Artificial Intelligence/Machine Learning, ASA(ALT), Phase I
Armament System AI Data Logger & Architecture
Objective
The objective of this Phase I topic is to collect, enable real-time transmission and archival of armaments usage data across all platforms for current and future AI developments. Data, such as shock, vibration, temperature, humidity, atmospheric pressure, and other useful data. The data logger allows for off network data collection, ensuring 365/24/7 data collection. This data will allow AI algorithms to identify or predict critical operational use cases (round count, tube wear, blast over pressure). Usage areas include operational decisions, training, future R&D optimization, situational awareness, logistics & maintenance.
Description
The purpose of this Phase I topic is to collect, transmit and archive data from armament systems (artillery, mortars, crew served, remote, squad) for use in AI/ML applications. Please see the objective for usage areas. The data collected can be used for many areas across the armaments lifecycle for current and future unknown application. The topic should eventually aid in the development of a robust AI data architecture and repository strategy and identify potential AI/ML development efforts based on data collection and architecture. Currently, there is limited data collected through log books and some SW usage logs. Battlefield networks limit the ability to transmit the data real time, but no limitations are in place to collect data for future use beyond SWAP concerns. Sensor integration and SWAP reductions allow for more sensors to be utilized without effecting armaments operations. Ability to conduct AI/ML on the edge will allow data consumption. This supports armaments operations both on the battlefield and off (Training, Situational Awareness, Battlefield Decisions, R&D optimization, Logistics and Maintenance), If successful, armament systems and their operators will be more effective and reduce the time to neutralize a threat. It will also greatly impact the logistics, maintenance and future R&D cycles by utilizing actual usage data rather than estimated.
Phase I
In order to be successful in your Phase I submission, the following must be demonstrated: Identify sensors and data criteria (resolution & sample rate), propose data architecture and strategy, including data storage and transfer methods, and identify potential AI/ML development efforts based on data collection and architecture
Phase II
In order to be successful in your Phase II submission, the following must be demonstrated: Develop base data logger module and data architecture with repository for armament systems and develop specific data logger module for extended range munitions applications
Phase III
In order to be successful in your Phase III submission, the following must be demonstrated: Develop Extended Range Cannon Artillery (ERCA) based data logger with on the edge AI/ML modules with collected data specific to armament application.
For more information, and to submit your full proposal package, visit the DSIP Portal.
References:
Russell, Stephen, and Tarek Abdelzaher. “The internet of battlefield things: the next generation of command, control, communications and intelligence (C3I) decision-making.” MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM). IEEE, 2018 “Utilizing Low Cost Sensors on Mortar Platforms for Fire Control Applications”, R. Tillinghast, G. Byrne, S. Sadowski, A. Yu, & M. Wright. Proceedings: NDIA Armaments Systems Forum, Scheduled for April 2016 Iyer, Brijesh, and Niket Patil. “IoT enabled tracking and monitoring sensor for military applications.” International Journal of System Assurance Engineering and Management 9.6 (2018): 1294-1301.
Objective
The objective of this Phase I topic is to collect, enable real-time transmission and archival of armaments usage data across all platforms for current and future AI developments. Data, such as shock, vibration, temperature, humidity, atmospheric pressure, and other useful data. The data logger allows for off network data collection, ensuring 365/24/7 data collection. This data will allow AI algorithms to identify or predict critical operational use cases (round count, tube wear, blast over pressure). Usage areas include operational decisions, training, future R&D optimization, situational awareness, logistics & maintenance.
Description
The purpose of this Phase I topic is to collect, transmit and archive data from armament systems (artillery, mortars, crew served, remote, squad) for use in AI/ML applications. Please see the objective for usage areas. The data collected can be used for many areas across the armaments lifecycle for current and future unknown application. The topic should eventually aid in the development of a robust AI data architecture and repository strategy and identify potential AI/ML development efforts based on data collection and architecture. Currently, there is limited data collected through log books and some SW usage logs. Battlefield networks limit the ability to transmit the data real time, but no limitations are in place to collect data for future use beyond SWAP concerns. Sensor integration and SWAP reductions allow for more sensors to be utilized without effecting armaments operations. Ability to conduct AI/ML on the edge will allow data consumption. This supports armaments operations both on the battlefield and off (Training, Situational Awareness, Battlefield Decisions, R&D optimization, Logistics and Maintenance), If successful, armament systems and their operators will be more effective and reduce the time to neutralize a threat. It will also greatly impact the logistics, maintenance and future R&D cycles by utilizing actual usage data rather than estimated.
Phase I
In order to be successful in your Phase I submission, the following must be demonstrated: Identify sensors and data criteria (resolution & sample rate), propose data architecture and strategy, including data storage and transfer methods, and identify potential AI/ML development efforts based on data collection and architecture
Phase II
In order to be successful in your Phase II submission, the following must be demonstrated: Develop base data logger module and data architecture with repository for armament systems and develop specific data logger module for extended range munitions applications
Phase III
In order to be successful in your Phase III submission, the following must be demonstrated: Develop Extended Range Cannon Artillery (ERCA) based data logger with on the edge AI/ML modules with collected data specific to armament application.
For more information, and to submit your full proposal package, visit the DSIP Portal.
References:
Russell, Stephen, and Tarek Abdelzaher. “The internet of battlefield things: the next generation of command, control, communications and intelligence (C3I) decision-making.” MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM). IEEE, 2018 “Utilizing Low Cost Sensors on Mortar Platforms for Fire Control Applications”, R. Tillinghast, G. Byrne, S. Sadowski, A. Yu, & M. Wright. Proceedings: NDIA Armaments Systems Forum, Scheduled for April 2016 Iyer, Brijesh, and Niket Patil. “IoT enabled tracking and monitoring sensor for military applications.” International Journal of System Assurance Engineering and Management 9.6 (2018): 1294-1301.