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

Simulation of Cognitive Electronic Warfare System With Sine and Square Waves

Today’s Electronic Warfare (EW) receivers need advanced technology to achieve real-time surveillance operations. Dynamic and intelligent systems are required for UAVs and other airborne applications. The airborne Electronic Warfare systems must be knowledge-based systems, learning from the threat scenario with highly integrated capabilities to detect, react, and adapt to radar threats in real-time. Artificial intelligence is a machine-dependent process, by adapting certain rules and logic supported by human intelligence, AI can be used for cognitive processing. Cognitive signal processing is required for making the system autonomous and dynamic in nature. Military action on radar signatures requires a set of commands to be executed dynamically with the help of the proposed EW system. It is proposed to design and develop a cognitive EW architecture and simulation of machine learning that combines neural network architecture with the help of sine and square waves as input. This paper presents the Cognitive signal processing for EW systems with Neural Network, Recurrent Neural Network (RNN), Machine learning (ML), and Deep learning (DL) techniques with their simulation with sine and square waves

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

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