Document Type
Dissertation
Date of Award
8-31-2021
Degree Name
Doctor of Philosophy in Electrical Engineering - (Ph.D.)
Department
Electrical and Computer Engineering
First Advisor
Alexander Haimovich
Second Advisor
Osvaldo Simeone
Third Advisor
Ali Abdi
Fourth Advisor
Joerg Kliewer
Fifth Advisor
Eliza Zoi-Heleni Michalopoulou
Abstract
In this dissertation, the problem of data-driven joint design of transmitted waveform and detector in a radar system is addressed. Two novel learning-based approaches to waveform and detector design are proposed based on end-to-end training of the radar system. The first approach consists of alternating supervised training of the detector for a fixed waveform and reinforcement learning of the transmitter for a fixed detector. In the second approach, the transmitter and detector are trained simultaneously. Various operational waveform constraints, such as peak-to-average-power ratio (PAR) and spectral compatibility, are incorporated into the design. Unlike traditional radar design methods that rely on rigid mathematical models, it is shown that radar learning can be robustified to uncertainties about environment by training the detector with synthetic data generated from multiple statistical models of the environment. Theoretical considerations and results show that the proposed methods are capable of adapting the transmitted waveform to environmental conditions while satisfying design constraints.
Recommended Citation
Jiang, Wei, "Learning of radar system for target detection" (2021). Dissertations. 1742.
https://digitalcommons.njit.edu/dissertations/1742
