Automatic modulation recognition of radar signals based on histogram of oriented gradient via improved principal component analysis

Automatic modulation recognition (AMR) of radar signals plays a critical role in electronic reconnaissance. Current AMR algorithms are mainly based on convolutional neural networks (CNN), which can learn the feature hierarchy by establishing high-level features from low-level features. However, for time–frequency analysis-based methods, distinct spectrum already reflect characteristics. Thus, this study develops novel approach shape descriptors via histograms oriented gradients (HOG) and support vector machine (SVM). Comparison studies with classic CNN-based methods have also been done to reveal superiority designed approach. Experimental results demonstrate that HOG-SVM has more efficient performance. To further enhance classification precision under low signal-to-noise ratios, an improved principal component analysis denoising algorithm is developed improve signal quality intense noise background. Experiments simulated measured proposed accurately distinguish environments.