Neural Network-Based Digital Predistortion and Self-Interference Cancellation in a Quadrature Balanced Full Duplex Transmitter

Loebl, Erez; Ginzberg, Nimrod; Cohen, E.

doi:10.1109/comcas52219.2021.9629003

Cited by 3 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the field of artificial intelligence has received significant weight as a practical solution to a variety of problems, and it has received a significant scope of research in the fields of computer vision [31], voice processing [32], radar [33], and more [34]. Many studies have reported great contributions to in accuracy and lowered complexity [35,36] using AI-based processing compared to classic solutions that do not include AI.…”

Section: Artificial-intelligence-based Detection and Classificationmentioning

confidence: 99%

Neural-Network-Based Target Classification and Range Detection by CW MMW Radar

Richter,

Balal,

Pinhasi

2023

Remote Sensing

View full text Add to dashboard Cite

This study presents a reliable classification of walking pedestrians and animals using a radar operating in the millimeter waves (mmW) regime. In addition to the defined targets, additional targets were added in an attempt to fool the radar and to present the robustness of the proposed technique. In addition to the classification capabilities, the presented scheme allowed for the ability to detect the range of targets. The classification was achieved using a deep neural network (DNN) architecture, which received the recordings from the radar as an input after the pre-processing procedure. Qualitative detection was made possible due to the radar’s operation at extremely high frequencies so that even the tiny movements of limbs influenced the detection, thus enabling the high-quality classification of various targets. The classification results presented a high achievable accuracy even in the case where the targets attempted to fool the radar and mimicked other targets. The combination of the use of high frequencies alongside neural-network-based classification demonstrated the superiority of the proposed scheme in this research over the state of the art. The neural network was analyzed with the help of interpretable tools such as explainable AI (XAI) to achieve a better understanding of the DNN’s decision-making process and the mechanisms via which it was able to perform multiple tasks at once.

show abstract