Under the condition of low SNR, enhancing the precision of parameter estimation for linear frequency modulation (LFM) signals and diminishing the complexity of the relevant methods represent crucial challenges that are presently being confronted. To address this problem, a parameter estimation method for LFM signals based on the High-Resolution Representation network (HRRnet) is proposed. The fundamental concept underlying this method lies in the employment of a strategy that combines the expansion of the receptive field with the fusion of multi-scale features. This enables the efficient extraction of both global and local information, which in turn augments the expressive power of the inherent signal characteristics and consequently mitigates the impact of noise interference. Based on this strategy, a high-resolution representation of the time–frequency spectrum of the signals is performed to improve the distinguishability of the time–frequency spectrum, and it further improve the accuracy of parameter estimation for LFM signals. In addition, the network utilizes dilated convolution to expand the receptive field while reducing the dependence on network depth, so as to control the network complexity and further optimize the computational efficiency. Experimental results show that when the SNR is greater than −12 dB and the tolerable error is equal to 0.1, the average accuracy of the HRRnet method for estimating the initial frequency and frequency modulation coefficient of LFM signals can reach above 95.53% and 91.19%, respectively, and its number of parameters and computational complexity are reduced to more than 20.47% and 20.37% of those of the existing methods.

Automatic Modulation Classification (AMC) aims to automatically identify modulation types based on the features of received signals, and acts as a vital technique for spectrum sensing, cognitive radio and electronic countermeasures. However, existing methods generally overlook the issue that modulation signals with similar characteristics are prone to confusion. In particular, under strong interference conditions, feature distributions become blurred and class boundaries tend to overlap, further exacerbating misclassification among modulation types with similar characteristics, thereby limiting improvements in classification accuracy and model robustness. To address this challenge, a modulation classification method based on fuzzy sample feature enhancement is proposed. Specifically, a modulation class entropy constraint and a fuzzy sample feature enhancement constraint are introduced to establish a Multi-scale Fuzzy Sample Feature Enhancement Framework (MTFSFEF). Through fuzzy sample selection and feature representation refinement, the proposed framework effectively mitigates feature overlap among fuzzy samples and enhances inter-class separability and discriminability. For $\mathrm{SNR}\ge 0\mathrm{dB}$, MTFSFEF delivers superior average classification accuracies across all three benchmark datasets: $92.82\%$ on RML2016.10a, over $93.43\%$ on RML2016.10b, and exceeding $92.78\%$ on RML2018.01a, outperforming existing methods by up to $2.68\%$, $2.89\%$, and $2.73\%$, respectively.