Recognition and Estimation for Frequency-Modulated Continuous-Wave Radars in Unknown and Complex Spectrum Environments

Abstract

The recognition and estimation of frequency-modulated continuous-wave (FMCW) radar signals is critical for both military electronic countermeasures (ECM) and civilian autonomous driving. However, the increasingly complex radio environment poses two new challenges for detection equipment. First, multiple FMCW radars can share the same frequency band, resulting in overlap of received FMCW signals in the time-frequency domain. Second, unexpected signals in unknown spectrum environments can affect the cognitive performance of FMCW signals. This paper proposes a semantic-based learning network (SLN) that simultaneously learns modulation classification and parameter regression of FMCW signals. By integrating recognition and estimation into a single network, the system can be optimized end-to-end as a whole. Additionally, instance-level semantic learning facilitates the parallel analysis of multiple components in overlapping signals. Finally, contrastive clustering in SLN achieves suppression of unexpected signals. Numerous comparative experimental results demonstrate that SLN has the desirable ability to simultaneously recognize and estimate FMCW signals in real-time, even in unknown spectrum environments.