Unlocking Signal Processing With Image Detection: A Frequency Hopping Detection Scheme for Complex EMI Environments Using STFT and CenterNet

Abstract

Accurate detection and parameter estimation of frequency hopping (FH) signals remain challenging in FH signal-based transmission systems. This study proposes a scheme combining time-frequency analysis (TFA) and deep learning (DL)-based image processing algorithms to alleviate the degradation of detection accuracy and estimation performance caused by complex electromagnetic interference (EMI). A short-time Fourier transform (STFT) was used to obtain the signal spectrogram, which reflects the signal energy in a concentration-dependent manner. Then, a CenterNet-based deep network was employed to identify each FH hop's shape and position, reducing the computational burden via a lightweight neural network while maintaining high recognition accuracy. Inverse mapping from the coordinates to the spectrogram was used to perform parameter estimation in the time-frequency (TF) domain. The estimation error was reduced by precisely locating the centroid of the signal energy using CenterNet. The simulation results demonstrate that the proposed scheme can accurately estimate the FH signal at a low signal-to-noise ratio (SNR) with complex EMI. Furthermore, appropriately determining the optimal parameters of CenterNet to ensure the estimator performance provides a novel approach for integrating DL into signal detection and estimation in complex EMI environments.

Low False Alarm and Narrow-Wide Band Compatible Signal Detection Algorithm Combining the Multiscale Wavelet Transform Extremum Detection With the Spectrum Energy Detection

“…• Partial areas of wideband signal may be detected as the narrowband signals at a small scale because of the fluctuation within signal bandwidth. An example is shown in Figure 6, three narrowband signals are falsely detected within the wideband signal at scale 2 6 ;…”

“…Through the estimation and clustering of the noise top of the modified spectrum, the adaptive energy detection threshold estimation value is obtained. The wavelet transform of the wideband spectrum at scale 2 6 and 2 10 are shown in Figure 11. It can be found that the extreme values of the wavelet transform corresponding to the edge of the narrowband signal are obvious at small-scale transform, while the extreme values of the wavelet transform corresponding to the edges of the wideband signal are obvious at the large-scale transform.…”

“…Signal detection is the primary task of the signal reconnaissance and the basis of the signal analysis [1]- [6]. Early classical signal detection techniques, including the energy detection(ED), matched-filter detection (MFD), and cyclic stationary feature detection (CFD), are proposed for narrow-band systems [7]- [9].…”

Adaptive Joint Carrier and DOA Estimations of FHSS Signals Based on Knowledge-Enhanced Compressed Measurements and Deep Learning

“…From the aspect of signal analysis, the transform-based methods, including short-time Fourier transform (STFT)-based [3][4][5][6], wavelet transform-based [7][8][9][10], and autocorrelation analysis-based [11] methods, are the most straightforward approaches for signal parameter estimations. Even in recent years, transform-based methods have continued to be extensively developed.…”