Classification of low probability of interception communication signal modulations based on time-frequency analysis and artificial neural network

Zhang, Gangqiang; Dong, Yangze; Liu, Pingxiang

doi:10.1109/icecc.2011.6066722

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…This detector may fail when the ambient noise consists of correlated components (e.g., background tonal shipping noise) or when the noise power fluctuates. Feature extraction is also offered in [69], where the communication modulation type is determined from analyzing time-frequency images.…”

Section: ) Cyclostationary-feature Detectormentioning

confidence: 99%

Low Probability of Detection for Underwater Acoustic Communication: A Review

Diamant

Lampe

2018

IEEE Access

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Low probability of detection (LPD) is an extremely important characteristic of an underwater acoustic communication (UWAC) system when used for military-related applications, since the detection of a communication signal in the channel may reveal the presence of the transmitter or receiver. Furthermore, the recent advances in the understanding of the environmental effects of sound transmission in the ocean have led to a growing interest in LPD for UWAC also for civilian use. This is because systems that are designed for reliable communication at low signal power have a reduced environmental impact. In this paper, we identify the main challenges for the design of UWAC LPD systems. We describe and classify common approaches for transmission, reception, and interception of LPD signals, and we discuss their advantages and weaknesses. We also present several methods to determine the LPD capability of a system and suggest to adopt the range ratio test as a performance measure that captures the effects of signal propagation through the UWAC channel and the capabilities of the communication receiver and a signal interceptor. In light of the environmental benefits of LPD transmission and ongoing discussions about limiting the power spectral density of UWAC signals through regulations, we believe that LPD transmission is an area of growing importance for UWAC research and development. We hope that this paper serves as a motivation and a starting point for further research in this field.

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Section: ) Cyclostationary-feature Detectormentioning

confidence: 99%

Low Probability of Detection for Underwater Acoustic Communication: A Review

Diamant

Lampe

2018

IEEE Access

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“…For example, multiband energy detectors can detect signals spread in frequency by some pseudo-random sequence or via frequency hopping [24], but would not suffice for differentiating between biological signals and artificial ones. Other interception approaches that use feature analysis to, e.g., discover fluctuations in time [25] or detect a signal's pattern [26] may also fail because the signal is made to be very similar to real bio-vocalization. Further, since both the biomimicked signal and the real biological signal stem from a single source, interception by tracking or localization to separate a single source from an isometric noise will not assist in the signal's discrimination.…”

Section: Introductionmentioning

confidence: 99%

Detection of Dolphin Whistle-Like Biomimicking Signals by Phase Analysis

Davidesco

Diamant

2022

IEEE Access

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The application of low probability of detection (LPD) in underwater acoustic communication is challenging due to the limited bandwidth and frequency band available that allows simple interception using energy detection. Confronting that, recent LPD schemes offer to disguise the communication signal as a vocalization of a marine mammal. This way, the signals can be transmitted at high power, while the interceptor believes these are biological sounds. In this paper, we propose a first interceptor tailored to distinguish between anthropogenic and biological sounds. Our main assumption is that, due to limitations on the dump-off factor of the acoustic projector, the phase of a real whistle would be much more diverse than that of a disguised whistle-like signal. We therefore propose as a classification measure the randomness of the signal's phase. The phase is calculated by a phase-locked-loop, while the phase's randomness is measured by entropy. Our results show that the approximate entropy, which uncovers regularities in a signal, is a good classification metric. Analysis of data obtained from two sea experiments and from a large database of tagged dolphins' whistles, shows that our interception scheme can well distinguish between real and biomimicked signals. INDEX TERMSLow probability of detection; Underwater acoustic communications; biomimicking; phase locked loop; entropy analysis VOLUME 4, 2016

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“…Blind identification is a difficult task and recognition process is even more challenging especially in low SNR because the blind interceptor have a limited facilities in receiving a signal compared to the ground station. Many efforts have been made, and several classifiers have been proposed in literature, some use neural network methods which need high computational work [1], [2] ,or time-frequency representations [3], [4] ,or using Maximum likelihood approach that may be achieving better results but requiring more complexity [15] ,or by using higher order cumulants [9], [12]- [14] . Most of the proposed methods in literature deals with modulation types which are not used in satellite signals like FSK and ASK.…”

Section: Introductionmentioning

confidence: 99%

C3. Automatic digital modulation recognition of satellite communication signals

Shakra¹,

Shaheen²,

Bakr³

et al. 2015

2015 32nd National Radio Science Conference (NRSC)

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Mohamed S. Abdel-Latif EW Department, M. T. C. mnna3000@yahoo.com Automatic modulation recognition is very important for communication intelligence applications such as signal interception for satellite communications, civil authority, surveillance and threat analysis. Automatic digital modulation recognition is a system that automatically classifies the modulation type of the intercepted signal. This paper presents a method for the automatic recognition of digital modulations without a priori knowledge of the signal parameters. This algorithm works at low signal to noise ratio and get nearly to 100 % of correct classification percentage at 0 dB which is effective in Electronic warfare and communication surveillance applications. This method can recognize common single-carrier modulated satellite communication signals such as M-ary phase-shift keying, M-ary quadrature amplitude modulation and M-ary amplitude phase shift keying which used in DVB-S2. This algorithm is performed by a sequential steps to blindly classify the signal using PSD of instantaneous amplitude, kurtosis and HOS (cumulants).

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Classification of low probability of interception communication signal modulations based on time-frequency analysis and artificial neural network

Cited by 6 publications

References 4 publications

Low Probability of Detection for Underwater Acoustic Communication: A Review

Low Probability of Detection for Underwater Acoustic Communication: A Review

Detection of Dolphin Whistle-Like Biomimicking Signals by Phase Analysis

C3. Automatic digital modulation recognition of satellite communication signals

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