N221-010 TITLE: Magnetometer Classification of Underwater Objects

OUSD (R&E) MODERNIZATION PRIORITY: General Warfighting Requirements (GWR)

TECHNOLOGY AREA(S): Air Platforms

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 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: Design and develop a system using existing sensors and real-time signal-processing algorithms for classification of underwater objects.

DESCRIPTION: Modern frontline submarines pose a threat to maritime strategy and naval operations. The proliferation of acoustic quieting techniques has severely decreased the detection range of passive acoustic sensors. Active acoustic sensors can provide longer range, however they do not provide classification of the target.

New compact sensors provide an opportunity to develop improved real-time detection, target parameter extraction, target localization, prosecution/tracking, and classification of underwater objects. Advanced signal processing techniques may generate classification from a variety of sources, including magnetometers; magnetic dipoles; and extremely low frequency (ELF), ultralow frequency (ULF), electric fields (E-Fields); or other sources of opportunity, such as magnetotelluric or very low frequency radio wave sources. The desired end-product is a system for classification of underwater objects consisting of the magnetometers (< 4) and an open architecture, software module with the necessary algorithms to process sensor data and conduct data fusion for classification. It will be requested for the software to process the data in real time, that is, capable of providing a result analysis and display of the data while data is being taken. The magnetometer will be an onboard system vice a towed magnetometer. Developed software must be compatible with platform architecture, i.e., Joint Mission Planning System (JMPS).

Examples of features the system should account for include, but are not limited to:

• geo-location coordinates
• date and time stamp
• platform altitude
• graphical user interface
• probability of detection
• false alarm rate
• geologic, geo-atmospheric, oceanic, and platform noise characteristics
• data fusion

If the target is identified as a mobile submerged target, the following features should be identified:

• target course, speed, and depth parameters
• target length
• target diameter
• target screw turn rate
• target screw configuration (i.e., 5-bladed screw)

If the target is determined to be a submarine, the following features should be identified:

• submarine sail length and location
• submarine type

Magnetic Anomaly Detection (MAD) sensor metrics include, but are not limited to:

• noise floor: < 0.35 pT/rt Hz from 0.01�100 Hz
• magnetometer: able to operate in all of Earth's field orientations and magnitudes
• magnetometer: not sensitive to motion-induced measurement errors or the MAD system must be able to compensate for motion-induced measurement errors
• cost per system: Objective < $2,000, Threshold < $10,000
• volume: < 100 cc (sensor Head), < 500 cc (electronic module)
• power: Objective < 1 W, Threshold < 5 W

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

PHASE I: Demonstrate a conceptual design of real-time, open architecture software signal processing algorithms to achieve classification of underwater objects using commercial off-the-shelf magnetometer sensors onboard rotorcraft. The Phase I effort will include prototype plans to be developed under Phase II. Awardees should provide NAVAIR a white paper of the developed or implemented theory.

PHASE II: Develop a candidate prototype for real-time magnetometer sensor and signal processing for classification of underwater objects using an open architecture software. Perform algorithm testing and performance validation using simulated processed signals and actual data. Refine the software, integrate it with the proposed sensors and a commercial magnetometer in the laboratory (or other location), and demonstrate the system classification performance. Use of fictitious classification data is acceptable.) Conduct a flight test to demonstrate the prototype magnetometer sensor and signal processing for classification of underwater objects against a target of opportunity (surface ship if the target of opportunity is not available). Demonstration parameters should be approved by the Technical Point of Contact (TPOC). Deliver the classification of underwater objects prototype to the Government for use as a laboratory demonstration model.

Work in Phase II may become classified. Please see note in Description paragraph.

PHASE III DUAL USE APPLICATIONS: Final transition of this project will involve implementation of the software to JMPS or other hardware architecture specific to the platform. In addition, noise models tailored to the requested platform should be developed. Final testing will involve compilation of real data in the platform, processing of the data in the software, analysis of software results with those simulated with probability models created by software, and measurement of software effectiveness. End results should provide detection, localization, and classification of a target with a signal of around 100 to 101 pT/rt Hz.

Development of a magnetic detection capability that can be implemented and, potentially, sold to different airborne platforms for the detection of unknown magnetic targets hidden underground or at sea. Possible industries include military, security, atmospheric, and surveillance.

REFERENCES:

1. Ben-Kish, A., & Romalis, M. V. (2010). Dead-zone-free atomic magnetometry with simultaneous excitation of orientation and alignment resonances. Physical review letters, 105(19), 193601. https://doi.org/10.1103/PhysRevLett.105.193601.
2. Bickel, S. H. (1979). Small signal compensation of magnetic fields resulting from aircraft manuvers. IEEE Transactions on aerospace and electronic systems, (4), 518-525. https://doi.org/10.1109/TAES.1979.308736.
3. Shah, V., Knappe, S., Schwindt, P. D., & Kitching, J. (2007). Subpicotesla atomic magnetometry with a microfabricated vapour cell. Nature Photonics, 1(11), 649-652. https://www.nist.gov/publications/subpicotesla-atomic-magnetometry-microfabricated-vapour-cell.
4. Department of Defense. (2006, February 28). DoD 5220.22-M National Industrial Security Program Operating Manual (Incorporating Change 2, May 18, 2016). Department of Defense. https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodm/522022m.pdf.

KEYWORDS: Electromagnetic Detection; Underwater Object Classification; Signal Processing; Rotorcraft; Magnetometer; Object Vector Characterization