Adaptive Narrowband Trainer

Navy SBIR 21.1 - Topic N211-065
NAVSEA - Naval Sea Systems Command - Mr. Dean Putnam - dean.r.putnam@navy.mil
Opens: January 14, 2021 - Closes: February 18, 2021 (12:00pm EDT)

N211-065 TITLE: Adaptive Narrowband Trainer

RT&L FOCUS AREA(S): General Warfighting Requirements

TECHNOLOGY AREA(S): Human Systems

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 3.5 of 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: Develop an adaptive narrowband trainer that leverages artificial intelligence (AI), machine learning (ML), and real-world elemental playback data to detect, analyze, and classify real-world Passive Narrowband (PNB) signatures of submarine contacts.

DESCRIPTION: As adversary submarine forces field platforms that are increasingly capable and stealthy, the ability to detect these ships becomes ever more challenging. The ability to pace the threat with newer sensors and processing systems is part of the solution, but sonar sensor operators require better tools to effectively exploit the new sensor and system capabilities, specifically regarding narrowband detection and analysis. Relatively few training products address narrowband detection and analysis. Those narrowband training products that exist fail to leverage adaptive training, and Artificial Intelligence and Machine Learning (AI/ML). Existing interfaces to the latest tactical systems are limited, and existing narrowband trainers are not equipped for updates to represent emerging narrowband phenomena.

Sailor evaluation of PNB signals is particularly critical in successful Anti-Submarine Warfare (ASW) prosecutions. Depending on the sensor suite available, passive narrowband signals to be analyzed can be hidden across hundreds of beams of bearing and hundreds of frequency bins per line of bearing. Identifying specific signals associated with undersea threats in a timely manner is both a difficult skill to acquire and a skill that is highly perishable when use or effective training is not constant.

The Navy seeks an innovative narrowband trainer that adapts to emerging threats and maximizes accelerated learning. Essential elements of the trainer include a training or gaming platform that is both engaging and instructive. The trainer would ideally be accessed through the Moodle Learning Management System (LMS) resident on the respective shipboard tactical system, and capture operator performance data by leveraging experience API (xAPI) and the learning record store (LRS) resident on the tactical system. The trainer may also be accessed by ashore trainers such as the Submarine Multi-mission Team Trainer (SMMTT), the Multifunctional Instructional Trainer (MIT), the Applied Classroom (ACR) or the Virtual Operational Team Trainer (VOTT).

The trainer should be designed to include updateable real world elemental acoustic data provided by the Navy as a stimulus for the tactical displays the operator observes. The trainer should employ adaptive training techniques that will progress the student depending upon his/her level of knowledge and level of performance. In cases where the student is more proficient, that student should progress faster through the scenario and not be required to complete tasks associated with lower levels of knowledge or proficiency. The trainer should employ AI/ML to increase the challenge to the student according to the student’s capabilities and to facilitate a rapid learning curve. The trainer should provide feedback to the student, both in the form of dynamic hinting during the scenario and a post exercise evaluation or after action report that is referenced to an approved performance standard for better objectivity. The trainer should represent all display surfaces for data analysis and be able to represent the state of automation present in the build of sonar analysis software the sailor is tasked to use.

Additionally, the new training capability should leverage best practices in adaptive training. According to Metzler-Baddeley [Ref 2], "adaptive training used study time more efficiently than the chosen control conditions that is participants did not waste time studying items they already knew and were able to concentrate on items that required more training." In turn, the trainer would maximize the operator’s time and increase training efficiency. According to Forsyth et. al [Ref 3], "adaptive learning recognizes the pace of student learning varies and provides instructors with the tools needed to relieve the time pressure of increased enrollment to reach students where they are in the learning process to enhance both student and teacher effectiveness." Applying new adaptive learning approaches would benefit not only sonar operators at sea, but would also provide the schoolhouses with an additional resource for classrooms ashore. In addition, Sailor 2025’s Ready Relevant Learning initiative focuses on developing a "learning continuum where training is delivered by modern methods to enable faster learning and better knowledge retention at multiple points throughout a career." Adaptive training would provide an excellent modern solution to this problem.

This SBIR topic addresses CNO’s desire to achieve "high velocity learning at every level" and supports Sailor 2025. This topic would seek to apply the best concepts, techniques and technologies to accelerate learning for individuals, teams, and organizations.

Additionally, this topic addresses current training requirements identified in the most recent Submarine Tactical Requirements Group Advanced Development Prioritized Focus Area letter specifically requesting "embedded training systems should be adaptive to the skill level of the trainee and …real world elemental data should be capable of playback…allows for continuous learning on and off watch".

Finally, this topic addresses current training requirements identified in the most recent AN/SQQ89 Advanced Capability Build Prioritized Focus Areas letter, to wit: "System training for operators should be readily available and delivered using modern training techniques and adaptive system training…"

Sailor evaluation of passive narrowband signals is particularly critical in successful ASW prosecutions.

Initial testing of this trainer can be accomplished at the company site given the prerequisite to provide a representative simulation of narrowband acoustic information that would be resident on a tactical system, tactical trainer or virtualized training simulator. Final testing and certification would be accomplished at the prime system integrator site or a site that contains a representative tactical system simulator with an installed Moodle Learning Management System. Initial testing would be conducted by the developer with Government/Government-designated representatives. Final testing and certification would be conducted by Government/Government-designated representatives in concert with Naval surface and submarine force active duty sonar operators.

Metrics used to assess the learning training capability will refer to learning gain, successful assessment of the adaptive training algorithm, and an acceptable usability score using the System Usability Scale (SUS). The Kirkpatrick Four-Level Training Evaluation Model will be used to objectively measure the effectiveness of training. The learning narrowband trainer must be able to integrate into the Moodle LMS as well as the learning record store (LRS) present in the learning architecture embedded in the tactical system of submarines and surface ships.

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 Security Agency (DCSA). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this contract as set forth by DCSA and NAVSEA 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 advance phases of this contract.

PHASE I: Develop a concept for an adaptive narrowband trainer utilizing AI/ML to teach and reinforce passive narrowband signature recognition. Demonstrate the concept feasibly meets all the parameters detailed in the Description through modeling and analysis. Also, demonstrate the concept can operate in the Moodle LMS, experience API (xAPI), and the LRS discussed in the Description. The Phase I Option, if exercised, will include the initial design specifications and capabilities description to build a prototype solution in Phase II.

PHASE II: Develop and deliver a prototype adaptive narrowband trainer for testing by ASW personnel in the Fleet. Demonstrate prototype performance through the required range of parameters in the Description. The Government or the company will provide facilities for testing and validating the prototype.

It is probable that the work under this effort will be classified under Phase II (see Description section for details).

PHASE III DUAL USE APPLICATIONS: Support the Navy in transitioning the technology to Navy use through system integration and qualification testing for the adaptive narrowband trainer prototype. Assist in transition and integration of the prototype to a future Advanced Processing Build (APB) Combat System with the potential integration including the Advanced Capabilities Build (ACB).

The narrowband training capability can be adapted to other technical fields that involve operator assessment of faint signals, found in electronic communications, medical diagnostic tools, and other engineering disciplines that deal with oscillating signals. Adaptive learning and AI/ML are innovative approaches that would be useful to the wider education and business communities as a whole.

REFERENCES:

  1. Forsyth, B., Kimble, C., Birch, J., Deel, G. and Brauer, T. "Maximizing the Adaptive Learning Technology Experience." Journal of Higher Education Theory & Practice, 16(4), 2016, pp. 80-88. https://articlegateway.com/index.php/JHETP/article/view/1992/1892
  2. Metzler-Baddeley, C. and Baddeley, R. J. "Does adaptive training work?" Applied Cognitive Psychology, 23(2), 2009, pp.254-266. https://doi.org/10.1002/acp.1454
  3. "AN/SQQ-89(V) Undersea Warfare / Anti-Submarine Warfare Combat System." United States Navy Fact File, 15 January 2019. https://www.navy.mil/navydata/fact_display.asp?cid=2100&tid=318&ct=2
  4. Isson, Jean Paul and Harriott, Jesse. "People analytics in the era of big data: changing the way you attract, acquire, develop, and retain talent." Wiley & Sons, Inc., 2016. https://www.worldcat.org/title/people-analytics-in-the-era-of-big-data-changing-the-way-you-attract-acquire-develop-and-retain-talent/oclc/991078367&referer
  5. Abraham, Douglas A. and Siderius, Martin. "Detecting Signals with Known Form: Matched Filters." ASA Press: Underwater Acoustic Signal Processing: Modeling, Detection, and Estimation, Springer, 2019. https://www.worldcat.org/title/underwater-acoustic-signal-processing-modeling-detection-and-estimation/oclc/1099924371&referer=brief_results

KEYWORDS: Narrowband Training Products; Adaptive Training Techniques; Ready, Relevant Learning; Artificial Intelligence; Machine Learning; Kirkpatrick Model.

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