Drone Swarm Detection Using Artificial Intelligence Based on Ultrafast Neural Networks

Drone Swarm Detection Using Artificial Intelligence Based on Ultrafast Neural Networks is sponsored by U.S. Army (Army SBIR|STTR Program). The Army seeks drone swarm detection using artificial intelligence. Businesses should develop neural network architecture and learning processing algorithms for drone identification based on ultra-fast neural networks with low power consumption.

This includes airborne drone detection via radio frequency transmissions or radar signatures.

Drone Swarm Detection Using Artificial Intelligence Based on Ultrafast Neural Networks – Army SBIR|STTR Program Software Modernization, Army STTR, Phase I Drone Swarm Detection Using Artificial Intelligence Based on Ultrafast Neural Networks Application Due Date: 06/12/2024 Amount Up To: Up to: $194,000 The Army wants drone swarm detection using artificial intelligence.

Businesses should develop neural network architecture and learning processing algorithms for drone identification based on ultra-fast neural networks with low power consumption. The Army Small Business Technology Transfer topic seeks airborne drone detection via radio frequency transmissions or radar signatures using ultra-fast neural networks.

The drone identification and classification are done by rapidly analyzing RF signals from one or several receiving antennas. As several target drones are in the antenna’s range, the received signal may represent a result of interference of several sources. For multiple target identification in a drone swarm, it is crucial to process information in parallel and directly at the carrier microwave frequency.

The Army recently researched applications of AI based on neuromorphic networks,particularly using magnetic artificial neurons to solve a variety of computational and signal processing problems. Neuromorphic computing seeks to replicate human brain functionality in nanoscale using man-made neurons and synapses.

The advantage of this approach is highly parallelized computing with large amounts of memory in close proximity to the computing elements, which results in substantially increased speed and reduced computing power consumption. The methodologies described are particularly suited for defense-related computing.

This is due to a number of unique features such as nano-scale sizes, simple implementation of memory elements and strongly nonlinear dynamics. Of a particular interest for military applications is the low-power consumption of the network elements, as well as the possibility of operation in GHz and even THz frequency ranges.

These high-frequency properties allow one to utilize neural networks for the parallel processing of drone microwave signals at the carrier frequency without digitization or super-heterodyning . Another important consideration in the drone identification problem is the power requirements of the device.

Recently, the Army saw that neural networks based on artificial antiferromagnetic neurons are capable of performing simple identification tasks in sub-nanosecond timeframes with extremely low power consumption of less than 1 pJ per synaptic operation. These results look very promising for the development of mobile, ultra-fast and low-power devices for the neuromorphic identification of drones.

The Army wants to develop a neural network capable of simultaneous, ultra-fast (time scale of nanoseconds) identification and targeting of large drone groups (swarms) threatening ground vehicles. The Army also seeks to design an optimal architecture for an ultra-fast neural network with integrated memory to develop and test data-processing network algorithms suitable for the ultra-fast detection of multiple drones in a drone swarm.

Using computer simulations, vendors must demonstrate the possibility of using AI in the form of an ultra-fast neural network for processing multiple microwave drone signals without super-heterodyning or/and digitization. These businesses need to demonstrate the possibility of classification of drone microwave signals using ultra-fast neural networks in a case where the input signals from drones are monochromatic (unmodulated).

Firms must determine the optimal materials for the development of ultra-fast, lower power consumption neural networks. They need to develop the principles of building large neural networks that will utilize the ultra-fast processing capabilities of the chosen network elements (artificial neurons).

Vendors should develop and test learning algorithms for drone identification in the presence of a single, multiple and modulated drone signatures. Using computer simulations, businesses must demonstrate successful drone classification using a developed ultra-fast neural network. Companies need to determine the processing time, power consumption, weight and size of an anti-drone device based on neural networks.

Vendors should demonstrate successful drone identification using an experimental prototype of a developed neural network. Companies need to demonstrate the possibility of simultaneous identification of multiple drone targets.

Potential applications include lightweight, ultracompact antennae for use in reconnaissance and observation drones (commercial and military), as well as real-time monitoring of frequency agile microwave K band signals with potential applications to Active and Passive protection systems. The commercial application could include Autonomous driving platforms and radar-based collison avoidance systems.

All eligible businesses must submit proposals by noon ET. To view full solicitation details, click here . For more information, and to submit your full proposal package, visit the DSIP Portal .

STTR Help Desk: usarmy. rtp. devcom-arl.

mbx. sttr-pmo@army. mil J.

Grollier, D. Querlioz, K. Y.

Camsari, et al. , “Neuromorphic spintronics. ” Nat.

Electron. 3, 360–370 (2020). https://doi.

org/10. 1038/s41928-019-0360-9 A. Ross, N.

Leroux, A. De Riz, et al. , “Multilayer spintronic neural networks with radiofrequency connections.

” Nat. Nanotechnol. (2023).

https://doi. org/10. 1038/s41565-023-01452-w J.

Torrejon, M. Riou, 3. F.

Araujo, et al. , “Neuromorphic computing with nanoscale spintronic oscillators. ” Nature 547, 428–431 (2017).

https://doi. org/10. 1038/nature23011 R.

Khymyn, I. Lisenkov, J. Voorheis, et al.

, “Ultra-fast artificial neuron: generation of picosecond-duration spikes in a current-driven antiferromagnetic auto-oscillator. ” Sci. Rep.

8, 15727 (2018). https://doi. org/10.

1038/s41598-018-33697-0 H. Bradley, S. Louis, C.

Trevillian, et al. , “Artificial neurons based on antiferromagnetic auto-oscillators as a platform for neuromorphic computing. ” AIP Advances 13, 015206 (2023).

https://aip. scitation. org/doi/10.

1063/5. 0128530 KEYWORDS: artificial intelligence, ultra-fast, artificial neuron, drone identification, learning algorithm The Army wants drone swarm detection using artificial intelligence. Businesses should develop neural network architecture and learning processing algorithms for drone identification based on ultra-fast neural networks with low power consumption.

The Army Small Business Technology Transfer topic seeks airborne drone detection via radio frequency transmissions or radar signatures using ultra-fast neural networks. The drone identification and classification are done by rapidly analyzing RF signals from one or several receiving antennas. As several target drones are in the antenna’s range, the received signal may represent a result of interference of several sources.

For multiple target identification in a drone swarm, it is crucial to process information in parallel and directly at the carrier microwave frequency. The Army recently researched applications of AI based on neuromorphic networks,particularly using magnetic artificial neurons to solve a variety of computational and signal processing problems.

Neuromorphic computing seeks to replicate human brain functionality in nanoscale using man-made neurons and synapses. The advantage of this approach is highly parallelized computing with large amounts of memory in close proximity to the computing elements, which results in substantially increased speed and reduced computing power consumption. The methodologies described are particularly suited for defense-related computing.

This is due to a number of unique features such as nano-scale sizes, simple implementation of memory elements and strongly nonlinear dynamics. Of a particular interest for military applications is the low-power consumption of the network elements, as well as the possibility of operation in GHz and even THz frequency ranges.

These high-frequency properties allow one to utilize neural networks for the parallel processing of drone microwave signals at the carrier frequency without digitization or super-heterodyning . Another important consideration in the drone identification problem is the power requirements of the device.

Recently, the Army saw that neural networks based on artificial antiferromagnetic neurons are capable of performing simple identification tasks in sub-nanosecond timeframes with extremely low power consumption of less than 1 pJ per synaptic operation. These results look very promising for the development of mobile, ultra-fast and low-power devices for the neuromorphic identification of drones.

The Army wants to develop a neural network capable of simultaneous, ultra-fast (time scale of nanoseconds) identification and targeting of large drone groups (swarms) threatening ground vehicles. The Army also seeks to design an optimal architecture for an ultra-fast neural network with integrated memory to develop and test data-processing network algorithms suitable for the ultra-fast detection of multiple drones in a drone swarm.

Using computer simulations, vendors must demonstrate the possibility of using AI in the form of an ultra-fast neural network for processing multiple microwave drone signals without super-heterodyning or/and digitization. These businesses need to demonstrate the possibility of classification of drone microwave signals using ultra-fast neural networks in a case where the input signals from drones are monochromatic (unmodulated).

Firms must determine the optimal materials for the development of ultra-fast, lower power consumption neural networks. They need to develop the principles of building large neural networks that will utilize the ultra-fast processing capabilities of the chosen network elements (artificial neurons).

Vendors should develop and test learning algorithms for drone identification in the presence of a single, multiple and modulated drone signatures. Using computer simulations, businesses must demonstrate successful drone classification using a developed ultra-fast neural network. Companies need to determine the processing time, power consumption, weight and size of an anti-drone device based on neural networks.

Vendors should demonstrate successful drone identification using an experimental prototype of a developed neural network. Companies need to demonstrate the possibility of simultaneous identification of multiple drone targets.

Potential applications include lightweight, ultracompact antennae for use in reconnaissance and observation drones (commercial and military), as well as real-time monitoring of frequency agile microwave K band signals with potential applications to Active and Passive protection systems. The commercial application could include Autonomous driving platforms and radar-based collison avoidance systems.

All eligible businesses must submit proposals by noon ET. To view full solicitation details, click here . For more information, and to submit your full proposal package, visit the DSIP Portal .

STTR Help Desk: usarmy. rtp. devcom-arl.

mbx. sttr-pmo@army. mil J.

Grollier, D. Querlioz, K. Y.

Camsari, et al. , “Neuromorphic spintronics. ” Nat.

Electron. 3, 360–370 (2020). https://doi.

org/10. 1038/s41928-019-0360-9 A. Ross, N.

Leroux, A. De Riz, et al. , “Multilayer spintronic neural networks with radiofrequency connections.

” Nat. Nanotechnol. (2023).

https://doi. org/10. 1038/s41565-023-01452-w J.

Torrejon, M. Riou, 3. F.

Araujo, et al. , “Neuromorphic computing with nanoscale spintronic oscillators. ” Nature 547, 428–431 (2017).

https://doi. org/10. 1038/nature23011 R.

Khymyn, I. Lisenkov, J. Voorheis, et al.

, “Ultra-fast artificial neuron: generation of picosecond-duration spikes in a current-driven antiferromagnetic auto-oscillator. ” Sci. Rep.

8, 15727 (2018). https://doi. org/10.

1038/s41598-018-33697-0 H. Bradley, S. Louis, C.

Trevillian, et al. , “Artificial neurons based on antiferromagnetic auto-oscillators as a platform for neuromorphic computing. ” AIP Advances 13, 015206 (2023).

https://aip. scitation. org/doi/10.

1063/5. 0128530 KEYWORDS: artificial intelligence, ultra-fast, artificial neuron, drone identification, learning algorithm 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)