Time Frequency Analysis of OFDM-LFM Waveforms for Multistatic Airborne Radar

Dash, Dillip; Dhar, Kodanda; Jayaraman, Valarmathi

doi:10.1109/icicct.2018.8473233

Cited by 3 publications

(2 citation statements)

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“…To an NLFM group of signals, authors also assigned quadratic frequency modulation (QFM), sinusoidal frequency modulation (SiFM), etc. There are also works [ 15 ] considering the application of OFDM signals for radar purposes. In [ 2 ], there is an algorithm for identifying so-called exotic modulations, which include signals modeled as a combination of LFM and Biphase modulation (BPM).…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Typically, the ability to recognize common modulation types such as Phase Shift Keying (PSK), Frequency Shift Keying (FSK), Amplitude Shift Keying (ASK) or Quadrature Amplitude Modulation (QAM) is considered [ 13 , 14 ]. There are also studies taking into account the possibility of detection of OFDM transmission, among other communication signals [ 15 , 16 ]. In the case of electronic intelligence (ELINT), the main task of the system is to detect, classify, and determine the location of the emission sources other than communication.…”

Section: Introductionmentioning

confidence: 99%

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An Application of Analytic Wavelet Transform and Convolutional Neural Network for Radar Intrapulse Modulation Recognition

Walenczykowska

Kawalec

Krenc

2023

Sensors

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This article analyses the possibility of using the Analytic Wavelet Transform (AWT) and the Convolutional Neural Network (CNN) for the purpose of recognizing the intrapulse modulation of radar signals. Firstly, the possibilities of using AWT by the algorithms of automatic signal recognition are discussed. Then, the research focuses on the influence of the parameters of the generalized Morse wavelet on the classification accuracy. The paper’s novelty is also related to the use of the generalized Morse wavelet (GMW) as a superfamily of analytical wavelets with a Convolutional Neural Network (CNN) as classifier applied for intrapulse recognition purposes. GWT is used to obtain time–frequency images (TFI), and SqueezeNet was chosen as the CNN classifier. The article takes into account selected types of intrapulse modulation, namely linear frequency modulation (LFM) and the following types of phase-coded waveform (PCW): Frank, Barker, P1, P2, and Px. The authors also consider the possibility of using other time–frequency transformations such as Short-Time Fourier Transform(STFT) or Wigner–Ville Distribution (WVD). Finally, authors present the results of the simulation tests carried out in the Matlab environment, taking into account the signal-to-noise ratio (SNR) in the range from −6 to 0 dB.

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Section: Materials and Methodsmentioning

confidence: 99%