Aircraft Radar Altimeter Interference Mitigation Through a CNN-Layer Only Denoising Autoencoder Architecture
Denoising autoencoders for signal processing applications have been shown to experience significant difficulty in learning to reconstruct radio frequency communication signals, particularly in the large sample regime. In communication systems, this challenge is primarily due to the need to reconstru...
Gespeichert in:
Hauptverfasser: | , , , , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Denoising autoencoders for signal processing applications have been shown to
experience significant difficulty in learning to reconstruct radio frequency
communication signals, particularly in the large sample regime. In
communication systems, this challenge is primarily due to the need to
reconstruct the modulated data stream which is generally highly stochastic in
nature. In this work, we take advantage of this limitation by using the
denoising autoencoder to instead remove interfering radio frequency
communication signals while reconstructing highly structured FMCW radar
signals. More specifically, in this work we show that a CNN-layer only
autoencoder architecture can be utilized to improve the accuracy of a radar
altimeter's ranging estimate even in severe interference environments
consisting of a multitude of interference signals. This is demonstrated through
comprehensive performance analysis of an end-to-end FMCW radar altimeter
simulation with and without the convolutional layer-only autoencoder. The
proposed approach significantly improves interference mitigation in the
presence of both narrow-band tone interference as well as wideband QPSK
interference in terms of range RMS error, number of false altitude reports, and
the peak-to-sidelobe ratio of the resulting range profile. FMCW radar signals
of up to 40,000 IQ samples can be reliably reconstructed. |
---|---|
DOI: | 10.48550/arxiv.2410.03423 |