Company
Portfolio Data
Back to Company SearchHYPERKELP INC
UEI: DZAFQK2E2911
Number of Employees: 2
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
SBIR/STTR Involvement
Year of first award: 2023
2
Phase I Awards
1
Phase II Awards
50%
Conversion Rate
$510,300
Phase I Dollars
$998,321
Phase II Dollars
$1,508,621
Total Awarded
Awards
Buoy Based Distant Early Warning for Hypersonics
Amount: $998,321 Topic: N231-013
Phase II of the HyperKelp project is dedicated to advancing the development of a buoy-based counter-hypersonics sensing system, building on Phase I's groundwork. The technical objectives include certification, security, and clearance requirements, payload integration onto the KSB platform, optimization of target acquisition performance, system performance characterization, integration with command and control systems, and end-user training and operational deployment.The work plan is structured to progress from initial planning to prototype development, testing, and operational deployment. Tasks encompass security compliance, prototype design and integration, signal processing optimization, testing and validation, command and control integration, and end-user training.This ĀPhase II aims to deliver a robust counter-hypersonics sensing system, enhancing national security capabilities against hypersonic threats. Through collaboration and systematic execution, Phase II sets the stage for the system's widespread adoption and deployment in Phase III.
Tagged as:
SBIR
Phase II
2025
DOD
NAVY
SBIR Phase I: Feasibility of an L5 GPS-Based Tsunami Detection and Alerting System
Amount: $274,694 Topic: ET
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in the development and deployment of an effective, affordable, and globally accessible detection and alert system for tsunamis on coastlines worldwide. Tsunamis are among the most significant ways in which the ocean impacts human civilization. With the increasing population density along coastlines and the global rise in sea levels, the potential for tsunamis to cause unprecedented harm is higher than ever. The proposed product providing enhanced wave height and arrival time maps to its customers could provide significantly more timely alerts to at-risk populations, providing them with essential evacuation information and crucial hours to prepare for approaching waves. This could herald a major shift in tsunami preparedness and resilience. This technology offers wide-ranging benefits for various groups and industries such as small island economies, defense operations, and commercial port operators. To develop these capabilities, this project proposes a buoy-deployable software product that takes advantage of new generations of Global Positioning System (GPS) to detect tsunamis at sea, hours before they make landfall. It will use revolutionary advancements in GPS technology, particularly in vertical accuracy, to detect, prepare for, and mitigate the formidable threat of tsunamis. For the first time, new generations of GPS provide sufficient resolution to detect the subtle vertical displacement of the ocean surface caused by passing tsunami waves with a single receiver even in open ocean. The project will focus on the development of novel methods of signal classification using Dense Neural Network (DNN) and optimize them with rapid Machine Learning (ML) methods. This is expected to help demonstrate that tsunami signatures can be perceived in real-time by low-cost and low-power on-edge processing capabilities. When this new on-edge technology is deployed on widespread ocean buoys, it would form a robust tsunami detection network. These buoys will serve as sentinels, capable of sensing distinct sea level changes that signal an impending tsunami. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Tagged as:
SBIR
Phase I
2024
NSF
Buoy Based Distant Early Warning for Hypersonics
Amount: $235,606 Topic: N231-013
While Electro Optic (EO) sensors stationed on the surface and space systems are key to the Deprtment of the NavyÆs (DoN) ability to track and counter hypersonic threats, they face limitations. Both space based and surface level sensors are prone to signal blockage by clouds, which absorb optical and infrared signals characteristic to Hypersonic Glide Vehicles HGVs. Well over 50% of the ocean is subject to cloud cover at a given time, meaning that the majority of deployed surface based EO sensors will face significant challenges in detecting and identifying obscured threat signatures. These obstacles demand any successful tracking system to take a multimodal approach to the detection of HGVs, especially during the glide and terminal phases of flight.Ā HyperKelp proposes that any effective counter hypersonic EO sensing mesh will incorporate tip-and-cue capabilities from rapidly deployable infrasound and acoustic sensors. By fusing EO signals with acoustic and infrasound signals, signatures of overhead hypersonic threats cannot be masked by cloud cover or changes in velocity. Such signals have been used to study hypersonic craft at large standoff distances since the Apollo missions, but only recently have advancements in edge computing enabled the classification of the source vehicles. HyperKelpÆs autonomous sensor platforms will incorporate this technology to host acoustic sensors and novel, non-traditonal signal processing models to act as classifier engines. If successful, these automated classifiers will provide tip-offs for cooperative sensor array technology, including optical sensors further along threat axes. This multimodal approach is resilient to weather conditions, increases preparedness throughout the killchain, and offers redundant and independent sources of information that will strengthen all EO-based detection systems.Ā This Phase I will deliver a feasibility study of a deep machine learning technique to train Dense Neural Networks (DNN) as acoustic signal source classifiers. Building on HyperKelpÆs existing body of work, these DNNs will analyze real time spectrogram feeds to identify acoustic signal structures, like N-wave overpressures, that are consistent with overhead hypersonic vehicles. This work will culminate in a plan to prototype this passive sensor and target classification capability onboard HyperKelpÆs autonomous Kelp Smart Buoy (KSBM) platforms during a Phase II. The Phase I Option will integrate and deploy the Phase I products aboard HyperKelpÆs existing KSBM system to accelerate the project toward delivery of a mission-ready product by the end of a Phase II.
Tagged as:
SBIR
Phase I
2023
DOD
NAVY