Intra-pulse Feature Research Articles

Radar Emitter Classification Based on a Multiperspective Collaborative Clustering Method and Radar Characteristic Spectrum

Modern cognitive electronic reconnaissance methods for radar systems must contend with the complex electromagnetic environments arising from the deployment of multiple signal sources and radar countermeasures, which greatly limit access to the degree of prior information required to enable effective target recognition. The present work addresses this issue by proposing a multiperspective collaborative clustering method for sorting radiation sources based on the multiperspective information of radar signals. In contrast to conventional collaborative training approaches, which are suitable only for semisupervised learning, the proposed multiperspective collaborative clustering method performs unsupervised clustering, cluster label transfer, and dimensionality reduction by linear discriminant analysis iteratively based on the differences between the clustering results obtained from two signal perspectives radiation signal sorting can be conducted in a noncooperative context. The results of comparative experiments demonstrate that the proposed multiperspective sorting method can make full use of the difference information between basic signal characteristics and intrapulse features and thereby improve the accuracy of clustering-based radiation source sorting. Accordingly, the sorting ability of the proposed method is superior to those of other state-of-the-art clustering methods and that of the single-perspective clustering-based sorting method.

Radar Emitter Identification Based on Auto-Correlation Function and Bispectrum via Convolutional Neural Network

This article proposes to apply the auto-correlation function (ACF), bispectrum analysis, and convolutional neural networks (CNN) to implement radar emitter identification (REI) based on intrapulse features. In this work, we combine ACF with bispectrum for signal feature extraction. We first calculate the ACF of each emitter signal, and then the bispectrum of the ACF and obtain the spectrograms. The spectrum images are taken as the feature maps of the radar emitters and fed into the CNN classifier to realize automatic identification. We simulate signal samples of different modulation types in experiments. We also consider the feature extraction method directly using bispectrum analysis for comparison. The simulation results demonstrate that by combining ACF with bispectrum analysis, the proposed scheme can attain stronger robustness to noise, the spectrograms of our approach have more pronounced features, and our approach can achieve better identification performance at low signal-to-noise ratios.

Radar Signal Sorting Method Based on Radar Coherent Characteristic

Aiming at the problem of reliability reduction of signal sorting in terms of the traditional five parameters and intrapulse feature in a complex electromagnetic environment, a new signal sorting method based on radar coherent characteristics is proposed. The main idea of this method is using spectrum analysis to obtain the spectrum images of coherent and noncoherent signals. Image-processing technology is used to extract the feature difference between the two spectrum images, and the central-moment feature is introduced to describe this difference. Through simulation analysis, the feasibility of using the central-moment feature as the coherent feature for signal sorting was proved. In order to check the effectiveness of the proposed feature, a number of simulations were conducted to demonstrate the sorting capability in terms of the coherent feature. From the simulations, it can be seen that the proposed feature not only can be used as a new feature for signal sorting but also that it can be utilized as a supplement for five typical parameters and the intrapulse feature to improve the sorting accuracy rate. Simulations also showed the proposed method could achieve satisfactory sorting results in a low signal-to-noise ratio (SNR). When the SNR was 5 dB, the sorting accuracy rate could reach 98%.

Power-based pulsed radar detection using wavelet denoising and spectral threshold with pattern analysis

AbstractIn this paper, an algorithm for extracting and localizing a radar pulse in a noisy environment is described. The algorithm combines two powerful tools: wavelet denoising and the short-time Fourier transform (STFT) analysis with statistical-based threshold. We aim to detect radar pulses transmitted by any radar in blind mode regardless of the intra-pulse modulation and parametric features. The use of the proposed technique makes the detection and localization of radar pulses possible under very low signal-to-noise ratio conditions (−18 dB), which leads to a reduction of the required signal power or alternatively extends the detection range of radar systems. Radar classes pattern-based analysis is used in blind mode to decrease the probability of false alarm.

Research and Experiment of Radar Signal Support Vector Clustering Sorting Based on Feature Extraction and Feature Selection

The result of radar signal sorting directly affects the performance of electronic reconnaissance equipment. Sorting method based on intra-pulse features has become a research focus in recent years. However, as the number of extracted features increases, the dimension of the feature vector becomes higher and higher. And too many dimensional feature vectors would make the complexity of the sorting algorithm increase geometrically. In this way, feature selection becomes more and more necessary. Combining the latest research on fuzzy rough sets, this paper proposes two feature selection methods, namely two-steps attribute reduction based on fuzzy dependency (TARFD) algorithm and fuzzy rough artificial bee colony (FRABC) algorithm. The TARFD method uses the candidate attribute set as starting point, according to the definition of the redundant attribute set. Then the less important attributes are successively eliminated. The FRABC method starts from the dependence degree of fuzzy rough set, and constructs a fitness function that reflects the importance of the attribute subset and the reduction rate. Based on this function, the artificial bee colony algorithm is used to reduce the attributes of the dataset. Using the TARFD and FRABC algorithms, the extracted feature sets, including entropy feature set, Zernike moment feature set, pseudo Zernike feature set, gray level co-occurrence matrix (GLCM) feature set, and Hu-invariant moment feature set are processed, then an optimal feature subset was obtained and a sorting test was performed. The results show the effectiveness of the extracted intra-pulse features and the efficiency of the feature selection algorithm.

Jamming decision under condition of incomplete jamming rule library

To solve the problem of the low rate of template matching (TM) jamming decision method under the incomplete jamming rule library condition in traditional electronic warfare, a clustering and resampling – support vector machine (CRS-SVM) jamming decision method, is proposed. For the air-to-air scene of airborne multifunctional fire control radar, the inter-pulse pulse and intra-pulse features are extracted, and a jamming rule library that is divided into the source domain and target domain sample according to the missing rate is constructed in this scene. DBSCAN algorithm is used to cluster the dataset, and the structure information is found. The training samples are generated via resampling. SVM is used to transform the inner product of the target domain space into the inner product of the feature space to establish the feature space hyperplane. The target domain samples are labelled as the corresponding jamming style label to realise the fast and effective decision. The experiment results show that the proposed method can effectively improve the accuracy and robustness of jamming decision compared with the traditional TM method.

A Real-time Intra-pulse Recognition Method of Radar Signals Based on Restricted Boltzmann Machines

Intra-pulse features extraction of radar is of great research significance in electronic reconnaissance technology. With continuous development and equipment of the new system radar, the characteristics of the modern electromagnetic environment could be summarized as density, complexity and variability, which makes the traditional signal identification methods difficult to achieve the desired effect and determines the recognition must be real-time. Therefore, this paper is devoted to the study a fast method and performance analysis in extracting intra-pulse features of radar emitters in complex electromagnetic environment, especially in low SNR environment, and the experiment process could achieve real-time recognition.

Radar Emitter Signal Intra-Pulse Feature Extraction Based on Empirical Mode Decomposition

Aiming at this problem that the analysis of instantaneous frequency for radar emitter signal(RES), based on empirical mode decomposition(EMD) the intra-pulse feature extraction method for RES is studied. Using empirical mode decomposition the normalized energy entropy (NeEn) feature is extracted. The experiment result displays that if the signal-to-noise ratio is greater than 12 dB, the feature shows only small fluctuations.

Radar Signal Intra-Pulse Feature Extraction Based on Improved Wavelet Transform Algorithm

With the new system radar put into practical use, the characteristics of complex radar signals are changing and developing. The traditional analysis method of one-dimensional transformation domain is no longer applicable to the modern radar signal processing, and it is necessary to seek new methods in the two-dimensional transformation domain. The time-frequency analysis method is the most widely used method in the two-dimensional transformation domain. In this paper, two typical time-frequency analysis methods of short-time Fourier transform and Wigner-Ville distribution are studied by analyzing the time-frequency transform of typical radar reconnaissance linear frequency modulation signal, aiming at the problem of low accuracy and sen-sitivity to the signal noise of common methods, the improved wavelet transform algorithm was proposed.

Subtle intrapulse feature extraction based on CEEMDAN for radar signals

Subtle intrapulse feature extraction based on CEEMDAN for radar signals

Feature evaluation of radar signal based on aggregation, discreteness and divisibility

Quality of intrapulse feature about radar signals has proved to be a significant foundation to decide whether the signals can be differentiated effectively.For evaluating the quality quantitatively,a method adopting fuzziness and close-degree to evaluate intrapulse feature aggregation and discreteness of signals was proposed in this paper.Space distribution of intrapluse feature about radar signals was analyzed with this method,while intrapulse feature aggregation was evaluated by fuzziness and intrapulse feature discreteness was evaluated by close-degree.And for the overlapping states of feature space distribution,a linear separable measure of intrapluse feature about radar signals was put forward by within-class distance,between-class distance and linear discriminated criterion.The simulation results,based on the experiments of two intrapulse features extracted via time-frequency atom approach about five kinds of radar signal,show that the method and measure proposed in the paper are effective and feasible.It provides a new idea and approach for quantitatively evaluating features of the radar emitter signal.

Intrapulse Feature Evaluation of Radar Signal based on Complexity of Space

Intrapulse Feature Evaluation of Radar Signal based on Complexity of Space