N141-009 TITLE: Autonomous Environmental Sensor Performance Prediction Tool for Multi-Static Active and Passive Anti-Submarine Warfare (ASW) Systems

TECHNOLOGY AREAS: Sensors, Battlespace

ACQUISITION PROGRAM: PMA 264

RESTRICTION ON PERFORMANCE BY FOREIGN CITIZENS (i.e., those holding non-U.S. Passports): This topic is "ITAR Restricted". The information and materials provided pursuant to or resulting from this topic are restricted under the International Traffic in Arms Regulations (ITAR), 22 CFR Parts 120-130, which control the export of defense-related material and services, including the export of sensitive technical data. Foreign Citizens may perform work under an award resulting from this topic only if they hold the "Permanent Resident Card", or are designated as "Protected Individuals" as defined by 8 U.S.C. 1324b(a)(3). If a proposal for this topic contains participation by a foreign citizen who is not in one of the above two categories, the proposal will be rejected.

OBJECTIVE: Develop an autonomous Anti-Submarine Warfare (ASW) sensor performance prediction tool that utilizes measured and predicted ocean environmental data retrieved via a network interface.

DESCRIPTION: Air ASW sensor systems like the Multi-static Active Coherent "MAC" (SSQ-125) sonobuoy source and ADAR (SSQ-101A/B) receiver to provide coherent pulses and waveform flexibility like doppler-speed sensitive and frequency modulated (FM) clutter suppression. The number of operational settings for this system can be quite large requiring thousands of permutations to optimize a single sonobuoy search area. It can be difficult to determine exactly which settings provide the best optimal detection capability for any given operational area. To solve this problem, a Air ASW multi-static active and passive modeling and simulation tool needs to be developed to help operators determine the best operating performance for various ASW sensor systems.

The tool should model drifting ocean currents and calculate and characterize ocean environments for optimal use of naval ASW sensors and provide the best successful operational recommendation for naval ASW sensor use. The tool should reiterate automatically with a new set of parameters while trying to optimize the probability of detection, signal excess coverage, and detection range. The tool should be able to run multiple calculations and predictions for many locations, times of year, pattern variations, including sonobuoy spacing, source depths, receiver depths, and sonobuoy pulse definitions such as pulse length and pulse type. The tool should use the data autonomously to determine the optimized acoustic parameters for sonobuoy patterns in active and passive fields. The tool should be able to ingest in-situ measured data from the Air community, such as forecasted sound speed from bathymetry (BT) measurements, wind speed, drift, and ambient noise. These measured and predicted ocean environmental parameters should be retrieved via an interface that would be used to populate the performance prediction tool. Autonomous performance prediction should be provided by interfacing with the Naval Oceanographic Office environmental database to obtain sound speed profile overlay, ambient noise, shipping level, sonobuoy drift overlays, and other oceanographic environmental data. The performance prediction results including sonobuoy depth and spacing pattern recommendations, ping plan strategies, and pulse settings should be stored in a database management system for retrieval via web access. The most optimized deployable patterns in an area should be instantaneously provided via the database management system. The tool should learn from itself by analyzing newly predicted or measured data against previously modeled scenarios. If the input environmental data matches a previously modeled event for a given location and time of year, then the system should obtain the result from the data management system and not recalculate performance predictions that are known. Due to constant changes in weather patterns, temperatures, and ocean currents, historical data should be replaced with data that is reflective of the current or the Naval Oceanographic Office predicted environment. As new environmentally measured or forecasted data becomes available, the tool should automatically optimize sensor performance measurements for various operational areas. The tool should quickly maximize the probability of detection, signal excess, detection range, signal excess surface area map, and number of detection opportunities. The tool should produce recommendations for optimized sonobuoy pattern placement, sensor depth settings, detection range, signal excess and ping plans on an ongoing 24 hours 7 days a week basis. The tool should support multi-static active systems, narrowband passive prediction and detection range modeling, and run in batch mode and utilize parallel processing techniques. The tool should also be able to handle ocean currents and drifting of sonobuoy systems.

Users should be able to select the location (latitude/longitude), month, source depth, receiver depth, target/threat type, threat depth, ping rate, aircraft parameters, sonobuoy pattern geometery, and pulse definitions (Triple Hyperbolic Frequency Modulation, Hyperbolic Frequency Modulation, Continuous Wave) to use for mission planning and derive a performance prediction estimate called probability of detection (PD). Users should also be able to add environmental overlays, such as in-situ sound speed measurements, the Naval Oceanographic Office predicted sound speed files, known as wavelets, should be integrated. By automating the acoustic modeling and simulation for Air ASW sensor performance and using current, relevant, and future-casted environmental data, the resulting performance predictions should be more reflective of the true environment and greatly increase the probability of success when ASW assets are deployed to detect, track, and localize adversarial threats.

PHASE I: Determine feasibility and design a concept for a multi-static active and passive modeling and simulation tool for ASW systems.

PHASE II: Develop, demonstrate and validate the concept that was designed in Phase I.

PHASE III: Complete testing and transition tool to the applicable platforms.

PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: The system could be used commercially to quickly model the ocean environment using sound speed and ambient noise measurements to enhance marine mammal mitigation and detection and tracking of fish.

REFERENCES:
1. Urick, R. J. (1967). Principles of Underwater Sound for Engineers (1st ed.). New York: McGraw-Hill Book Company.

2. U.S. Naval Air Development Center. (1965). Lecture Notes on Underwater Acoustics (Air Warfare Research Department Report No. NADC-WR-6509). Johnsville, PA: Bartberger, C. http://www.dtic.mil/dtic/tr/fulltext/u2/468869.pdf

3. Tolstoy, I., & Clay, C. (1987). Ocean Acoustics: Theory and Experiment in Underwater Sound. Acoustical Society of America.

4. Horton, J. W. (1959). Fundamentals of Sonar (2nd ed.). Annapolis: United States Naval Institute Press.

5. Horton, C.W. (1969). Signal Processing of Underwater Acoustic Waves (1st ed.) U. S. Government Printing Office.

6. Burdic, W.S. (2002) Underwater Acoustics System Analysis (2nd ed.). Los Altos: Peninsula Publishing.

7. Lurton, X. (2010). An Introduction to Underwater Acoustics: Principles and Applications (2nd ed.) Chinchester: Springer Praxis Books.

KEYWORDS: Simulation; Modeling; Asw; Sonobuoy; Acoustic Propagation; Acoustic Detection

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