TY - GEN
T1 - End-to-end Learning of Waveform Generation and Detection for Radar Systems
AU - Jiang, Wei
AU - Haimovich, Alexander M.
AU - Simeone, Osvaldo
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - An end-to-end learning approach is proposed for the joint design of transmitted waveform and detector in a radar system. Detector and transmitted waveform are trained alternately: For a fixed transmitted waveform, the detector is trained using supervised learning so as to approximate the Neyman-Pearson detector; and for a fixed detector, the transmitted waveform is trained using reinforcement learning based on feedback from the receiver. No prior knowledge is assumed about the target and clutter models. Both transmitter and receiver are implemented as feedforward neural networks. Numerical results show that the proposed end-to-end learning approach is able to obtain a more robust radar performance in clutter and colored noise of arbitrary probability density functions as compared to conventional methods, and to successfully adapt the transmitted waveform to environmental conditions.
AB - An end-to-end learning approach is proposed for the joint design of transmitted waveform and detector in a radar system. Detector and transmitted waveform are trained alternately: For a fixed transmitted waveform, the detector is trained using supervised learning so as to approximate the Neyman-Pearson detector; and for a fixed detector, the transmitted waveform is trained using reinforcement learning based on feedback from the receiver. No prior knowledge is assumed about the target and clutter models. Both transmitter and receiver are implemented as feedforward neural networks. Numerical results show that the proposed end-to-end learning approach is able to obtain a more robust radar performance in clutter and colored noise of arbitrary probability density functions as compared to conventional methods, and to successfully adapt the transmitted waveform to environmental conditions.
KW - Radar waveform design
KW - neural network
KW - radar detector design
KW - reinforcement learning
KW - supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85083305123&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083305123&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF44664.2019.9049027
DO - 10.1109/IEEECONF44664.2019.9049027
M3 - Conference contribution
AN - SCOPUS:85083305123
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1672
EP - 1676
BT - Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Y2 - 3 November 2019 through 6 November 2019
ER -