Attachment 0010 - PROJECT VISTA PAST ARMY SBIR Awards - 10 October 2023.pdf

Company Award Title Branch Program Agency Tracking Number

Contract Award Year

Award Amount

DUNS

Hubzone Owned

Socially and Economically Disadvantaged

Woman Owned

Number Employees

Address1 Address2 City State Zip Contact Name Contact Phone Contact Email PI Name

PI

Title

PI Phone PI Email Research Keywords Abstract

EMAG TECHNOLOGIES, INC.

Radio Network Model Plugin for Unreal Engine Vehicle Simulation

Army SBIR A202-131-

W56HZV-

21-C-

2021 ######## 8364937

N N N 12 775 Technology Drive, Suite 300

Ann Arbor MI 48108-

Dr. Kazem F. Sabet (734) 996-3624 ksabet@emagtech.com Dr. Kazem F. Sabet (734) 996-3624 ksabet@emagtech.com Modeling & Simulation,Radio Channel Modeling,radio simulation,Unreal Engine,communication system,radio wave propagation,Terrain,antenna,wireless network

The primary objective of this SBIR project is to develop a plugin for Unreal Engine 4 that can be used to simulate a wireless radio network using the Unreal Engine environment (e.g. landscape map, weather, buildings, vehicles) that supports the same conditions as in the real world (e.g. line of distance interference) for evaluation of robotic and autonomous systems (RAS) operating in Manned-Unmanned Teaming (MUMT) scenarios. The Phase I feasibility study will develop a comprehensive software architecture and will describe how it will meet the goal of creating Radio Network Model Plugin for Unreal Engine Vehicle Simulation. The proposed software will be based on EMAG’s revolutionary near-real-time Polarimatrix solver for physics based-communication channel and link analysis.

Nanotok LLC Advanced, Compact Acoustic Particle Velocity- Pressure Sensory System

Army SBIR A2-8463

W911QX-

21-C-

2021 ######## 0813611

N N N 4 3075 Raccoon Pt Road PO Box

Eastsound WA 98245-

Elizabeth Dixon (206) 717-4450 elizabeth.dixon@nanotok.com Jim Waite (206) 717-4450 jim.waite@nanotok.com Acoustic vector sensor,Sound Intensity,infrasound,Acoustic Target Tracking,Bearing Angles,Triaxial Accelerometer,sensor network,Microphone array

The flow of acoustic energy is described by intensity, equal to the product of acoustic pressure and the velocity of the air moved by the acoustic field. A new triaxial acoustic particle velocity sensor measures the motion of a lightweight rigid volume, within which a MEMS accelerometer is embedded. Pressure is measured by a complementary MEMS microphone. Together, these MEMS sensors facilitate accurate measurement of acoustic intensity spanning the infrasonic (1.5 kHz) frequency bands, with 100 dB of dynamic range. Benchtop calibration of the sensors is accomplished by an acoustic wave tube calibrator. The new Accelerometer-based Intensity Vector Sensor (AIVS, pronounced /āvs/) is combined with a battery operated, low SWaP sampling and processing system based on an IoT system-on-chip, and packaged in a 10 cm cube for use in the field. Each of these wireless nodes can be synchronized by wire or GNSS, such that a collection of AIVS in diverse positions can self-organize into a collaborating sensor network for applications related to sound field mapping, target detection and tracking, acoustic source location, and sound power estimation. To minimize power dissipation (averaging less than 20 mW), the processing architecture facilitates local detection based on time and frequency dependent intensity signatures, which limits use of the wireless subsystem to periods when acoustic activity exceeds the detection criteria. In lieu of real-time wireless transfer, local storage can be employed to capture events for later export or analysis. The AIVS node interfaces to commercial environmental and weather sensors, is weatherproof, wind protected, and sustains operation over a wide temperature range. AIVS nodes are delivered with software demonstrating how the system is used for multi-copter (drone) tracking. Upon detection of a passing drone, the triaxial measurements are combined to estimate the azimuth and elevation angles to the target. When operating in a small network and other nodes observe the same target, a network controller computes the 3-d position and direction of the detected target. The demonstration software is intended to facilitate adoption of the AIVS in other such multi-node detection and tracking scenarios. For very low frequency infrasound measurements, the AIVS occupies little space and for some applications a small AIVS network could replace large aperture microphone arrays. Because the accelerometer is embedded within a protective lightweight solid substrate, the sensor is much more resistant to wind noise than microphone systems. MEMS accelerometers are sensitive to DC, facilitating very good phase match between axes as well as between physically separated AIVS nodes.

Stottler Henke Associates, Inc.

Electronic Warfare-Smart COordination of UAV Teams (SCOUT)

Army SBIR A201-053-

W56KGU-

20-C-

2020 ######## 6081767

N N N 50 1650 South Amphlett Blvd.

Suite 300

San Mateo CA 94402-

Carolyn Maxwell (925) 337-4640 carolyn@stottlerhenke.com Richard Stottler (650) 931-2700 stottler@stottlerhenke.com

Heterogeneous UAV Teams,Autonomous UAV Swarms,Non-permissive Communications,Behavior Transition Network (BTN),Case-Based Reasoning,Probabilistic Road Map Planner (PRMP),Artificial Intelligence (AI),electronic warfare

The ultimate goal is to allow autonomous UAV teams to autonomously make decisions and coordinate their efforts in an intelligent and resilient way. This will require intelligent real-time information-sharing, planning, role allocation, and detailed path planning and autonomous, intelligent, adaptive behavior by the UAVs. Stottler Henke’s SCOUT, based on several existing mature technologies, and previously demonstrated on actual flying UAVs, completely autonomously creates path plans and automatically reacts to found targets by performing additional path planning as necessary. Furthermore, SCOUT can allocate search areas for individual UAVs based on their individual capabilities (in sensing and mobility) and autonomously plan around recently detected threats. Tactics also exist for employing multiple UAVs against a single target simultaneously. SCOUT is a general architecture that can be easily applied to different mission types and in Phase I, we will adapt SCOUT to electronic warfare domain, specifically in the A2AD environment. Missions will include deception/seduction of enemy sensors and penetration and suppression of IADS (similar to the SEAD mission which SCOUT has been proven to be effective with), which requires adapting and developing new AI techniques to automate all aspects of intelligently, automatically selecting appropriate plays, robustly assigning roles and planning routes, and adaptively executing each role, robustly and predictably in diverse environments, including intelligent information sharing. The existing SCOUT architecture provides an already-flight-tested and proven foundation for the development of the proposed EW-SCOUT. EW-SCOUT will enable fully autonomous UAS EW-related missions in the A2AD environment, allowing for allied manned aircraft to more safely penetrate the airspace. We will design the ultimate system and, to prove its feasibility, prototype all aspects of it in Phase I based on our current SCOUT architecture and demonstrate it with our already-existing Multi-Agent SimulaTor (MAST). MAST is able to simulate the movement of air and ground forces, factoring in environmental variables such as terrain and weather and executes Monte-Carlo-styled scenarios. It is capable of simulating UAV detection and tracking via sensors, human and mechanical, and communications over a variety of different technologies (e.g., visual, radar, IR, acoustic). In Phase I, we plan to make improvements to the communications feature of MAST to include new technologies such as low-energy lasers and to expand the set of DIL environment behaviors. We will also investigate integration with possible ALE platforms, representative sensors, and UAVs, supported by a group of highly qualified subject matter experts in EW operations.

UHV TECHNOLOGIES, INC.

Object Detection and Classification for Small Objects in/on Water

Army SBIR A201-060-

W9132T- 20-C-

2020 ######## 0140050

N Y N 18 1708 Jaggie Fox Way

Lexington KY 40511-

Nalin Kumar (817) 880-3880 kumarmaple@aol.com Manuel Garcia, (210) 863-3812 mgarcia@nanoRANCH.com Water,Polarimetry,Radar,Sonar,artificial intelligence,Sensor,Fusion

“Technologies with object detection capabilities sensitive enough to identify fast moving objects floating on the surface of water (or just below the surface) can offer important capabilities to understand the environment, upstream activities, and add to competencies for future military readiness and the warfighter.” To address this need, UHV and UTARI proposes a multi-pronged approach; (i) use of a Long Wave Infrared Imaging Polarimeter (LWIRP) for imaging objects on water, (ii) test other sensors such as radar and sonar (iii) leverage UHV’s previous success with object detection and classification neural networks for military applications and (iv) investigate a machine learning / artificial intelligence (ML/AI) based sensor fusion approach to detect threats quickly. UHV has contacted Polaris, manufacturer of the LWIRP camera, and they have expressed a high interest in working with UHV to develop object detection and classification neural networks for use with their existing lines of Pyxis LWIRP cameras. This strategy maximizes the chances of success by (i) using a commercially available product for the LWIRP, the Pyxis model, which has already been demonstrated for improved imaging on water surfaces (ii) using a large array of state-of-the-art image segmentation methods to determine architectures that are most suited to perform this task (iii) mitigating the risk associated with image segmentation methods on highly successful neural networks which UHV has demonstrated in real world conditions (iv) proposing a sensor suite enabling detection under different weather conditions (v) proposes a novel neural network architecture for integrating datasets from multiple sensors in real time.

ALPHACORE INC

Slant Path Optical Turbulence and Atmospheric Characterization

Army SBIR A2-8471

W91CRB-

21-C-

2020 ######## 0786025

N N N 25 304 S Rockford Dr

Tempe AZ 85281-

Esko Mikkola (480) 494-5618 engineering@alphacoreinc.com Esen Salcin (480) 494-5618 engineering@alphacoreinc.com optical turbulence,atmospheric characterization,test range equipment,directed energy

The US Army requires a multi-technique slant path optical turbulence and atmospheric characterization field data collection system that can be part of an Unmanned Aircraft System (UAS) payload. This system would support measurement of optical turbulence and atmospheric characterization through imaging and meteorological sensor suite allowing (i) single-ended, (ii) simultaneous and multiple line-of-sights for horizontal and slant path measurements in addition to (iii) flexible positioning of the measurement site (via control of UAS). This is a significant improvement over existing large ground- or tower-based scintillation and transmission measurement systems that are not designed for slant path measurements, measure single line of sight, and require extended hardware calibration. Department of Defense (DOD) is spending billions of dollars developing optical laser weapon and targeting systems for the warfighter as well as infrared sensor/imaging systems that can provide enhanced imaging in Degraded Visual Environments (DVE) on both land and air platforms. US Air Force (USAF) and US Army programs to be fielded within the next 5-15 years will require slant path optical atmospheric transmission and atmospheric turbulence truth data. The ability to test and characterize atmospheric turbulence and atmospheric transmission properties will be critical, affecting the performance of advanced laser systems and aircraft imaging systems. To address need in the SBIR A19-024 Phase II program, Alphacore will build the slant path atmospheric characterization and optical turbulence measurement system concept investigated in Phase I period into a fieldable prototype system called OCULAR (OptiCal tUrbuLence meAsuRement) system. OCULAR is a UAV mountable field data collection system for both horizontal and slant path measurements consisting of high-frame rate visible (VIS) and Long-Wave Infrared (LWIR) imaging cameras, laser range finder, and on-board mini wind and weather sensors. The system also has a Ground Control Station consisting of a complete meteorological station. The UAS payload together with ground station will provide integrated path and nodal measurements and compose a complete atmospheric characterization solution. In Phase II Alphacore will compare reference scintillometer measurements against Alphacore’s OCULAR system. These field tests will be performed for (i) both day and night, (ii) horizontal path, (iii) slant-path (using a tower) for at least 2 different environmental conditions. In addition, product usefulness will also be verified by comparisons to truth data and simulation results from multiple reliable sources based on measured atmospheric sounding profiles. OCULAR will be integrated into an easy to use Graphical User Interface (GUI) that can be adapted for use at government test range facilities. This software improves upon the software developed by Alphacore during Phase I.

BLUE STORM ASSOCIATES INC. Weather Situational Awareness In The Cockpit Army SBIR A2-8252 W911W6- 21-C-

2020 ######## 8317843

N N Y 23 5680 King Centre Dr. Suite

Alexandria VA 22315-

Mary Lockhart (571) 235-8226 mary.lockhart@pemdastech.com Michael Gauthier (321) 431-6408 michael.gauthier@pemdastech.com weather in the cockpit,weather situational awareness,Common Operating Picture,Decision Support Tools,Army aviation,Environmental Intelligence (EI),Aviation Mission Common Server (AMCS),Tactical Airspace Integration System

(TAIS)

In this Phase 2 effort, PEMDAS Technologies and Innovations (PEMDAS) proposes to fully develop a prototype Weather Collaborative Operating Picture System (WxCOP-PS) using the technologies assessed, and knowledge gained during our Phase I effort. The proposed innovation will deliver complementary (and consistent) graphical weather decision support products to Army aviation stakeholders on the ground and directly into aircraft cockpits to improve individual and shared Situational Awareness (SA). We will leverage our PEMDAS Systems Engineering Framework to design, build, and demonstrate the functionality of a WxCOP-PS that satisfies requirements identified during needs assessment conducted during our Phase 1 effort. In accordance with AR115-10 (Weather Support for the US Army), the WxCOP-PS will leverage weather data from authoritative sources, to generate and deliver tailored graphical weather decision support products to Army aviation stakeholders. We will develop, build, test, and operationalize custom algorithms capable of extracting information from gridded (2D, 3D, and 4D) weather datasets, (2D) image files, and textual weather data to generate optimized product sets, with extremely reduced file sizes, capable of being exploited within the cockpit. The airborne segment will be Future Airborne Capabilities Environment (FACE) compliant and integrate with the Aviation Mission Common Server (AMCS) and aircraft displays. The ground segment will integrate with the Tactical Airspace Integration System (TAIS) and associated Army air traffic management infrastructure such as the Army Mobile Tower System (MOTS). The WxCOP-PS will be designed to reduce workload while increasing individual and shared SA by improving team knowledge of the battlespace weather. WxCOP system attributes include: Tactically relevant weather information Collaborative/common operating weather picture Intuitive tailored graphical displays and user interfaces Ability to operate in austere and data sparse environments Compatibility and seamless integration with existing systems Data throughput needed to support concept of getting graphical weather data into the cockpit

Intelligent Automation, Inc.

CVGN: Conditional Image/Video Generation Network with Generative Adversarial Network

Army SBIR A2-8343

W56KGU-

21-C-

2020 ######## 1619115

N N N 240 15400 Calhoun Drive Suite 190

Rockville MD 20855-

Mark James (301) 294-5221 mjames@i-a-i.com Gang Mei (301) 294-5257 mgang@i-a-i.com Computer Vision,deep learning,data augmentation,low-shot learning,Generative Adversarial Network

An automatic, on-demand software solution is desired that is capable of generating large scale image/video training datasets for downstream analytic algorithms such as object classification or target tracking, with user selected attributes such as types of foreground objects, background scenes, weather conditions, camera view angles, etc., given a small labeled seed dataset or even no seed data at all. To address this critical need and limitations of state-of-the-art data generation approaches, Intelligent Automation, Inc. (IAI), proposes to design and implement an automatic, on-demand Generative Adversarial Network (GAN) based algorithm framework called CVGN, for large dataset generation that can be tailored to DoD’s special needs, e.g., generating infrared (IR) images and videos from overhead camera angles such as Unmanned Aerial System (UAS). The key innovation of the proposed approach is seamlessly combining unsupervised representation learning, a high fidelity simulation engine, transfer learning, and data augmentation with on-demand training data generation.

R-DEX SYSTEMS, INC.

DeepSAR: Automating the Extraction of Tactical Information from Synthetic Aperture Radar Imagery

Army SBIR

AX194-

166-0034

W15QKN-

20-P-

2020 ######## 0792414

N N N 8 1225 Johnson Ferry Rd

Suite 240 Marietta GA 30068 Robert Bock (678) 641-2380 robert@r-dex.com Robert Bock

Direct or of Scienc e and Engin eering

(678) 641-2380 robert.bock@r-dex.com

Synthetic Aperture Radar (SAR),Intelligence Surveillance and Reconnaissance (ISR),machine learning,artificial intelligence,Deep Learning,foliage penetration,detection,classification

R-DEX Systems (R-DEX) proposes to leverage its unique expertise in artificial intelligence, machine learning, and advanced sensor solutions to develop DeepSAR, a groundbreaking software platform to automate the extraction of tactical information from Synthetic Aperture Radar (SAR) imagery for the U.S. Army and other DOD customers. As the use of SAR in the Army on manned and unmanned airborne platforms continues to grow, there is a critical need to automate the analysis of SAR imagery. Currently, human analysts are overwhelmed by the massive amounts of SAR imagery collected every day. To compound the problem, SAR imagery is harder to analyze than EO/IR imagery because the images are more susceptible to the location of scattering centers, multipath scattering, and occlusions. DeepSAR will leverage proven software to autonomously extract timely actionable information from SAR imagery. Unlike other modalities, SAR can capture images during the day and night and in all weather conditions. In addition, SAR allows detection through dense foliage, forested areas, wooded terrain, camouflage, and has applicability in multiple areas of operation. In the short term, DeepSAR will reside in a ground station; however, in the future it can be integrated directly on the airborne platform, thereby radically mitigating downlink requirements.

INTELLISENSE SYSTEMS INC AMWS Ceilometer to 25,000 feet Army SBIR S2S-0006

W911NF

20C0049

2020 ######## 0809219

N N N 160 21041 S.

Western Ave.

Torrance CA 90501-

Selvy Utama (310) 320-1827 notify@intellisenseinc.com Chris Ulmer (310) 320-1827 sbir@socom.mil

To address the USSOCOM need for a lightweight, man-packable aviation weather solution with a battery powered, all-weather ceilometer, Intellisense Systems, Inc. (Intellisense) proposes to develop the Micro Weather Sensor Auxiliary Ceilometer (MWS-AC), which will be a 25,000-ft-capable ceilometer with all-weather capability based on the revolutionary ceilometer module onboard the Advanced Micro Weather Sensor (AMWS) , which can detect clouds up to 5,000 ft currently, has been in production for over 2 years, and has been fielded to Department of Defense Services worldwide. As a result, the existing portable ceilometer design will be augmented to provide cloud detection over 25,000 ft, and include an active optical window clearing with portable power and communications, which directly address the USSOCOM requirements for an unattended, all weather ceilometer. The Micro Weather Sensor (MWS®) was initially developed under a Phase II USSOCOM topic and was transitioned and qualified for production under a Phase III contract. Intellisense will build upon the success of the MWS by developing modular plug-and-play auxiliary accessories, such as the proposed Auxiliary Ceilometer. During this Phase II, Intellisense will leverage preliminary developments made on a 25,000-ft ceilometer capability with a window precipitation clearing process to provide an enhanced, ruggedized, and integrated package that complements the existing MWS. The MWS is fully capable of sending reliable and regular weather reports through Iridium satellite communications, and the newly designed auxiliary ceilometers will be deployed to complement existing MWS units to utilize their reporting capabilities and Iridium communications.