Real-time low-complexity automatic modulation classifier for pulsed radar signals

Iglesias, Victor; Grajal, Jesús; Royer, Pablo; Sánchez, María del Carmen; López‐Vallejo, Marisa; Yeste-Ojeda, Omar A.

doi:10.1109/taes.2014.130183

Cited by 43 publications

(26 citation statements)

References 27 publications

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“…Since the amplitude spectrum obtained from DFT will be scaled by the factor N , it is necessary to multiply the result with 1/ N to eliminate the coefficient N in equation (11). Then, the amplitude correction factor of the Hamming window…”

Section: Bionic Sonar Signal Modelmentioning

confidence: 99%

“…In the last few decades, many methods have been proposed to improve the stealth capability of active sonar systems through signal waveform design [1][2][3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…For example, for frequency-hopping signals, each pulse could be continuous-wave (CW) or linear frequency modulated (LFM); however, the CW pulses have the feature of being of rectangle in the time domain and single frequency in the frequency domain, while LFM pulses are of both rectangle in the time domain and linearly changed frequency in the frequency domain [6]. As a result, these signals can be identified and classified easily in practice [11][12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Disguised Bionic Sonar Signal Waveform Design with Its Possible Camouflage Application Strategy for Underwater Sensor Platforms

et al. 2018

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Section: Bionic Sonar Signal Modelmentioning

confidence: 99%

“…In the last few decades, many methods have been proposed to improve the stealth capability of active sonar systems through signal waveform design [1][2][3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Disguised Bionic Sonar Signal Waveform Design with Its Possible Camouflage Application Strategy for Underwater Sensor Platforms

et al. 2018

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“…By sending out signals for target detection, an active sonar system [ 1 , 2 , 3 , 4 , 5 ] unavoidably risks being detected and identified by the others too. In the last few decades, many methods have been proposed to improve the covert capability of active sonar systems through signal waveform design [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] from the perspectives of low probability of interception (LPI) or low probability of detection (LPD).…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Covert Active Sonar Strategy

Jiang

Wang

Duan

et al. 2018

Sensors

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The covertness of the active sonar is a very important issue and the sonar signal waveform design problem was studied to improve covertness of the system. Many marine mammals produce call pulses for communication and echolocation, and existing interception systems normally classify these biological signals as ocean noise and filter them out. Based on this, a bio-inspired covert active sonar strategy was proposed. The true, rather than man-made sperm whale, call pulses were used to serve as sonar waveforms so as to ensure the camouflage ability of sonar waveforms. A range and velocity measurement combination (RVMC) was designed by using two true sperm whale call pulses which had excellent range resolution (RR) and large Doppler tolerance (DT). The range and velocity estimation methods were developed based on the RVMC. In the sonar receiver, the correlation technology was used to confirm the start and end time of sonar signals and their echoes, and then based on the developed range and velocity estimation method, the range and velocity of the underwater target were obtained. Then, the RVMC was embedded into the true sperm whale call-train to improve the camouflage ability of the sonar signal-train. Finally, experiment results were provided to verify the performance of the proposed method.

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“…Another slightly dierent HDT classier is presented in [26]. By using the unwrapped phase, they implement the solution real-time on a Field Programmable Logic Gate (FPGA).…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Radar Modulation Classification

Fahlman¹

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Automatic modulation classication is concerned with identifying the modulation present on a radio wave. This can be any type of radar or communication signal. It is employed in elds such as cognitive radio for communications, radar analysis for electronic warfare. This thesis is dedicated to classifying a variety of modulations used in modern radar. These include unmodulated, various types of frequency modulation, and phase shift keyed waveforms. This task is accomplished through feature extraction and machine learning techniques. The objective is to determine a suitable method applicable for real-time implementation in a complex electronic warfare environment. Three techniques are proposed: a decision tree combined with Multilayer Perceptron Neural Network, a Multilayer Perceptron Neural Network, and a Convolutional Neural Network. The simulation results show that the decision tree achieves a low classication performance, the Multilayer Perceptron achieves good results in a controlled environment, while the Convolutional Neural Network achieves good generalizable results. The eects of noise, pulse width, and frequency changes are discussed. Each systems latency is also examined.

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Real-time low-complexity automatic modulation classifier for pulsed radar signals

Cited by 43 publications

References 27 publications

Disguised Bionic Sonar Signal Waveform Design with Its Possible Camouflage Application Strategy for Underwater Sensor Platforms

Disguised Bionic Sonar Signal Waveform Design with Its Possible Camouflage Application Strategy for Underwater Sensor Platforms

Bio-Inspired Covert Active Sonar Strategy

Automatic Radar Modulation Classification

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