Simulation of Radar Modulation Estimation Using Curvelet Transform for EW Applications.

State-of-the-art distributed system development with low power specifications is especially required for different applications. Ultra-wideband radar (UWB), Multi Input Multi Output radar (MIMO), and cognitive-based radar technology are the current trends in modern radar systems. Current EW systems are developed with digital receiver technology using Nyquist Sampling. Radar modulation recognition and correct radar identification are essential requirements for EW receiver systems. The radar signal acquisition through compressed sensing and reducing the number of samples using sub-Nyquist sampling play a vital role in EW systems. Random Modulator preintegrator (RMPI) is a current concept for extracting waveforms with dominant base vectors. RMPI explores the structural and geometrical properties of the signal apart from traditional time and frequency domain analysis for improved identification. The Curvelet transform-based RMPI methodology is an inverse problem with exceptionally fewer measurements. In this paper, Curveletbased modulation estimation, identification of radars, and Curvelet-simulated results of different radar waveforms with noise are depicted. The discrete Curvelet transform algorithm (USFFT) is simulated with an input signal of Ultra-Wide Band (UWB) pulse, pulse spikes, Linear Frequency Modulated (LFM), and Barker-coded pulse waveforms. The FFT output is also measured with Nyquist samples for comparison. This paper has contributed the simulation of Electronic Warfare techniques with Modulation Estimations as part of ESM (Electronic Support Measure), the Compressed sensing and Curvelet-based modulation estimation, identification of radars simulated with different radar waveforms with/without noise, and the discrete Curvelet transform algorithm is simulated with input signal of UWB pulse, pulse spikes, LFM, and Barker coded. [ABSTRACT FROM AUTHOR]