Multicomponent Radar FM Signal Detection With Multiple Hypothesis Frequency Tracking
Chao He, Lei Zhang, Song Wei, Shao-Peng Wei, Yuyuan Fang · IEEE Transactions on Aerospace and Electronic Systems · 2025
Detecting the non-cooperative radar signal received by an electronic support (ES) receiver is integral to cognizing non-cooperative radars' electronic behaviors. However, due to the receiver's broad beam and large bandwidth in the reconnaissance scenario, quantities of radar signals are intercepted, contributing to the challenge of detecting the multi-component signal. At the same time, sidelobe emission and long-distance transmission of the signal vastly decrease the power of the intercepted signal, resulting in the challenge of detecting signals with a low signal-to-noise ratio (SNR). This paper proposes the multiple hypothesis frequency tracking (MHFT) algorithm to address the multi-component radar frequency-modulated (FM) signal detection against low SNR. Using a high false-alarm-probability TF detecter, the proposed algorithm generates time-frequency (TF) measurements of both the radar signals and the noise from the intercepted signal. Subsequently, the algorithm assesses the correlation of these raw measurements by tracking the signal's frequency and further extracts TF trajectories to ultimately achieve FM radar signal detection. This paper re-models the hypothesis probability and the MHFT algorithm's hypothesis procedure to achieve robust and efficient measurement correlation under the signal reconnaissance scenario. By tracking the frequency, delaying decisions, and determining the local optimal measurement assignment, the proposed algorithm can effectively exclude the influence of false alarms against low SNR and accurately correlate the TF traces of multi-component radar signals. According to comparison experiments, the proposed algorithm can effectively detect multi-component radar FM signals against lower SNR.