Working Mode Recognition of Non-Specific Radar Based on ResNet-SVM Learning Framework.

Mode recognition is a basic task to interpret the behavior of multi-functional radar. The existing methods need to train complex and huge neural networks to improve the recognition ability, and it is difficult to deal with the mismatch between the training set and the test set. In this paper, a learning framework based on residual neural network (ResNet) and support vector machine (SVM) is designed, to solve the problem of mode recognition for non-specific radar, called multi-source joint recognition framework (MSJR). The key idea of the framework is to embed the prior knowledge of radar mode into the machine learning model, and combine the manual intervention and automatic extraction of features. The model can purposefully learn the feature representation of the signal on the working mode, which weakens the impact brought by the mismatch between training and test data. In order to solve the problem of difficult recognition under signal defect conditions, a two-stage cascade training method is designed, to give full play to the data representation ability of ResNet and the high-dimensional feature classification ability of SVM. Experiments show that the average recognition rate of the proposed model, with embedded radar knowledge, is improved by 33.7% compared with the purely data-driven model. Compared with other similar state-of-the-art reported models, such as AlexNet, VGGNet, LeNet, ResNet, and ConvNet, the recognition rate is increased by 12%. Under the condition of 0-35% leaky pulses in the independent test set, MSJR still has a recognition rate of more than 90%, which also proves its effectiveness and robustness in the recognition of unknown signals with similar semantic characteristics.

An End-to-End Deep Learning Approach for State Recognition of Multifunction Radars.

With the widespread use of multifunction radars (MFRs), it is hard for the traditional radar signal recognition technology to meet the needs of current electronic intelligence systems. For signal recognition of an MFR, it is necessary to identify not only the type or individual of the emitter but also its current state. Existing methods identify MFR states through hierarchical modeling, but most of them rely heavily on prior information. In the paper, we focus on the MFR state recognition with actual intercepted MFR signals and develop it by introducing recurrent neural networks (RNNs) of deep learning into the modeling of MFR signals. According to the layered MFR signal architecture, we propose a novel end-to-end state recognition approach with two RNNs' connections. This approach makes full use of RNNs' ability to directly tackle corrupted data and automatically learn the features from input data. So, it is practical and less dependent on prior information. In addition, the hierarchical modeling method applied to the end-to-end network effectively restricts the scale of the end-to-end model so that the model can be trained with a small amount of data. Simulation results on a real MFR show the excellent recognition performance of our end-to-end approach with little prior information.

Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network.

Signal features can be obscured in noisy environments, resulting in low accuracy of radar emitter signal recognition based on traditional methods. To improve the ability of learning features from noisy signals, a new radar emitter signal recognition method based on one-dimensional (1D) deep residual shrinkage network (DRSN) is proposed, which offers the following advantages: (i) Unimportant features are eliminated using the soft thresholding function, and the thresholds are automatically set based on the attention mechanism; (ii) without any professional knowledge of signal processing or dimension conversion of data, the 1D DRSN can automatically learn the features characterizing the signal directly from the 1D data and achieve a high recognition rate for noisy signals. The effectiveness of the 1D DRSN was experimentally verified under different types of noise. In addition, comparison with other deep learning methods revealed the superior performance of the DRSN. Last, the mechanism of eliminating redundant features using the soft thresholding function was analyzed.

Two-Dimensional Angle Estimation of Two-Parallel Nested Arrays Based on Sparse Bayesian Estimation.

To increase the number of estimable signal sources, two-parallel nested arrays are proposed, which consist of two subarrays with sensors, and can estimate the two-dimensional (2-D) direction of arrival (DOA) of signal sources. To solve the problem of direction finding with two-parallel nested arrays, a 2-D DOA estimation algorithm based on sparse Bayesian estimation is proposed. Through a vectorization matrix, smoothing reconstruction matrix and singular value decomposition (SVD), the algorithm reduces the size of the sparse dictionary and data noise. A sparse Bayesian learning algorithm is used to estimate one dimension angle. By a joint covariance matrix, another dimension angle is estimated, and the estimated angles from two dimensions can be automatically paired. The simulation results show that the number of DOA signals that can be estimated by the proposed two-parallel nested arrays is much larger than the number of sensors. The proposed two-dimensional DOA estimation algorithm has excellent estimation performance.

Application of time-reversal in electromagnetic power synthesis under distributed motion platform.

Space-time adaptive focusing is the most prominent feature of time-reversal electromagnetic waves. This paper studies the spatial power synthesis technology of distributed motion platforms based on time-reversal electromagnetic waves. Firstly, the spatial power synthesis process based on time-reversal on a distributed fixed platform is modeled. Then, the time-reversal signal processing process of a distributed array on the motion platform is deduced, and the feasibility of realizing precise power focusing is verified in theory. Finally, the factors affecting the power synthesis effect in the target area are analyzed. The simulation results indicate that the spatial power synthesis on the distributed motion platform based on the time-reversal can effectively balance between power focusing effect and computational complexity. Also, the proposed method has better efficiency than the existing techniques, and it has strong practicability and feasibility.

The application of polysaccharide-based nanogels in peptides/proteins and anticancer drugs delivery.

Finding adequate carriers for proteins/peptides and anticancer drugs delivery has become an urgent need, owing to the growing number of therapeutic macromolecules and the increasing amount of cancer incidence. Polysaccharide-based nanogels have attracted interest as carriers for proteins/peptides and anticancer drugs because of their characteristic properties like biodegradability, biocompatibility, stimuli-responsive behaviour, softness and swelling to help achieve a controlled, triggered response at the target site. In addition, the groups of the polysaccharide backbone are able to be modified to develop functional nanogels. Some polysaccharides have the intrinsic ability to recognise specific cell types, allowing the design of targeted drug delivery systems through receptor-mediated endocytosis. This review is aimed at describing and exploring the potential of polysaccharides that are used in nanogels which can help to deliver proteins/peptides and anticancer drugs.