The recognition and estimation of frequency-modulated continuous-wave (FMCW) radar signals are critical for both military electronic countermeasures 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 an 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 article 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.
Recognition and Estimation for Frequency-Modulated Continuous-Wave Radars in Unknown and Complex Spectrum Environments
IEEE Transactions on Aerospace and Electronic Systems ; 59 , 5 ; 6098-6111
2023-10-01
2007839 byte
Article (Journal)
Electronic Resource
English
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