Position, Navigation, and Timing, ASA(ALT), Direct to Phase II
Novel Positioning, Navigation and Timing Signal Classification Techniques
Direct to Phase II Selectees
Objective
The Army wants to develop the capability to classify signals in real-time that impact navigation systems. Through the solicitation, the Army will better understand the types of signals experienced in relevant environments to appropriately apply mitigation techniques to minimize harm. Currently, navigation systems depend on Radio Frequency signals that can experience interferences.
Quickly understanding signal characteristics to react to and mitigate negative impacts remains a challenge. Current antenna technologies treat all signals as the same and attempt to ignore them equally. With more sophisticated interference sources, this is not always successful. However, if the Army can identify the technique used to interfere with navigation, it can implement more impactful mitigation methods.
Description
This effort provides a risk reduction approach to improve performance, provide cost savings and expand the application of the technology sensor solution set that includes additional Army aviation assets. It seeks to demonstrate novel adaptive learning techniques to perform Position, Navigation and Timing signal classification of the battlefield environment.
The proposed topic seeks to build upon Artificial Intelligence/Machine Learning algorithm technologies. We have seen progress throughout the community in demonstrating the ability to classify signals using AI/ML.
This topic will also expand on the progress and move towards real-time signal classification. ML approaches allow adaptability in the detection process that can identify new unknown interference sources. These new signal types can subsequently train an antenna system without requiring an upgrade. This will allow faster decisions, providing more protection for the navigation system.
Phase I
This topic accepts Direct to Phase II proposals. Proposers interested in submitting a DP2 proposal must provide documentation to substantiate that the vendor met the scientific, technical merit and feasibility equivalent requirements to a Phase I. Documentation can include data, reports, specific measurements and success criteria of a prototype.
Phase II
Vendors must provide:
- Two antenna systems capable of detecting, classifying interference signals in real-time and protecting the navigation solution from harm.
- Data collection of relevant signals, training the AI/ML solution and successfully demonstrating the ability to detect and identify the signal types in a relevant environment.
The antenna design must allow for portable AI/ML training techniques that can move from one antenna system to another. This will help upgrade the antenna systems to manage new signals and support other antenna systems in the same environment.
The demonstration antenna system consists of antenna elements, antenna electronics and associated AI/ML algorithms (hardware and software solutions). The Army intends to assess these antenna systems in a relevant environment.
Phase III
- The military and private sector have used advanced PNT technologies for decades. PNT firms can fall into two categories: emerging and legacy. Legacy PNT firms focus on Global Positioning Systems-enabled tech and inertial guidance systems. Emerging PNT organizations focus on major enhancements to existing systems or entirely original approaches.
- End-users for the PNT technology market span multiple sectors.
- The defense market consists of land, air, space and naval with applications for autonomous vehicles, drones, and satellites.
- Government and civil applications include traffic management, rail control, disaster management and other critical government infrastructure.
- Commercial applications include transport and logistics, aviation, marine, agriculture, mobile mapping and surveying.
Submission information
All eligible businesses must submit proposals by noon p.m. Eastern Time.
To view full solicitation details, click here.
For more information, and to submit your full proposal package, visit the DSIP Portal.
SBIR|STTR Help Desk: usarmy.sbirsttr@army.mil
References:
- O’Shea, T. Roy, T. C. Clancy, “Over-the-Air Deep Learning Based Radio Signal Classification,” IEEE J. of Selected Topics in Signal Processing, 12(1), Feb. 2018, pp. 168-179
- https://arxiv.org/pdf/1712.04578
- R. Conlin, et al., “Keras2c: A Library for Converting Keras Neural Networks to Real-time Compatible C,” Engineering Appl. of Artificial Intelligence, v. 100, April 2021, 104182
- https://www.sciencedirect.com/science/article/abs/pii/S0952197621000294
- R. Morales Ferre, et al., “Jammer Classification in GNSS Bands Via ML Algorithms,” Sensors, 2019, doi:10.3390/s19224841
- https://www.researchgate.net/publication/337096847_Jammer_Classification_in_GNSS_Bands_Via_Machine_Learning_Algorithms
- Positioning, Navigation, and Timing; Artificial Intelligence; Machine Learning; AI/ML algorithms; Signal Classification; Antenna; Antenna System; Radio Frequency (RF) signals; Global Positioning System (; Global Navigation Satellite System; Interference sources; Navigation systems
Direct to Phase II Selectees
Objective
The Army wants to develop the capability to classify signals in real-time that impact navigation systems. Through the solicitation, the Army will better understand the types of signals experienced in relevant environments to appropriately apply mitigation techniques to minimize harm. Currently, navigation systems depend on Radio Frequency signals that can experience interferences.
Quickly understanding signal characteristics to react to and mitigate negative impacts remains a challenge. Current antenna technologies treat all signals as the same and attempt to ignore them equally. With more sophisticated interference sources, this is not always successful. However, if the Army can identify the technique used to interfere with navigation, it can implement more impactful mitigation methods.
Description
This effort provides a risk reduction approach to improve performance, provide cost savings and expand the application of the technology sensor solution set that includes additional Army aviation assets. It seeks to demonstrate novel adaptive learning techniques to perform Position, Navigation and Timing signal classification of the battlefield environment.
The proposed topic seeks to build upon Artificial Intelligence/Machine Learning algorithm technologies. We have seen progress throughout the community in demonstrating the ability to classify signals using AI/ML.
This topic will also expand on the progress and move towards real-time signal classification. ML approaches allow adaptability in the detection process that can identify new unknown interference sources. These new signal types can subsequently train an antenna system without requiring an upgrade. This will allow faster decisions, providing more protection for the navigation system.
Phase I
This topic accepts Direct to Phase II proposals. Proposers interested in submitting a DP2 proposal must provide documentation to substantiate that the vendor met the scientific, technical merit and feasibility equivalent requirements to a Phase I. Documentation can include data, reports, specific measurements and success criteria of a prototype.
Phase II
Vendors must provide:
- Two antenna systems capable of detecting, classifying interference signals in real-time and protecting the navigation solution from harm.
- Data collection of relevant signals, training the AI/ML solution and successfully demonstrating the ability to detect and identify the signal types in a relevant environment.
The antenna design must allow for portable AI/ML training techniques that can move from one antenna system to another. This will help upgrade the antenna systems to manage new signals and support other antenna systems in the same environment.
The demonstration antenna system consists of antenna elements, antenna electronics and associated AI/ML algorithms (hardware and software solutions). The Army intends to assess these antenna systems in a relevant environment.
Phase III
- The military and private sector have used advanced PNT technologies for decades. PNT firms can fall into two categories: emerging and legacy. Legacy PNT firms focus on Global Positioning Systems-enabled tech and inertial guidance systems. Emerging PNT organizations focus on major enhancements to existing systems or entirely original approaches.
- End-users for the PNT technology market span multiple sectors.
- The defense market consists of land, air, space and naval with applications for autonomous vehicles, drones, and satellites.
- Government and civil applications include traffic management, rail control, disaster management and other critical government infrastructure.
- Commercial applications include transport and logistics, aviation, marine, agriculture, mobile mapping and surveying.
Submission information
All eligible businesses must submit proposals by noon p.m. Eastern Time.
To view full solicitation details, click here.
For more information, and to submit your full proposal package, visit the DSIP Portal.
SBIR|STTR Help Desk: usarmy.sbirsttr@army.mil
References:
- O’Shea, T. Roy, T. C. Clancy, “Over-the-Air Deep Learning Based Radio Signal Classification,” IEEE J. of Selected Topics in Signal Processing, 12(1), Feb. 2018, pp. 168-179
- https://arxiv.org/pdf/1712.04578
- R. Conlin, et al., “Keras2c: A Library for Converting Keras Neural Networks to Real-time Compatible C,” Engineering Appl. of Artificial Intelligence, v. 100, April 2021, 104182
- https://www.sciencedirect.com/science/article/abs/pii/S0952197621000294
- R. Morales Ferre, et al., “Jammer Classification in GNSS Bands Via ML Algorithms,” Sensors, 2019, doi:10.3390/s19224841
- https://www.researchgate.net/publication/337096847_Jammer_Classification_in_GNSS_Bands_Via_Machine_Learning_Algorithms
- Positioning, Navigation, and Timing; Artificial Intelligence; Machine Learning; AI/ML algorithms; Signal Classification; Antenna; Antenna System; Radio Frequency (RF) signals; Global Positioning System (; Global Navigation Satellite System; Interference sources; Navigation systems