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PSEUDOLITHIC, INC. STTR Phase I Award, August 2022

A STTR Phase I contract was awarded to PSEUDOLITHIC, INC. in August, 2022 for $172,904.0 USD from the U.S. Department of Defense and United States Army.

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sbir.gov/node/2319321
Is a
SBIR/STTR Awards
SBIR/STTR Awards

SBIR/STTR Award attributes

SBIR/STTR Award Recipient
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PSEUDOLITHIC, INC.
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Government Agency
U.S. Department of Defense
U.S. Department of Defense
0
Government Branch
United States Army
United States Army
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Award Type
STTR0
Contract Number (US Government)
W911NF-22-P-00520
Award Phase
Phase I0
Award Amount (USD)
172,9040
Date Awarded
August 1, 2022
0
End Date
January 31, 2023
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Abstract

PseudolithIC Inc. and Michigan State University (MSU) are pleased to offer the CoUNTER proposal to address the threat posed by the proliferation of Unmanned Aerial Systems (UASs) with a codesigned hardware-signal processing approach. This proposal combines the benefits of mm-Wave signals, advanced MIMO architectures and novel micro-Doppler signal processing to deliver superior active radar performance for this mission. The use of COTS silicon chips, such as those used in automotive radars at 77 GHz, are exploited to dramatically lower system cost.  PseudolithIC will leverage its proprietary technology to augment the COTS performance via a commercialized heterogeneous integration process to realize chip scale mm-Wave solutions that combine the analog performance of compound semiconductors with the cost and digital integration of silicon circuits. Additionally, the team will incorporate the advanced radar signal processing capabilities of the MSU team to deliver a comprehensive solution with a compelling combination of radar performance and affordability.  More specifically, the CoUNTER solution will realize an integrated radar system that incorporates the three key features of (1) low-cost, high-power mm-Wave aperture enabled by PLIC’s proprietary integration technology augmenting COTS radar chips, (2) high performance discrimination software built from the demonstrated performance of MSU micro-Doppler techniques and machine learning algorithms (>90% accuracy demonstrated), and (3) an integrated testbed to provide quantitative analysis of system design and accelerate machine learning process for correlating model and experiment.  Finally, the PseudolithIC team will integrate the hardware and software into a complete radar system solution.

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