Development of an Unmanned Aerial Systems (UAS) Passive Detection, Tracking, And, Identification System for Ground Vehicles is sponsored by U.S. Army SBIR. This opportunity supports mission-aligned projects and measurable outcomes.

Development of an Unmanned Aerial Systems (UAS) Passive Detection, Tracking, And, Identification System for Ground Vehicles. – Army SBIR|STTR Program Development of an Unmanned Aerial Systems (UAS) Passive Detection, Tracking, And, Identification System for Ground Vehicles. Application Due Date: 07/30/2024 Develop a drone detection, tracking, and identification system using passive sensors .

Develop a system for drone detection, tracking, and identification with low power consumption based on passive electronic devices that don’t radiate energy into surrounding environment. Energy harvesting sensors are possible.

The goal of this Army Small Business Innovative Research (SBIR) topic is the detection, tracking, and identification of airborne drones from their radio frequency transmissions, visual or acoustic signatures using passive sensors. The drone identification and classification are done by rapidly analyzing signals from one or several receiving antennas.

As several target drones are often present in the antenna’s range, the received signal may represent a result of interference of several sources. For such a multiple target identification in a drone swarm, it is crucial to be able to process information in parallel and directly from the passive sensor or array of sensors.

Recent research into applications of artificial intelligence (AI) was executed to solve a variety of computational and signal processing problems. Of a particular interest for military applications is the low power consumption of the sensor network elements. Another important consideration in the drone detection, tracking, and identification problem is the power requirements of the device.

Recently, it has been demonstrated that sensor networks are capable of performing simple detection, tracking, and identification tasks in nanosecond time with extremely low power consumption. These results look very promising for the development of mobile passive and low-power devices for detection and identification of drones.

The goal of this topic is to develop a passive sensor system capable of simultaneous detection, tracking, and identification of single and multiple (swarms) drones threatening ground vehicles. Another goal is to design an optimal architecture of passive sensor networks with integrated memory, and to develop and test learning and data-processing network algorithms suitable for detection of single and multiple drones (swarm).

The system shall be able to detect and track up to 2km away with full hemispherical coverage. The system will include a soldier user interface control panel with the ability to alert at least one single operator. The system is allowed for degraded performance within a wooded/dense environment or within a large metropolitan environment.

The system will allow for installation on tactical and combat ground vehicles (to include Army watercraft ). Determine technical feasibility of passive sensors for drone detection. Using computer simulations, demonstrate the possibility of using passive electromagnetic acoustic, optical, and other innovative sensing for processing multiple drone signatures.

Demonstrate possibility of classification of drone signatures using these passive sensor systems. Develop the solution to achieve the capabilities outlined in Phase I . Demonstrate that the solution meets the first major milestone of identifying optimum materials for the development of passive low-power consumption sensors for UAS detection, tracking, and identification.

Develop principles of building networks of passive sensors that will utilize fast processing capabilities of the chosen network elements. Develop and test learning algorithms for drone identification in the presence of a single and multiple drone signatures and modulated drone signals. Using computer simulations, demonstrate successful drone classification using sensor network.

Determine processing time, power consumption, weight and size of an adversarial drone device based on passive sensors. The system will be evaluated for MAF compliance with the GVSC owned vehicle base kit in the GVSC Vehicle Protection Integration Lab (VPIL) . The contractor shall provide a performance assessment on the prototype system at the end of the first year of Phase II.

A prototype system shall be available and delivered to GVSC at the end of the first year of Phase II which will be evaluated for MAF compliance in the VPIL and demonstrated in a simulated virtual environment. Two complete systems shall be delivered to GVSC at the end of the second year of Phase II following a physical demonstration assessment of one complete system installed on an Infantry Squad Vehicle (ISV) at Camp Grayling, MI.

Expand the capabilities of the solution to simulate different environments and conditions to better reflect the operating environments of Army vehicles. Demonstrate applicability of use within an urban environment to be available for municipal security, law enforcement, and commercial vehicles. For more information, and to submit your full proposal package, visit the DSIP Portal .

SBIR|STTR Help Desk: usarmy. sbirsttr@army. mil Yang Bai, et al.

: “Acoustic-based sensing and applications: A survey” https://www. sciencedirect. com/science/article/pii/S1389128620311282 ; Paul Regtien, Edwin Dertien, Optical sensors, in Sensors for Mechatronics (Second Edition), 2018 https://www.

sciencedirect. com/topics/materials-science/optical-sensor ; “Combat drones: We are in a new era of warfare – here’s why” https://www. bbc.

com/news/world-60047328 ; 4. Nakamura, Junichi (2005). Image Sensors and Signal Processing for Digital Still Cameras.

CRC Press. pp. 169–172.

ISBN 9781420026856. Develop a drone detection, tracking, and identification system using passive sensors . Develop a system for drone detection, tracking, and identification with low power consumption based on passive electronic devices that don’t radiate energy into surrounding environment.

Energy harvesting sensors are possible. The goal of this Army Small Business Innovative Research (SBIR) topic is the detection, tracking, and identification of airborne drones from their radio frequency transmissions, visual or acoustic signatures using passive sensors. The drone identification and classification are done by rapidly analyzing signals from one or several receiving antennas.

As several target drones are often present in the antenna’s range, the received signal may represent a result of interference of several sources. For such a multiple target identification in a drone swarm, it is crucial to be able to process information in parallel and directly from the passive sensor or array of sensors.

Recent research into applications of artificial intelligence (AI) was executed to solve a variety of computational and signal processing problems. Of a particular interest for military applications is the low power consumption of the sensor network elements. Another important consideration in the drone detection, tracking, and identification problem is the power requirements of the device.

Recently, it has been demonstrated that sensor networks are capable of performing simple detection, tracking, and identification tasks in nanosecond time with extremely low power consumption. These results look very promising for the development of mobile passive and low-power devices for detection and identification of drones.

The goal of this topic is to develop a passive sensor system capable of simultaneous detection, tracking, and identification of single and multiple (swarms) drones threatening ground vehicles. Another goal is to design an optimal architecture of passive sensor networks with integrated memory, and to develop and test learning and data-processing network algorithms suitable for detection of single and multiple drones (swarm).

The system shall be able to detect and track up to 2km away with full hemispherical coverage. The system will include a soldier user interface control panel with the ability to alert at least one single operator. The system is allowed for degraded performance within a wooded/dense environment or within a large metropolitan environment.

The system will allow for installation on tactical and combat ground vehicles (to include Army watercraft ). Determine technical feasibility of passive sensors for drone detection. Using computer simulations, demonstrate the possibility of using passive electromagnetic acoustic, optical, and other innovative sensing for processing multiple drone signatures.

Demonstrate possibility of classification of drone signatures using these passive sensor systems. Develop the solution to achieve the capabilities outlined in Phase I . Demonstrate that the solution meets the first major milestone of identifying optimum materials for the development of passive low-power consumption sensors for UAS detection, tracking, and identification.

Develop principles of building networks of passive sensors that will utilize fast processing capabilities of the chosen network elements. Develop and test learning algorithms for drone identification in the presence of a single and multiple drone signatures and modulated drone signals. Using computer simulations, demonstrate successful drone classification using sensor network.

Determine processing time, power consumption, weight and size of an adversarial drone device based on passive sensors. The system will be evaluated for MAF compliance with the GVSC owned vehicle base kit in the GVSC Vehicle Protection Integration Lab (VPIL) . The contractor shall provide a performance assessment on the prototype system at the end of the first year of Phase II.

A prototype system shall be available and delivered to GVSC at the end of the first year of Phase II which will be evaluated for MAF compliance in the VPIL and demonstrated in a simulated virtual environment. Two complete systems shall be delivered to GVSC at the end of the second year of Phase II following a physical demonstration assessment of one complete system installed on an Infantry Squad Vehicle (ISV) at Camp Grayling, MI.

Expand the capabilities of the solution to simulate different environments and conditions to better reflect the operating environments of Army vehicles. Demonstrate applicability of use within an urban environment to be available for municipal security, law enforcement, and commercial vehicles. For more information, and to submit your full proposal package, visit the DSIP Portal .

SBIR|STTR Help Desk: usarmy. sbirsttr@army. mil Yang Bai, et al.

: “Acoustic-based sensing and applications: A survey” https://www. sciencedirect. com/science/article/pii/S1389128620311282 ; Paul Regtien, Edwin Dertien, Optical sensors, in Sensors for Mechatronics (Second Edition), 2018 https://www.

sciencedirect. com/topics/materials-science/optical-sensor ; “Combat drones: We are in a new era of warfare – here’s why” https://www. bbc.

com/news/world-60047328 ; 4. Nakamura, Junichi (2005). Image Sensors and Signal Processing for Digital Still Cameras.

CRC Press. pp. 169–172.

ISBN 9781420026856. 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)