RADAR-EMBEDDED SATCOM WITH DEEP NEURAL NETWORK DEMODULATION
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Authors
Liu, Christopher Y.
Subjects
SATCOM
RADAR
machine learning
radar-embedded comms
AI
RADAR
machine learning
radar-embedded comms
AI
Advisors
Romero, Ric
Karpenko, Mark
Date of Issue
2020-09
Date
September 2020
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
In this work, the feasibility, design, and implementation of radar-embedded communications with
satellite applications are investigated. We design a deep neural network (DNN) machine learning detector to
demodulate SATCOM data. The performance result is compared with the detection method of using
maximum likelihood estimation (MLE) to estimate the amplitude and phase of the radar signal, which is
followed by a maximum likelihood detection (MLD) receiver. Pulsed radar and linear frequency modulation
(LFM) waveforms are chosen to embed communications symbols. Quaternary phase-shift keying (QPSK)
and eight phase-shift keying (8PSK) modulations are used for illustration. In this work, three DNN
demodulators for radar-embedded communications are developed. One of the DNN detectors actually
outperforms the MLD demodulator and is shown to be robust for pulsed radar-embedded communications.
One of our goals is to embed satellite communications into LFM waveform, which is used in synthetic
aperture radar (SAR). The DNN works well for LFM radar-embedded communications when the received
LFM phase offset is removed a priori. However, the DNN symbol error rate (SER) performance suffers
when the LFM phase offset is introduced for large RCR. Lastly, we perform laboratory transmission and
reception tests: a) shielded cable and b) over-the-air (OTA) tests. It is shown that pulsed radar-embedded
communication is feasible with both MLE-MLD and DNN detectors with reasonable SER performance.
Type
Thesis
Description
Approved for public release. Distribution is unlimited.
Series/Report No
Department
Mechanical and Aerospace Engineering (MAE)
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Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.