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"Novel Positioning, Navigation and Timing Signal Classification Techniques" is currently closed and not accepting applications.
Novel Positioning, Navigation and Timing Signal Classification Techniques is a Direct to Phase II SBIR grant from the U.S. Army SBIR/STTR Program that funds small businesses developing AI/ML-powered systems to classify radio frequency signals impacting military navigation in real-time.
The Army seeks technology that can identify interference sources on the battlefield and apply targeted mitigation techniques to protect navigation systems from jamming and spoofing. The program builds on advances in machine learning for signal classification and aims to develop adaptive algorithms that can identify novel, previously unknown interference sources.
Mayflower Communications Company was selected as a Direct to Phase II awardee. Eligible applicants are U.S.-based small businesses.
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Novel Positioning, Navigation and Timing Signal Classification Techniques – Army SBIR|STTR Program Position, Navigation, and Timing, Army SBIR, Direct to Phase II Novel Positioning, Navigation and Timing Signal Classification Techniques Application Due Date: 04/23/2024 Direct to Phase II Selectees Mayflower Communications Company, Inc. 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.
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.
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.
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.
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. 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 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 Mayflower Communications Company, Inc. 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.
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.
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.
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.
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. 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 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 Assistant Secretary of the Army for Acquisition, Logistics, and Technology ASA(ALT) releases contract opportunities on an ad-hoc basis to meet Army research and development needs.
Army Futures Command (AFC) releases topics during three specific solicitation periods throughout the fiscal year to address the Army’s current and anticipated war-fighting technology needs. Army STTR follows AFC’s topic release schedule but partners with a university, federally funded research and development center, or a qualified non-profit research institution as part of their contract.
Is the opportunity to establish the scientific, technical, commercial merit and feasibility of your proposed innovation. Is focused on the development, demonstration and delivery of your innovation from Phase I. Represents the commercialization phase of the program in which the company can market their products or services developed in Phase II, either to the government or in the commercial sector.
Allows small businesses to submit to Direct to Phase II applications if they performed the Phase I research through other funding sources. Provides funding to projects that require additional funding during their open Phase II contract. A Phase II Awardee may receive one additional, sequential Phase II award to continue the work of an initial Phase II award.
The sequential Phase II award has the same guideline amounts and limits as an initial Phase II award.
Artificial Intelligence/Machine Learning (supply chain management, logistics coordination, target identifications and simulation) Advanced Materials and Manufacturing (additive manufacturing) Autonomy (unmanned systems, drones, ground vehicle capabilities) Chemical and Biological (detection, defense) Cyber (biometric authentication, secure communications) Electronics (microelectronics, Very-Large-Scale Integration (VLSI)) Electronic Warfare (jamming, spoofing) Human Performance (wearables) Immersive (augmented reality, virtual reality, mixed reality) Network Technologies (antennas, radio frequency, communications systems) Position, Navigation, and Timing (GPS) Power (batteries, generators) Software Modernization (high performance computing, data management and visualization) Sensors (infrared sensing) Weapons Systems (hypersonics, munitions and projectiles, directed energy)
According to the current listing, eligibility includes: Small businesses in the United States. Confirm the full requirements in the official notice before applying.
The published deadline was April 23, 2026, which has passed. Check the official notice for any future application windows before investing time in a proposal.
Novel Positioning, Navigation and Timing Signal Classification Techniques is funded by U.S. Army SBIR|STTR Program. Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
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