Entries by Expedition Technology

Expedition Technology Receives Phase II SBIR to Continue Development of Advanced Signal Recovery Techniques for ONR

July 6, 2021 in , , /by

Expedition Technology, Inc. (EXP) announces a Phase 2 Small Business Innovative Research (SBIR) award for continued development of a neural network-based technique to mitigate the effects of saturation and clipping on data received from high-rate, direct-sampling analog to digital converters for the Office of Naval Research (ONR). The effort, named Fast Recovery of Signal Estimates using Neural Networks (FROSENN), develops machine […]

ChaRRNets: Channel Robust Representation Networks for RF Fingerprinting

May 20, 2021 in , , /by

For the past five years Expedition Technology has been developing novel deep learning architectures to solve a broad set of signal processing research problems. While we can’t always publish our results, we are very happy we can share the results of some of our foundational work developing channel-robust, complex-valued Convolutional Neural Networks (CNNs). While fully […]

Expedition Technology Awarded Phase II SBIR to Support Deep Learning-Based Improvements to Over the Horizon Radar for NGA

March 23, 2021 in , , /by

Expedition Technology, Inc. (EXP) announces a Phase II Small Business Innovation Research (SBIR) award to continue the Improved Geolocation for Over the Horizon Radar (IGOR) program for the National Geospatial-Intelligence Agency. This effort seeks to develop and demonstrate a prototype system that processes Over the Horizon Radar (OTHR) returns to produce improved geolocation estimates for […]

Agile Machine Learning: Taming the Wild West of ML Continuous Integration

February 9, 2021 in , , /by

by Michael Person and Carter Brown At Expedition (EXP), we develop state of the art systems for our customers that leverage cutting edge Machine Learning algorithms. While our solutions often overlap with published research, each program requires algorithmic customization, exploration, and experimental hypothesis verification. It was clear early on that, to meet our customers’ requirements, […]

Expedition Technology, Inc. Awarded DARPA Phase 3 Contract for RF Machine Learning

January 29, 2021 in , , /by

Expedition Technology, Inc. (EXP) announces a Phase 3 award to continue support to the Defense Advanced Research Projects Agency (DARPA) for the Radio Frequency Machine Learning System (RFMLS) program.  The goal of RFMLS is to develop the foundations for applying modern data-driven Machine Learning (ML) to the RF Spectrum domain. These innovations form the basis of a […]

Expedition Technology, Inc. Awarded Contract Extension from AFRL to Continue Machine Learning Development

January 7, 2021 in , , /by

Expedition Technology, Inc. (EXP) announces a $5 million, 30-month contract extension to continue 3D object detection and advanced analytic capability development for the Air Force Research Laboratory in support of the National Geospatial-Intelligence Agency (NGA). During the past 2 years under Intelligent Classification in 3D (IC3D) effort, EXP developed a machine learning (ML) based approach […]

Expedition Technology Listed on 2019 NVTC Tech 100

November 20, 2019 in , , /by

This week the Northern Virginia Technology Council (NVTC) announced the 2019 NVTC Tech 100, a list of groundbreaking companies, leaders and innovators within the region’s technology community. The list included Expedition Technology Inc, a growing technology company tackling hard problems by developing robust, modern solutions. Expedition is honored to be recognized as a technology leader, and […]

My Summer at Expedition Technology as a Machine Learning Intern

September 9, 2019 in , /by

by Jesse Ranjit (ed. Andrew Draganov) About three months ago, I started my internship at Expedition Technology Inc. My short time here was loaded with valuable industry experience not taught in the University environment. I had the opportunity to work in the agile software development cycle, to play an active role in code reviews and to truly contribute to an existing code base. […]