A novel radar signal recognition method based on a deep restricted Boltzmann machine

Radar signal recognition is of great importance in the field of electronic intelligence reconnaissance. To deal with the problem of parameter complexity and agility of multi-function radars in radar signal recognition, a new model called radar signal recognition based on the deep restricted Boltzmann machine (RSRDRBM) is proposed to extract the feature parameters and recognize the radar emitter. This model is composed of multiple restricted Boltzmann machines. A bottom-up hierarchical unsupervised learning is used to obtain the initial parameters, and then the traditional back propagation (BP) algorithm is conducted to fine-tune the network parameters. Softmax algorithm is used to classify the results at last. Simulation and comparison experiments show that the proposed method has the ability of extracting the parameter features and recognizing the radar emitters, and it is characterized with strong robustness as well as highly correct recognition rate

Deep Learning Techniques in Radar Emitter Identification

In the field of electronic warfare (EW), one of the crucial roles of electronic intelligence is the identification of radar signals. In an operational environment, it is very essential to identify radar emitters whether friend or foe so that appropriate radar countermeasures can be taken against them. With the electromagnetic environment becoming increasingly complex and the diversity of signal features, radar emitter identification with high recognition accuracy has become a significantly challenging task. Traditional radar identification methods have shown some limitations in this complex electromagnetic scenario. Several radar classification and identification methods based on artificial neural networks have emerged with the emergence of artificial neural networks, notably deep learning approaches. Machine learning and deep learning algorithms are now frequently utilized to extract various types of information from radar signals more accurately and robustly. This paper illustrates the use of Deep Neural Networks (DNN) in radar applications for emitter classification and identification. Since deep learning approaches are capable of accurately classifying complicated patterns in radar signals, they have demonstrated significant promise for identifying radar emitters. By offering a thorough literature analysis of deep learning-based methodologies, the study intends to assist researchers and practitioners in better understanding the application of deep learning techniques to challenges related to the classification and identification of radar emitters. The study demonstrates that DNN can be used successfully in applications for radar classification and identification.   &nbsp

Classifiers accuracy improvement based on missing data imputation

In this paper we investigate further and extend our previous work on radar signal identification and classification based on a data set which comprises continuous, discrete and categorical data that represent radar pulse train characteristics such as signal frequencies, pulse repetition, type of modulation, intervals, scan period, scanning type, etc. As the most of the real world datasets, it also contains high percentage of missing values and to deal with this problem we investigate three imputation techniques: Multiple Imputation (MI); K-Nearest Neighbour Imputation (KNNI); and Bagged Tree Imputation (BTI). We apply these methods to data samples with up to 60% missingness, this way doubling the number of instances with complete values in the resulting dataset. The imputation models performance is assessed with Wilcoxon’s test for statistical significance and Cohen’s effect size metrics. To solve the classification task, we employ three intelligent approaches: Neural Networks (NN); Support Vector Machines (SVM); and Random Forests (RF). Subsequently, we critically analyse which imputation method influences most the classifiers’ performance, using a multiclass classification accuracy metric, based on the area under the ROC curves. We consider two superclasses (‘military’ and ‘civil’), each containing several ‘subclasses’, and introduce and propose two new metrics: inner class accuracy (IA); and outer class accuracy (OA), in addition to the overall classification accuracy (OCA) metric. We conclude that they can be used as complementary to the OCA when choosing the best classifier for the problem at hand

Automatic recognition of radar signals based on time-frequency image shape character

Radar signal recognition is one of the key technologies of modern electronic surveillance systems. Time-frequency image provides a new way for recognizing the radar signal. In this paper, a series of image processing methods containing image enhancement, image threshold binarization and mathematical morphology is utilized to extract the shape character of smoothed pseudo wigner-ville time-frequency distribution of radar signal. And then the identification of radar signal is realized by the character. Simulation results of eight kinds of typical radar signal demonstrate that when signal noise ratio (SNR) is greater than -3 dB, the Legendre moments shape character of the time-frequency image is very stable. Moreover, the recognition rate by the character is more than 90 per cent except for the FRANK code signal when SNR > -3 dB. Test also show that the proposed method can effectively recognize radar signal with less character dimension through compared with exitsing algorithms.Defence Science Journal, 2013, 63(3), pp.308-314, DOI:http://dx.doi.org/10.14429/dsj.63.240

De-interleaving of Radar Pulses for EW Receivers with an ELINT Application

De-interleaving is a critical function in Electronic Warfare (EW) that has not received much attention in the literature regarding on-line Electronic Intelligence (ELINT) application. In ELINT, on-line analysis is important in order to allow for efficient data collection and for support of operational decisions. This dissertation proposed a de-interleaving solution for use with ELINT/Electronic-Support-Measures (ESM) receivers for purposes of ELINT with on-line application. The proposed solution does not require complex integration with existing EW systems or modifications to their sub-systems. Before proposing the solution, on-line de-interleaving algorithms were surveyed. Density-based spatial clustering of applications with noise (DBSCAN) is a clustering algorithm that has not been used before in de-interleaving; in this dissertation, it has proved to be effective. DBSCAN was thus selected as a component of the proposed de-interleaving solution due to its advantages over other surveyed algorithms. The proposed solution relies primarily on the parameters of Angle of Arrival (AOA), Radio Frequency (RF), and Time of Arrival (TOA). The time parameter was utilized in resolving RF agility. The solution is a system that is composed of different building blocks. The solution handles complex radar environments that include agility in RF, Pulse Width (PW), and Pulse Repetition Interval (PRI)

MULTISTATIC RADAR EMITTER IDENTIFICATION USING ENTROPY MAXIMIZATION BASED INDEPENDENT COMPONENT ANALYSIS

Radar emitter identification is state-of-the-art in modern electronic warfare. Presently multistatic architecture is adapted by almost all the radar systems for better tracking performance and accuracy in target detection. Hence, identification and classification of radar emitters operating in the surveillance region are the major problems. To deal with the difficulty of identification of radar emitters in a complex electromagnetic environment, in this work entropy maximization method of Independent Component Analysis (ICA) based on gradient ascent algorithm is proposed. This algorithm separates unknown source signals from the interleaved multi-component radar signals. The discrete source signals are extracted from the multi-component signal by optimizing the entropy where maximum entropy is achieved using a gradient ascent approach through unsupervised learning. As better detection capability and range resolution are achieved by Linear Frequency Modulated (LFM) signals for radar systems here, multicomponent LFM signals with low SNR are considered as the signal mixture from which, the independent sources separated. A mathematical model of the algorithm for entropy maximization is illustrated in this paper. Simulation result validates the effectiveness of the algorithm in terms of time domain separation of the signal, and time-frequency analysi