Accurate recognition of radar modulation mode helps to better estimate radar echo parameters, thereby occupying an advantageous position in the radar electronic warfare (EW). However, under low signal-to-noise ratio environments, recent deep-learning-based radar signal recognition methods often perform poorly due to the unsuitable denoising preprocess. In this paper, a denoising-guided disentangled network based on an inception structure is proposed to simultaneously complete the denoising and recognition of radar signals in an end-to-end manner. The pure radar signal representation (PSR) is disentangled from the noise signal representation (NSR) through a feature disentangler and used to learn a radar signal modulation recognizer under low-SNR environments. Signal noise mutual information loss is proposed to enlarge the gap between the PSR and the NSR. Experimental results demonstrate that our method can obtain a recognition accuracy of 98.75% in the −8 dB SNR and 89.25% in the −10 dB environment of 12 modulation formats.

Civil receivers of Global Navigation Satellite System (GNSS) are vulnerable to spoofing and jamming attacks due to their signal structures. The Spreading Code Authentication (SCA) technique is one of the GNSS message encryption identity authentication techniques. Its robustness and complexity are in between Navigation Message Authentication (NMA) and Navigation Message Encryption (NME)/Spreading Code Encryption (SCE). A commonly used spreading code authentication technique inserts unpredictable chips into the public spreading code. This method changes the signal structure, degrades the correlation of the spreading code, and causes performance loss. This paper proposes a binary phase hopping based spreading code authentication technique, which can achieve identity authentication without changing the existing signal structure. Analysis shows that this method can reduce the performance loss of the original signal and has good compatibility with the existing receiver architecture.

To better understand and evaluate the GNSS positioning performance, it is convenient to adopt corresponding measures to reduce the impact of errors on positioning. A GNSS positioning error estimation scheme based on an improved multi-layer perceptron model is proposed. The multi-head attention mechanism and gating operation are integrated into the multi-layer perceptron model to adaptively select and filter features, enhancing the model’s ability to understand input features. First, the original positioning error of the satellite is obtained through the Kalman filter positioning method. The data are then preprocessed to extract available features. Finally, the features are input into the constructed model for training and testing to obtain the estimated positioning error value. Two types of comparative experiments were completed. The performance of the presented model is evaluated by the root mean square error. Experimental results show that the proposed method performs well in terms of performance indicators, and has obvious advantages over other state-of-the-art methods. In particular, the root mean square error of the presented method in the first dataset is 0.239 m, which is $39.2\%$ and $17\%$ lower than the current state-of-the-art long short-term memory network and convolutional neural network, respectively. The presented method can provide higher-precision estimated values for studying the GNSS positioning error estimation problem.

In addressing the limitations of the self-attention mechanism, particularly in handling local features and channel adaptivity for modulation recognition tasks, this paper presents the Window Attention Convolution Network (WACN). The proposed approach partitions the input In-phase and Quadrature (IQ) signals into multiple local windows. Within these windows, attention computation constructed by deep convolution, deep dilated convolution and pointwise convolution is used to analyze local features and extract important information. Comprehensive evaluations based on four publicly available datasets demonstrate the superiority of WACN in terms of classification accuracy, comparing to other seven state-of-the-art models based on CNN, RNN, Transformer and hybrid networks. It is worth noting that on the HisarMod2019.1 dataset, WACN’s recognition accuracy is more than 85% at the lowest SNR, and the overall recognition accuracy is more than 95%. At 0dB, WACN significantly enhances accuracy compared to traditional modulation recognition methods. The implementation details and source code are available at github1.