Radar signal categorization using a neural network

Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications

Familiarity Discrimination of Radar Pulses

The ARTMAP-FD neural network performs both identification (placing test patterns in classes encountered during training) and familiarity discrimination (judging whether a test pattern belongs to any of the classes encountered during training). The performance of ARTMAP-FD is tested on radar pulse data obtained in the field, and compared to that of the nearest-neighbor-based NEN algorithm and to a k > 1 extension of NEN

Specific Emitter Identification Based on Fractal Features

If we take into consideration the fact that the radar signal recognition and identification process is an integral part of contemporary combat operations, the importance of the fractal analysis increases significantly. For this reason, the fractal analysis is used in the process of radar sources identification on the contemporary battlefield. Radar Signal Recognition (RSR) with the use of classical methods, that is based on statistical analysis of basic measurable parameters of a radar signal, such as Radio Frequency (RF), Amplitude (A), Pulse Width (PW) or Pulse Repetition Interval (PRI) is not enough to carry out the distinction process of particular copies of the same radar type. Only by this approach, the identification process of particular copies in a set of the same type emitters can be carried out. As a result, it is possible to maximize Correct Identification Coefficient (CIC) in the final stage of the recognition process, which is realized in Electronic Warfare (EW) systems. One of the most important elements of the whole recognition and identification process, which is realized in ELectronic INTelligence (ELINT) battlefield system, is building a measurement data vector, then a radar\u27s metrics and the same database. This approach is called Specific Emitter Identification (SEI)

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

A fundamental work on THz measurement techniques for application to steel manufacturing processes

The terahertz (THz) waves had not been obtained except by a huge system, such as a free electron laser, until an invention of a photo-mixing technique at Bell laboratory in 1984 [1]. The first method using the Auston switch could generate up to 1 THz [2]. After then, as a result of some efforts for extending the frequency limit, a combination of antennas for the generation and the detection reached several THz [3, 4]. This technique has developed, so far, with taking a form of filling up the so-called THz gap . At the same time, a lot of researches have been trying to increase the output power as well [5-7]. In the 1990s, a big advantage in the frequency band was brought by non-linear optical methods [8-11]. The technique led to drastically expand the frequency region and recently to realize a measurement up to 41 THz [12]. On the other hand, some efforts have yielded new generation and detection methods from other approaches, a CW-THz as well as the pulse generation [13-19]. Especially, a THz luminescence and a laser, originated in a research on the Bloch oscillator, are recently generated from a quantum cascade structure, even at an only low temperature of 60 K [20-22]. This research attracts a lot of attention, because it would be a breakthrough for the THz technique to become widespread into industrial area as well as research, in a point of low costs and easier operations. It is naturally thought that a technology of short pulse lasers has helped the THz field to be developed. As a background of an appearance of a stable Ti:sapphire laser and a high power chirped pulse amplification (CPA) laser, instead of a dye laser, a lot of concentration on the techniques of a pulse compression and amplification have been done. [23] Viewed from an application side, the THz technique has come into the limelight as a promising measurement method. A discovery of absorption peaks of a protein and a DNA in the THz region is promoting to put the technique into practice in the field of medicine and pharmaceutical science from several years ago [24-27]. It is also known that some absorption of light polar-molecules exist in the region, therefore, some ideas of gas and water content monitoring in the chemical and the food industries are proposed [28-32]. Furthermore, a lot of reports, such as measurements of carrier distribution in semiconductors, refractive index of a thin film and an object shape as radar, indicate that this technique would have a wide range of application [33-37]. I believe that it is worth challenging to apply it into the steel-making industry, due to its unique advantages. The THz wavelength of 30-300 ¼m can cope with both independence of a surface roughness of steel products and a detection with a sub-millimeter precision, for a remote surface inspection. There is also a possibility that it can measure thickness or dielectric constants of relatively high conductive materials, because of a high permeability against non-polar dielectric materials, short pulse detection and with a high signal-to-noise ratio of 103-5. Furthermore, there is a possibility that it could be applicable to a measurement at high temperature, for less influence by a thermal radiation, compared with the visible and infrared light. These ideas have motivated me to start this THz work

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

Electronic Warfare:Issues and Challenges for Emitter Classification

Electronic warfare (EW) is an important capability that provides advantage to defence forces over their adversaries. Defence forces gather tactical intelligence through EW sensors, which provide the means to counter hostile actions of enemy forces. Functions of an EW system is threat detection and the area surveillance so as to determine the identity of surrounding emitters. Emitter classification system identifies possible threats by analysing intercepted signals. Problem of identifying emitters based on its intercepted signal characteristics is a challenging problem in electronic warfare studies. Major issues and challenges for emitter classification such as drifting of emitter parameters due to aging, operational characteristic of an emitter, i.e., same emitter can operate on multiple bands and multiple pulse repetition frequencies (PRFs) are highlighted. A novel approach based on some well-known statistical methods, e.g., regression analysis, hypothesis testing, and discriminent analysis is proposed. The effectiveness of the proposed approach has been tested over ELINT (Electronic Intelligence) data and illustrated using simulation data. The proposed approach can play a solution for wide variety of problems in emitter classification in electronic warfare studies.Defence Science Journal, 2011, 61(3), pp.228-234, DOI:http://dx.doi.org/10.14429/dsj.61.52

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)