  This paper addresses a critical preliminary step in radar signal processing:
detecting the presence of a radar signal and robustly estimating its bandwidth.
Existing methods which are largely statistical feature-based approaches face
challenges in electronic warfare (EW) settings where prior information about
signals is lacking. While alternate deep learning based methods focus on more
challenging environments, they primarily formulate this as a binary
classification problem. In this research, we propose a novel methodology that
not only detects the presence of a signal, but also localises it in the time
domain and estimates its operating frequency band at that point in time. To
achieve robust estimation, we introduce a compound loss function that leverages
complementary information from both time-domain and frequency-domain
representations. By integrating these approaches, we aim to improve the
efficiency and accuracy of radar signal detection and parameter estimation,
reducing both unnecessary resource consumption and human effort in downstream
tasks.
