions or deviations caused by operator error, improper maintenance or unexpected events. To aid in the detection of health monitoring, wireless sensors are required at the node or at the component level to capture transient or steady-state response. This is particularly important as a transfer function is necessary to establish a frame of reference from which to capture pattern recognition characteristics of the component. To this end data from sensors can be measured by a handful of instruments including accelerometer, strain gage, thermo, temperature, etc., and converted for analysis purposes from time to frequency domain using Fast-Fourier-Transform (FFT) techniques or to modal domain [1, 2, 3, 7, 8]. Diagnostic functions reduce troubleshooting and maintenance times, prevent fault misdiagnosis, and avoid incorrect part repair or unnecessary replacement. Wireless devices are packaged to allow installation or mounting to components with the intent of eliminating cabling runs and the maintenance and installation costs associated with cables [4, 6]. PHASE I: The company will develop logical processes necessary to write algorithms to digital signal process sensor data including transient and steady-state input for the purpose of identifying and predicting faults and failures. Use of statistical methods is paramount for successful health monitoring and trending analysis for maintenance actions and predictive failures. Input sensor and transient data shall be from similar shipboard component, reasonable likeness or modeled to simulate system functions including known and predictive failure modes. Components shall be shipboard systems requiring health monitoring such as, but not limited to, actuation of valves that operate by electric or hydraulic based linear and rotatory dynamics, pumps, motors, compressors, etc. Consideration for algorithm processing shall execute code on processors that will be designed to capture data at the node where the component resides for the purposes of allowing wireless hardware to transmit secure output. Algorithms will also need to track the damage accumulation with continuous acceleration data (and gap filling for missing data). Example cases of machine failure modes in real situations (especially off-design conditions that commonly lead to failures, such as unexpected resonance conditions or deviations caused by human error, improper maintenance or unexpected process conditions). The company will develop methods for collecting and processing data and define requirements necessary to apply to specific applications where diagnostics and prognostics can be performed to apply condition based monitoring techniques. The Navy needs will be meet as company demonstrates component systems are tested to validate successful detection from monitoring various intentional input failure modes and providing symptom/effect as a result of the fault and failure for trending and life-cycle predictions. The company will prepare a development plan for Phase II, which will address technical risk reduction associated with developing algorithms for condition based monitoring based on sensor, transient and steady-state data, as well as performance goals of detecting and validating failure modes and key algorithm and statistical development milestones. PHASE II: Based on the results of Phase I and the Phase II development plan, the small business will be implementing the algorithms developed in Phase I within a comprehensive wireless infrastructure. The wireless infrastructure includes array of sensors common to a component node, wireless interface and self-contained power transmit and receive. The infrastructure is considered a managed network for a condition monitoring system. The company will follow a system development methodology already in progress that includes a requirements definition wherein performance, interface, security and reliability requirements are defined within a system specification. Component and software requirements specifications are developed based on the system requirements. The company will incorporate the algorithms for data processing into the infrastructure to add the diagnostics and prognostics necessary to allow for condition based monitoring at the node compatible within the wireless network. The entire infrastructure is considered a prototype and as such the company shall demonstrate that the algorithm will execute successfully and process various sensor input data defined within the system requirements and detect transient and steady-state waveforms for digital signal processing. It will be necessary to process data in the time, frequency and modal domain and allow for conversion from time domain to frequency domain using Fourier transform techniques. It will be necessary to demonstrate ability for correlation techniques of processing input data of a transfer function from operation to a known transfer function or criteria as reference. The differential will be used for deterministic as well as stochastic approach to measuring component health. Similarity sensor data will be auto-correlated to determine the solution for health status leading to actionable information. The successful system will be performance tested in situ either in a shipboard environment or laboratory depending on resources available. The testing will evaluate the analytical or model of simulated failure modes and data from actual component over a range of operational parameters to represent deployment cycles. Evaluation results will be used to refine the algorithms as necessary to meet Navy requirements. The company will prepare a Phase III development plan to transition the algorithms for Navy use. PHASE III: If the Phase II is successful, the company will be expected to support the Navy in transitioning the algorithms for Navy use should a Phase III award occur. Based on the Phase II results, the company will integrate the algorithms into a next generation wireless infrastructure for installation on a ship to conduct testing as an effective tool for diagnostics and prognostics as part of a new era in shipboard wireless processing of sensor data for machinery condition based monitoring. The processes used for the algorithm will be defined and incorporated into the wireless infrastructure requirements specification and used for procurement purposes. The company will continue to support the Navy for revision to the algorithm to improve processes necessary to monitor various systems and variants as improvements are made to enhance at node machinery condition based monitoring techniques. PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: The technology developed under this topic could be used in a wide range of industry applications, providing specific system performance information, and a method for collecting and analyzing such information, to enable a breakthrough in wireless at node condition based monitoring for shipboard environment. REFERENCES: 1. Carden, P. E., Fanning, P. (2004), “Vibration Based Condition Monitoring: A Review, Structural Health Monitoring, 355-377. 2. Goff, C. I., McNamara, C. L., Bradley, J. M., Trost, C. S., Dalton, W. J. and Jabaley, JR., M. E. (2011), “Maximizing Platform Value: Increasing VIRGINIA Class Deployments”. Naval Engineers Journal, 123: 119–139. https://www.navalengineers.org/Hamilton_Award_Papers/2011/Goff.pdf 3. Byington, Carl S., Michael J. Roemer, Gregory J. Kacprzynski, and Thomas Galie. “Prognostic Enhancements to Diagnostic Systems for Improved Condition-Based Maintenance”, 2002, DTIC Document Accession Number: ADA408880. http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA408880 4. Yick, Jennifer, Biswanath Mukherjee, and Dipak Ghosal. "Wireless sensor network survey." Computer networks 52.12 (2008): 2292-2330. http://ahvaz.ist.unomaha.edu/azad/temp/ali/08-yick-wireless-sensor-network-localization-coverage-survey-good.pdf 5. Byington, Carl S., Michael J. Roemer, Gregory J. Kacprzynski, and Thomas Galie. “Prognostic Enhancements to Diagnostic Systems for Improved Condition-Based Maintenance”, 2002, DTIC Document Accession Number: ADA408880. http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA408880 6. Loverich, Jacob J., Jeremy E. Frank, and Richard T. Geiger. "Self-powered Wireless Sensors for Condition Based Maintenance on Ships”, 2009 Intelligent Ships VIII Proceedings, May 20-21, 2009. Drexel University in Philadelphia, PA. https://navalengineers.org/SiteCollectionDocuments/2009%20Proceedings%20Documents/ISS%202009/Papers/Loverich_Frank_Geiger.pdf 7. Sinha, Jyoti; Elbhbah, Keri. “A future possibility of vibration based condition monitoring of rotating machines”, 2013, Mechanical Systems and Signal Processing, Volume 34, 231-240. 8. Chattopadhyay, Aditi, Mark Seaver, Antonio Papandreou-Suppapola, Seung B. Kim, Narayan Kovvali, Charles R. Farrar, Matt H. Triplett, and Mark M. Derriso. “A Structural Health Monitoring Workshop Roadmap for Transitioning Critical Technology from Research to Practice”, 2012, DTIC Document Accession Number: ADA554786. http://www.dtic.mil/dtic/tr/fulltext/u2/a554786.pdf KEYWORDS: Electric, hydraulic, actuator, wireless condition based monitoring; sensor, nodes, algorithm, diagnostics, prognostics, transient functions, damage accumulation, health monitoring, failure modes, detection and life-cycle N151-028 TITLE: Coastal Battlefield Reconnaissance and Analysis (COBRA) Comprehensive Model for Scene Generation, Target Injection and Sensor Performance TECHNOLOGY AREAS: Sensors, Electronics, Battlespace ACQUISITION PROGRAM: PMS495, Mine Warfare Program Office, COBRA The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 5.4.c.(8) of the solicitation. 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: Research and develop a comprehensive software scene generation, target injection and sensor performance model for COBRA. DESCRIPTION: The COBRA program (Ref 1) is interested in technologies that facilitate automated target recognition capabilities for previously unseen environments and target threats. Typically, algorithms are based on available test data sets. This may hinder the assessment and optimization of system performance in new environments and for new target threats. Such constraints may lead to outliers in the operational performance of the system when generalizations of past performance are extended to specific unseen locales and target types. To address these issues, in lieu of conducting numerous costly data collections, there is a need for a comprehensive system model that can be used to generate images simulating those that might be acquired in new environments and/or with new target types. The technologies developed under this topic will decrease costs by lowering the number of flight tests necessary for algorithm development and enable performance estimations in areas of interest where imagery is lacking. The comprehensive model to be developed will include sub-models for background terrain, targets, platform, and sensor. For potential techniques applicable to the comprehensive model, see Refs 2-5. Background terrain models can be cued from existing information sources, such as imagery collected by other airborne sensors. Target models will allow insertion of targets into the scene, including landmines and obstacles. The platform model will include aspects of the sensor platform affecting image acquisition, including platform position, orientation, and sensor pointing. A sensor model will provide a parametric, multi-spectral radiometric response given the scene radiometry generated by the other models. Imagery, metadata, and a data description will be provided to the selected contractor(s) by the Navy. PHASE I: Develop a comprehensive model architecture for the COBRA image acquisition system. Prepare conceptual designs for each model component, including targets, scene background, platform and sensor. Develop the models so they are capable of inserting targets into existing COBRA images and demonstrate the feasibility of the approach on sample images. The company will provide a Phase II development plan that addresses technical risk reduction and provides performance goals and key technical milestones. PHASE II: Based on Phase I designs and Phase II plan, implement complete scene generation and radiometric sensor models for COBRA. The scene generation model and radiometric models will be evaluated in conjunction with COBRA imagery previously collected to determine whether it can meet the performance goals defined in the Phase II development plan. Model performance will be demonstrated through prototype evaluation and detailed analysis. The company will prepare a Phase III development plan to transition the technology to Navy use. PHASE III: If the Phase II is successful, the company will be expected to support the Navy in transitioning the technology for Navy use. The company will utilize the models and software tools to improve performance of the COBRA Block I and II systems. The company will also, support updates to the COBRA Technical Data Package (TDP) to support the Navy in transitioning the design and technology into the COBRA Production baseline for future Navy use. The company will support the Navy for test and validation to certify and qualify the system for Navy use. PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: In addition to application of the Comprehensive Model in the COBRA program, the technology is directly adaptable to many commercial activities that require performance evaluation of multi-spectral remote sensing systems for applications such as forestry, agriculture, and Intelligence Preparation of the Operational Environment (IPOE). REFERENCES: 1. The US Navy -- Fact File: AN/DVS-1 Coastal Battlefield Reconnaissance and Analysis (COBRA), 2013. http://www.navy.mil/navydata/fact_display.asp?cid=2100&tid=1237&ct=2 2. Shaw, G. and Burke, H. “Spectral Imaging for Remote Sensing”, Lincoln Laboratory Journal, vol. 14, no. 1, pp. 3–28, 2003. https://www.ll.mit.edu/publications/journal/pdf/vol14_no1/14_1remotesensing.pdf 3. Fanning, J., Halford, C., Jacobs, E., and Richardson, P. (2005) "Multispectral Imager Modeling," SPIE 2005. https://www.memphis.edu/eece/cas/docs/Multispectral_Imager_Modeling_SPIE05.pdf 4. Keen, Wayne; Tanner, Michael; Coker, Charles; Crow, Dennis, GPU based synthetic scene generation for maritime environments, Technologies for Synthetic Environments: Hardware-in-the-Loop Testing XV. Edited by Buford, James A., Jr.; Murrer, Robert Lee, Jr. Proceedings of the SPIE, Volume 7663, article id. 76630O, 9 pp. (2010). 5. DIRSIG: The Digital Imaging and Remote Sensing Image Generation Model. http://dirsig.org/ Rochester Institute of Technology, Modeling and Simulations Group, 2014. KEYWORDS: Target insertion; multispectral scene generation; radiometric sensor model; Coastal Battlefield Reconnaissance and Analysis; passive multispectral; minefield detection N151-029 TITLE: Advanced Radio Magnetic Powder for Additive Manufacturing TECHNOLOGY AREAS: Materials/Processes ACQUISITION PROGRAM: PEO IWS2, PM SEWIP The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 5.4.c.(8) of the solicitation. 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: To develop an additive manufacturing process for low loss, high index, and high wave characteristic impedance magnetic powder utilizing breakthrough technology to improve Navy Electronic Warfare (EW) systems. DESCRIPTION: The Navy has a need to protect antennas from weather elements while seeking performance gains with advanced radome designs. Additively manufactured low loss, high index, and high characteristic impedance magnetic powders into shipboard materials could enhance the electromagnetic capabilities of these systems. The use of additive manufactured magnetic powders improves the structure materials’ protecting antenna while enhancing the antenna capabilities through possible three-dimensional electromagnetic properties. Recent advances in additive manufacturing have provided the enabling technology required to integrate these powder designs for the radome structures into the antenna systems. The Navy is seeking a process to introduce magnetic powders with particular electromagnetic characteristics that are not commercially available, described below, which can be additively manufactured in 2 ½ or 3 dimensional designs. A successful powder shall leverage additive manufacturing procedures used by industry and the Navy (Ref 1). The ideal solution would be applicable to many naval applications and across a large frequency range. The combination of a high wave impedance magnetic powder and low wave impedance of the host medium should yield near free space impedance. The company will assist the Navy in transitioning in radomes with dielectric or magnetic powders inserted via additive manufacturing to increase antenna power for Navy use. The concept for an additively manufactured material should operate in the 10-1000 megahertz (MHz) range with a band maximum electric and magnetic loss factor of 0.05, a band minimum index of refraction of 5, and near free space wave impedance at an operating range (5-10%). The effective properties of the magnetic powder and host medium can be modeled using effective media formulas (Ref 2). All approved materials will need to meet Navy Shipboard requirements for fire, smoke, and toxicity (Ref 3). PHASE I: Develop a formulation and manufacturing process for magnetic powder that meets the requirements described in the topic description. Demonstrate the capability to produce a new radome design utilizing magnetic powders in an additive manufacturing process to increase performance as well as the feasibility to utilize dielectric powders in lieu of magnetic powders to meet evolving Navy needs. PHASE II: Based on the results of Phase I and the Phase II contract statement of work, develop a 2½ or 3 dimensional varying prototype for evaluation that meets the description requirements. The prototype will be evaluated to determine its capability to meet future Navy needs for advanced radome powders. The prototype will be refined by the characterization of samples with homogeneous electromagnetic properties. Transmission line or free space measurement results of these homogeneous samples will be used to refine the prototype into an initial design that will meet Navy requirements with accompanying cost benefit analysis. The company will prepare a Phase III development plan to transition the technology for Navy use. PHASE III: Support the Navy in transitioning in radomes with dielectric or magnetic powders inserted via additive manufacturing to increase antenna power for Navy use. Support the Navy for test and validation to certify and qualify the system for Navy use and transition to intended PEO IWS2. PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: The potential for commercial applications include miniaturization of antennas. This system will reduce the footprint of many antennas used for industry and other Department of Defense (DOD) applications. The use of additive manufactured magnetic powders improves the structure materials protecting antenna while enhancing the antenna capabilities though possible three dimensional electromagnetic properties. REFERENCES: 1. D. Roper, B. Good, R McCauley, S. Yarlagadda, J. Smith, A. Good, P. Pa, M. Mirotznik, “Additive Manufacturing of Graded Dielectrics,” Smart Materials and Structures; March 2014. |