2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2020
DOI: 10.1109/spawc48557.2020.9154324
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Frequency Invariant Beampatterns for Wideband Synthetic Aperture Channel Sounders

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Cited by 4 publications
(7 citation statements)
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“…1) Detection Range: The Friis equation defined in [4] can be used to compute the maximum detection range possible given the transmit and receive antennas of a channel sounder. In a LoS geometry, the Friis equation for detection range is…”
Section: A Frequency Domain Sa Soundersmentioning
confidence: 99%
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“…1) Detection Range: The Friis equation defined in [4] can be used to compute the maximum detection range possible given the transmit and receive antennas of a channel sounder. In a LoS geometry, the Friis equation for detection range is…”
Section: A Frequency Domain Sa Soundersmentioning
confidence: 99%
“…With a frequency domain channel sounder one can design an optimized beamformer for every beam-steering direction at each discrete measurement frequency as described in [4]. Fig.…”
Section: Frequency Invariant Array Responsementioning
confidence: 99%
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“…One major challenge is the presence of noise in the received signals, which can degrade the accuracy of waveform analysis and identification. Techniques to mitigate the impact of noise, such as denoising algorithms or windowing methods, need to be employed.Additionally, the classification and recognition of radar signal waveforms require robust and efficient algorithms [5]. Overall, the challenges associated with the generation and analysis of radar signal waveforms include waveform design complexity, noise interference, complex time-frequency characteristics, and the development of robust classification algorithms.…”
Section: Introductionmentioning
confidence: 99%
“…From the author of [5]ResXNet was proposed, with a novel multiscale lightweight attention model, the model has a larger receptive field and a novel grouped residual structure to improve the feature representation capacity of the model. In addition, the convolution block attention module (CBAM) is utilized to effectively aggregate channel and spatial information, enabling the convolutional neural network model to extract features more effectively.…”
Section: Introductionmentioning
confidence: 99%