Analytic performance investigation of signal level estimator based on empirical characteristic function in impulsive noise

Babarsad, Sina Bakhshandeh; Saberali, S. Mohammad; Majidi, Mahdi

doi:10.1016/j.dsp.2019.04.009

Cited by 9 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Secondly, the sensing performance of intermittent chaos sensing system based on dualcoupled Duffing oscillator under the background of Lévy noise is studied and analyzed. Through the experimental analysis in the previous section, it is found that when (27 17 The intermittent chaotic single Duffing vibrator sensing system �ω ω ≤ 0.08 , the intermittent chaos sensing system of single Duffing oscillator can accurately show the phenomenon of intermittent chaos. Select appropriate parameters to enable the system to accurately show intermittent chaos.…”

Section: Dual-coupled Duffing Oscillatormentioning

confidence: 99%

A practical underwater information sensing system based on intermittent chaos under the background of Lévy noise

Zhang

Qin

Zhang

et al. 2022

J Wireless Com Network

View full text Add to dashboard Cite

Nowadays, the increasingly complex and changeable marine environment makes the signals received by the underwater sensing equipment not only contain the weak signals radiated by underwater targets but also accompanied by marine solid background noise, which leads to the degradation and distortion of underwater acoustic signals and the decline of underwater communication quality. Under the severe influence of ocean noise, the underwater acoustic sensing and acquisition system will have the problems of high SNR ratio threshold, minimal sensing bandwidth, and unable to sense the signal with unknown frequency effectively. The Lévy noise model has been selected to describe the marine noise environment and explain its scientificity in this paper. A parameter estimation method for Lévy noise is proposed. Under the condition of characteristic index $$\alpha =1.5$$ α = 1.5 and noise intensity $$D=0.1$$ D = 0.1 of the Lévy noise model, the estimated mean values of parameters are 1.5026 and 1.1664. The estimated variances are 0.0034 and 0.0046, which proves the effectiveness and applicability of the estimation method. Then, an improved dual-coupled Duffing oscillator sensing system is proposed to sense the weak signals with unknown frequency under Lévy noise. Under the background of Lévy with characteristic index $$\alpha =1.5$$ α = 1.5 , deflection parameter $$\beta =0$$ β = 0 and noise intensity $$D=0.1$$ D = 0.1 , the sensing error rate of our system with unknown frequency is $$0.054\%$$ 0.054 % , the lowest sensing signal amplitude is $$A=0.010$$ A = 0.010 , the lowest sensing SNR ratio is − 23.9254 dB, and the frequency of multi-frequency weak signals to be measured can be obtained. The estimation error of frequency sensing is $$0.33\%$$ 0.33 % .

show abstract

Section: Dual-coupled Duffing Oscillatormentioning

confidence: 99%

A practical underwater information sensing system based on intermittent chaos under the background of Lévy noise

Zhang

Qin

Zhang

et al. 2022

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…A few of the encountered problems include [16]: (1) limitation in the distance of propagation for sound waves due to attenuation from the absorption of the UWA waves; (2) reduction in the speed of propagation of sound waves, i.e., approximately 1500 m/s; (3) interference and echoes generated due to multipath reflection from the top and bottom of the sea surface; (4) the transmitter and receiver motion inducing heterogeneous characteristics to the underwater transmitted signal as well as the Doppler's effect; (5) the ocean noises. Moreover, the impulsive noise adversely affects the noise due to the presence of Gaussian noise and the performance of the signal processors degrades [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems

Junejo

Sattar

Adnan

et al. 2023

JMSE

View full text Add to dashboard Cite

In the past decades, researchers/scientists have paid attention to the physical layer of underwater communications (UWCs) due to a variety of scientific, military, and civil tasks completed beneath water. This includes numerous activities critical for communication, such as survey and monitoring of oceans, rescue, and response to disasters under the sea. Till the end of the last decade, many review articles addressing the history and survey of UWC have been published which were mostly focused on underwater sensor networks (UWSN), routing protocols, and underwater optical communication (UWOC). This paper provides an overview of underwater acoustic (UWA) physical layer techniques including cyclic prefix orthogonal frequency division multiplexing (CP-OFDM), zero padding orthogonal frequency division multiplexing (ZP-OFDM), time-domain synchronization orthogonal frequency division multiplexing (TDS-OFDM), multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM), generalized frequency division multiplexing (GFDM), unfiltered orthogonal frequency division multiplexing (UF-OFDM), continuous phase modulation orthogonal frequency division multiplexing (CPM-OFDM), filter bank multicarrier (FBMC) modulation, MIMO, spatial modulation technologies (SMTs), and orthogonal frequency division multiplexing index modulation (OFDM-IM). Additionally, this paper provides a comprehensive review of UWA channel modeling problems and challenges, such as transmission loss, propagation delay, signal-to-noise ratio (SNR) and distance, multipath effect, ambient noise effect, delay spread, Doppler effect modeling, Doppler shift estimation. Further, modern technologies of the physical layer of UWC have been discussed. This study also discusses the different modulation technology in terms of spectral efficiency, computational complexity, date rate, bit error rate (BER), and energy efficiency along with their merits and demerits.

show abstract

“…The major advantage of Gaussian processes is that the characteristic of a Gaussian signal can be completely described by using the second‐order statistics. Due to the man‐made interference, marine life noise, strong winds, heavy rain, and rainstorms, the ocean ambient noise in shallow water shows the impulsive nature [5–7], which is described by the heavy tail rather than the exponential tail. In other words, the performance of signal processors designed in the presence of Gaussian noise would be degraded when the noise is seriously affected by the impulsive noise [8–11].…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation of underwater impulsive noise with the Class B model

Zhang

Ying²,

Yang

et al. 2020

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

Analytic performance investigation of signal level estimator based on empirical characteristic function in impulsive noise

Cited by 9 publications

References 26 publications

A practical underwater information sensing system based on intermittent chaos under the background of Lévy noise

A practical underwater information sensing system based on intermittent chaos under the background of Lévy noise

A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems

Parameter estimation of underwater impulsive noise with the Class B model

Contact Info

Product

Resources

About