The accuracy of low probability intercept (LPI) radar waveform recognition is an important and challenging problem in electronic warfare. Aiming at the difficulty feature extraction rates LPI signal under a signal-to-noise ratio, inspired by symmetry theory, we propose new approach for method based on dual-channel convolutional neural network (CNN) fusion. Our contains three main modules: preprocessing module that converts waveforms into two-dimensional time-frequency images using...

Abstract Time‐frequency analysis based on Wigner‐Ville distribution (WVD) plays a significant role in analysing non‐stationary signals, but it is susceptible to interference from cross‐terms (CTs) for multi‐component signals. To address this issue, novel WVD enhancement method generative adversarial networks (namely WVD‐GAN) proposed, achieve highly‐concentrated time‐frequency (TF) representation. Specifically, deep feature extraction module designed with multiple residual connections the...

Radar signal recognition plays a crucial role in modern electronic reconnaissance systems. With the increasing complexity of electromagnetic environments, radar signals are susceptible to noise interference under low signal-to-noise ratio (SNR) conditions, posing challenge accurate recognition. To address this issue, we propose multilayer decomposition denoising empowered convolutional neural network (CNN) for Specifically, original first processed by denoising, which consists variational...

Image acquisition and transmission in wireless sensor networks (WSN) are core issues for some resource-deficient multimedia sensing applications. Reducing sampling rates data lowers node costs energy, addressing communication bottlenecks. Block compressed (BCS) can meet the above requirements. For BCS, sparsity or smoothness of block signal is a crucial parameter, which determines setting range rate. side node, we cannot directly obtain complete digital signal. Therefore, it becomes...

Anomalous road manhole covers pose a potential risk to safety in cities. In the development of smart cities, computer vision techniques use deep learning automatically detect anomalous avoid these risks. One important problem is that large amount data are required train anomaly cover detection model. The number usually small, which makes it challenge create training datasets quickly. To expand dataset and improve generalization model, researchers copy paste samples from original other order...

The class imbalance problem has been reported to exist in remote sensing and hinders the classification performance of many machine learning algorithms. Several technologies, such as data sampling methods, feature selection-based ensemble-based have proposed solve problem. However, these methods suffer from loss useful information or artificial noise, result overfitting. A novel double ensemble algorithm is deal with multi-class hyperspectral image this paper. This method first computes...

Objectives: To improve the recognition accuracy of radar signals under a low signal-to-noise ratio (SNR). Technology or Method: We propose novel signal method based on dual-channel model with histogram oriented gradients (HOG) feature extraction. Specifically, multisynchrosqueezing transform (MSST) and Choi-Williams distribution (CWD) are adopted individually to obtain time-frequency images signals, HOG extraction is performed preprocessed each channel respectively. Then, features two...

Low probability of intercept (LPI) radar signals are widely used in electronic countermeasures due to their low power and large bandwidth. However, they susceptible interference from noise, posing challenges for accurate identification. To address this issue, we propose an LPI signal recognition method based on feature enhancement with deep metric learning. Specifically, time-domain first transformed into time–frequency images via the Choi–Williams distribution. Then, a network...

To improve the accuracy of Low Probability Intercept (LPI) radar emitter signal identification at low Signal-to-Noise Ratio (SNR), we propose a new approach for LPI recognition with feature fusion based on time-frequency (T-F) transform. First, Choi-Williams distribution (CWD) and ambiguity function (AF) are used to convert signals T-F images, respectively. Then texture features preprocessed images extracted by gray level-gradient co-occurrence matrix (GLGCM). Finally, is realized Support...

In the process of electronic countermeasures, most studies radar signal recognition focus on Additive White Gaussian Noise (AGWN) channel scenarios, while pay less attention to influence deterioration recognition. Therefore, we propose a low interception rate method based time-frequency images under impairment. First, obtain shallow features LPI signals in fading channels by Choi-Williams distribution (CWD) transform that converts images. Second, design parallel residual network model for...

Low probability of intercept (LPI) radar signal recognition under low signal-to-noise ratio (SNR) is a challenging task within electronic reconnaissance systems, particularly when faced with scarce labeled data and limited resources. In this paper, we introduce an LPI method based on semi-supervised Support Vector Machine (SVM). First, utilize the Multi-Synchrosqueezing Transform (MSST) to obtain time–frequency images signals undergo necessary preprocessing operations. Then, image features...

Aiming at the problems of traditional low probability intercept (LPI) radar signal recognition algorithm, which is difficult to extract effective features and accuracy under signal-to-noise ratio (SNR), a method based on multi-synchrosqueezing transform (MSST) two channel feature extraction was proposed. First, MSST used obtain time frequency images, perform series pre-processing operations. Then, image are extracted through gray co-incidence matrix (GLCM) local binary variance mode (LBPV),...

Professional Digital Trunked Communication System (PDT) is typically designed to meet professional Chinese users' requirements. PDT provides a digital trunked system solution with low cost, wide area coverage and probability of smooth upgrading from analog system. In application, often restricted in shortage frequency resource, hand-over performance sometimes not capable trunking requirement, poor metropolitans indoor environment. Simulcast can be used deal these restrictions, based on the...

Community detection is an important research topic in network science and has received extensive attention. aims to reveal clusters or group structures complex networks, many methods have been proposed, including those based on graph convolutional (GCN). However, existing GCN model-based community rely excessively prior labels. Moreover, does not consider the difference node degree distribution, resulting poor feature expression ability. To address aforementioned issues, a self-supervised...

Radar signal recognition under low signal-to-noise ratio (SNR) conditions is a critical issue in modern electronic reconnaissance systems, which face significant challenges accuracy due to diversity. A novel method for radar detection based on the bagging support vector machine (SVM) proposed this paper.This firstly utilizes Choi–Williams distribution (CWD) and smooth pseudo Wigner-Ville (SPWVD) obtain different time–frequency images of signals, effectively leverages CWD’s strong aggregation...

Differential spatial modulation (DSM) is a multi-antenna differential technology with low power consumption and no demand of channel estimation. It suitable for communication systems that have fast migration velocity high requirements both complexity. However, due to the sparse structure coding, spectral efficiency limited. Therefore, media-based (MBM) introduced into system in this work improve spectral. A space time (DSTMBM) proposed. While retaining inherent advantages DSM, additional...

Code-index modulation (CIM) uses the index of spreading code to convey information, which can be integrated with different kinds MIMO systems achieve higher throughput. This paper proposes a novel CIM-integrated space time shift keying scheme called CIM-STSK. The CIM-STSK system employs three types resources transmit information bits, are Hadamard codes, dispersion matrix indexes and QAM constellation symbols. mathematical expression bit error rate is derived, theoretical analysis validated...