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AquaQuIPS Multi-INT Data Fusion in a Cloud
Navy SBIR FY2013.2
| Sol No.: |
Navy SBIR FY2013.2 |
| Topic No.: |
N132-135 |
| Topic Title: |
AquaQuIPS Multi-INT Data Fusion in a Cloud |
| Proposal No.: |
N132-135-0310 |
| Firm: |
Jove Sciences, Inc.
3834 Vista Azul
San Clemente, California 92672 |
| Contact: |
James Wilson |
| Phone: |
(949) 366-6554 |
| Abstract: |
The objective of this proposal is to design software for the very successful multi-INT AquaQuIPS (AQ) data fusion processor in a Navy Tactical Cloud (NTC) environment at AQ's SPAWAR processor location using the new 384 plus node super computer. Tasks proposed are:
Base Task 1A: Design and initially test a HADOOP/MapReduce based AQ sensor adaptor for a selected group of the 20+ existing AQ sensors depicted.
Base Task 1B: Design and initially test the "New Position" AQ data fusion module when it is developed for a NTC distributed processing environment. The existing AQ position track fusion module that is a single server "bottleneck", and the AQ DFP processing speed is expected to increase by a factor of ~ 30 when this task is completed.
Base Task 2: Design a Hybrid Relational Data Base/Key-Value (H-RDB/KV) distributed, massive parallel processing AQ Data Fusion Processor (DFP) for the NTC using as many processing nodes as possible for automating an Abnormal Behavior (AB) design using sensor sources available for selected events.
Option Task 1: Based on the Base Task results, AQ Team will produce an integration design for the PACOM JIOC and DCGS-N programs to enhance their performance.
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| Benefits: |
The capability developed is exactly what is critically needed in all eight operational COCOMs, and the PACOM JIOC use of AQ will be used as a template for other COCOMs, even though their missions are different. All COCOMs have surface ship threats and must have a capability like AQ on the NTC to have time to make an accurate interdict or kill decision. AQ on a NTC is also a valuable asset for Denied Areas missions where the US or its Allies cannot have an overt presence. One example is off islands in Indonesia and the Philippines where terrorist groups are building up bases of operations by sea. A second example is the detection, tracking, and classification of Self Powered Semi Submersibles (SPSSs)and Self Powered Fully Submersibles (SPFSs) in which DoD, USCG, and other intelligence agencies are deeply invested currently.
Ironically an application for AQ on the NTC will be most valuable for ASW missions. In crowded coastal environments there are way too many sonar LoB vs. time tracks to manually assess, and the sonarman desperately needs an automated system like AQ on the NTC to correlate non surface ship tracks with sonar LoB vs. time tracks so that these non ASW threats can be automatically "deliminated" as possible submarines. For the hopefully few sonar tracks remaining without a surface ship acoustic "partner", AQ can be used to "Fly on Top" of the possible submarines acoustic bearing sector from the Blue submaring or surface ASW platform.
Beyond the Navy applications mentioned above, the US Coast Guard, Air Force, all Port Security Agencies in the world; Merchant Marine agencies, commercial ship routing companies, individual shipping owners, Coalition Partner Navies, Customs & Border Patrol, the Air and Marine Operations Center (AMOC) at March Air Force Base, the Army's Joint IED Defeat Organization (JIEDDO), CIA, FBI, and many more customers are in critical need of autonomous acoustic sensors and acoustic/non acoustic track data fusion capabilities right now. Having identified the above potential customers above, each mission will require a well focused business plan that addresses unique customer needs. The limited space in this proposal will not allow specific examples of these business plan summaries, but EMCON Silent threats of all types (submarines, SPSSs, Pirate Ships, etc.) are best detected by active sensors, such as acoustic arrays and radar, and then data fused by AquaQuIPS' s (AQ's) complete surface ship track picture. AQ. The non acoustic part of AQ is already transitioning well in DoD and USCG and is TRL 9. The tasks proposed here will allow the acoustic algorithms in the Prater Data Fusion Tree to receive similar development.
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