72 research outputs found
DNCNet: deep radar signal denoising and recognition
No full textDeep learning with its rapid development and advancement has achieved unparalleled performance in many areas like computer vision as well as cognitive radio and signal recognition. However, the performance of most deep neural networks would suffer from degradation in the data mismatch scenario, e.g., the test dataset has a related but nonidentical distribution with the training dataset. Considering the noise corruption, a classifier's accuracy might drop sharply when it is tested on a dataset with much lower signal-to-noise ratio compared to its training dataset. To address this dilemma, in this work, we propose an efficient denoising and classification network (DNCNet) for radar signals. The DNCNet consists of denoising and classification subnetworks. First, a radar signal detection and synthetic mechanism is designed to generate pairwise clean data and noisy data for the DNCNet to train its denoising subnetwork. Then, a two-phase training procedure is proposed to train the denoising subnetwork in the first phase and strengthen the mapping between the denoising results and perceptual representation in the second. Experiments on synthetic and benchmark datasets validate the excellent performance of the proposed DNCNet against state-of-the-art methods in terms of both signal restoration quality and classification accuracy.</p
Balanced Neural Architecture Search and Its Application in Specific Emitter Identification
No full textThe performance of a single neural network can vary unexpectedly corresponding to different classification tasks, and thus the network with fixed structure may lack flexibility and often lead to overfitting or underfitting. It is urgent, also the main objective of this paper, to deal with the limitation of the fixed neural network structure on classifying radar signals in different electromagnetic environments. We in this paper propose a variable network architecture search (NAS) mechanism, called balanced-NAS framework, and apply it in specific emitter identification (SEI) to greatly improve the flexibility of model searching. In the proposed balanced-NAS framework, a 'block-cell' structure and a controller based recurrent neural network (RNN) are utilized to design models automatically according to external environment. In particular, a balance function is also proposed and utilized in the balanced-NAS framework, acting on the RNN controller to take both the validation accuracy and computational budget into consideration while searching for ideal models. The efficiency of the searching process is further enhanced by exploiting a progressive strategy to design simple and complicate child models where unpromising ones after each evaluation process are obsoleted to release searching space. Simulations and experiments indicate that the proposed balanced-NAS framework is extremely efficient and outperforms the conventional algorithms in classifying radar signals in different environments.</p
Robust Bayesian attention belief network for radar work mode recognition
No full textUnderstanding and analyzing radar work modes play a key role in electronic support measure system. Many classifiers, for example those based on convolutional neural network (CNN) and recurrent neural network (RNN), are available for recognizing radar work modes as well as emitter types from their waveform parameters. However, the performance of these methods may suffer significantly when confronting different types of signal degradation, e.g., measurement error, lost pulse and spurious pulse. To tackle this issue, we in this paper develop a Bayesian attention belief network (BABNet) based on Bayesian neural networks in which the probability distribution over weights can help to enhance the model robustness for corrupted data. In particular, we adopt pre-trained CNN as the Bayesian inference prior. This not only accelerates the convergence speed, but also avoids the training process getting stuck in bad local minima. Meanwhile, instead of using RNNs which are difficult to be implemented in parallel, the combination of padding operation and attention module in the proposed BABNet enables CNN, as the backbone, to process sequential data with variable length. Extensive experiments are conducted to demonstrate the recognition capability and robustness of the BABNet in different environments
Balanced neural architecture search and optimization for specific emitter identification
No full textFixed-structure neural network lacks flexibility when tackling different classification tasks, prompting a growing interest in developing automated neural architecture search (NAS) methods. Approaches so far mainly consider the classification accuracy of the searching results for NAS, yet another important factor, the computation cost, is ignored. In this paper, a feasibility problem is modeled subject to specific constraints in terms of both the classification accuracy and computation cost, which can greatly enhance the flexibility against the fixed 'balanced function' proposed in recent work in identifying radar signals in different electromagnetic environments. Moreover, to be able to traverse the infinite feasible region formed by the constraints, we propose a simple yet effective method based on the Gaussian process regression model by fine-tuning an initialized balanced function and leveraging a data distribution that meets the constraints. Experimental results demonstrate the superiority of the proposed NAS technique in designing comparably accurate network structures against manually-designed models, with less computation cost compared to conventional NAS algorithms. </p
Correction: Zhang et al. The Impact of Green Financial Policy on the Regional Economic Development Level and AQI—Evidence from Zhejiang Province, China. Sustainability 2023, 15, 4068
No full textRegarding the author correction request, it has been clarified that the original paper was composed by Daping Zhang, Pinzhen Cheng, Minxing Wang, Zhenming Chen and Lufei Huang [...
An End-to-End Deep Learning Approach for State Recognition of Multifunction Radars
No full textWith 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
Two-Dimensional Angle Estimation of Two-Parallel Nested Arrays Based on Sparse Bayesian Estimation
No full textTo 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
Fatigue crack growth in ceramics containing a viscous grain boundary phase at elevated temperatures
No full textElevated-temperature crack growth behavior in a commercial Al\sb2O\sb3 and a hot-pressed 30 vol.% TiB\sb2-SiC composite was examined under tensile static loading (static fatigue) and tension-tension cyclic loading (cyclic fatigue). The study was carried out at temperatures of 700-900\sp\circC, where the vitreous grain boundary phase flowed viscously. Experimental results have shown the existence of cyclic fatigue in these materials, but the cyclic effect cannot be seen as the consequence of a static fatigue mechanism, although under both cyclic and static loading conditions crack propagation assumed an intergranular fracture mode. The testing temperature, load ratio, and cyclic frequency were found to exert significant effects on cyclic fatigue-crack growth behavior. A damage zone was observed ahead of the crack tip in which grain boundary cavitation (or microcracking) occurred during fatigue-crack growth. An analytical model based upon the damage accumulation in the grain boundary phase was developed that successfully predicted the frequency and load ratio dependencies of crack growth. Values of activation energy for cyclic and static fatigue crack growth were approximately the same. Fracture mechanisms in both cases were also found to be similar. However, crack growth under static loads was faster than that under cyclic loads at the same maximum stress intensity. Such a difference in the growth rate suggested that the damage accumulation in the grain boundary phase differed during cyclic and static fatigue processes. In the TiB\sb2-SiC composite, cyclic fatigue-crack growth at elevated temperatures was affected by oxide-induced crack closure and showed an anomalous temperature dependence. After subtracting crack closure, cyclic fatigue-crack growth exhibited a temperature dependence that was governed by the viscous flow of the grain boundary phase.Made available in DSpace on 2011-05-07T14:23:12Z (GMT). No. of bitstreams: 2
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