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Operational Training for FFG-7 Anti Air Warfare (AAW) Combat System

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N/A
Agency Tracking Number: 32417
Amount: $67,630.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Timeline
Solicitation Year: N/A
Award Year: 1996
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
17010 Dah1gren Road Suite 6
King George, VA 22485
United States
DUNS: N/A
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Daniel E. Phillips
 (540) 663-3321
Business Contact
Phone: () -
Research Institution
N/A
Abstract

A need exists to develop an embedded training capability for AAW teams for the FFG-7 class to support the Navy's program of moving training to the operator's console on the ship. This method of using embedded training to "self-train" has been adopted as the prescribed means of accomplishing individual and team proficiency for all surface combatants. For various reasons, embedded training capability on FFG-7 class ships has not been funded. The most recent initiative, FAST (Frigate AAW System Trainer), proved to be too expensive. The challenge is to develop an inexpensive on-board team training capability which will encompass the detect-to-engage process and shall include the following equipments: AN/SPS-49 air search radar, AN/SPS-55 surface search radar, MK 92 fire control system, MK12 IFF, and SYS 2 integrated sensor signal processor. The BCI approach will be to leverage ongoing or previously completed development in the areas of: scenario generation and control, data collection, reconstruction analysis & debrief, stimulation, simulation, and advanced embedded training tools & technologies. Using this other R&D should provide a useful foundation for design analysis in order to produce an on-board training capability within the required limit of $200K per unit. BCVs extensive experience in AEGIS cruiser and destroyer embedded training design, development, and implementation will provide a critical predictor for success.

* Information listed above is at the time of submission. *

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