The Evaluation of Probe Signals for Impulse Response Measurements in Shallow Water Environment

Santoso, Tri Budi; Widjiati, Endang; Wirawan, Wirawan; Hendrantoro, Gamantyo

doi:10.1109/tim.2016.2519768

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…Specifically, this method can be used to predict the impulse response in various environments, and the evaluated pulse response can be used to perform VTRM processing on the FSK received signal to optimize the multipath interference in the transmission channel. The evaluation of the ESS impulse response is conducted following the methodology outlined by Santoso et al [16], which involves performing convolution operations on the received signal of the ESS transmitted through the channel and the time-reversed signal of the ESS transmission signal. First, the ESS-evaluated impulse response and actual impulse response are converted into a power delay profile (PDP) to determine the presence of any difference in the delay and signal strength of the actual and evaluated impulse responses.…”

Section: Preprocessing Of Vtrmmentioning

confidence: 99%

“…The top and bottom graphs exhibit the instantaneous frequency and ESS signal, respectively, at different time points. The actual pulse response can be obtained when the exponential sweeping frequency is convolved with the time-reversed transmitted signal after obtaining the received signal through the transmission channel[16]. Fig.4shows the steps to obtaining the pulse response using the ESS frequency.…”

mentioning

confidence: 99%

“…Therefore, this study performs VTRM processing on the received signals by using the evaluated pulse response to restore the pulse signal. Additionally, Santoso et al[16] noted that it is normal for an evaluated impulse response to differ slightly from the actual impulse response in terms of magnitude, where either can be greater than the other.…”

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confidence: 99%

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Design of Deep Learning Acoustic Sonar Receiver with Temporal/ Spatial Underwater Channel Feature Extraction Capability

Chih-Ta Yen,

Un-Hung Chen

2024

Int. j. eng. technol. innov.

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In this study, deep learning network technology is employed to solve the problem of rapid changes in underwater channels. The modulation techniques employed are frequency-shift keying (FSK) and the BELLHOP module of MATLAB; they are used to create water with multipath, Doppler shifts, and additive Gaussian white noise such that underwater acoustic receiving signals simulating the actual ocean environment can be obtained. The southwest coastal area of Taiwan is simulated in the manuscript. The results reveal that optimizing the environment by using the virtual time reversal mirror (VTRM) technique can generally mitigate the bit error rates (BERs) of the deep learning network’s model receiver and traditional demodulation receiver. Lastly, seven deep learning networks are deployed to demodulate the FSK signals, and these approaches are compared with traditional demodulation techniques to determine the deep learning network techniques that are most suitable for marine environments.

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Section: Preprocessing Of Vtrmmentioning

confidence: 99%

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confidence: 99%

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Design of Deep Learning Acoustic Sonar Receiver with Temporal/ Spatial Underwater Channel Feature Extraction Capability

Chih-Ta Yen,

Un-Hung Chen

2024

Int. j. eng. technol. innov.

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Linear frequency modulation signal for channel impulse response measurement in shallow water environment

Widiarti¹,

Rahmat²,

Adianto³

et al. 2021

IOP Conf. Ser.: Mater. Sci. Eng.

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Understanding the characteristics of channel propagation plays a very important role in the design of underwater acoustic communication. Underwater acoustic channel (UAC) model is usually carried out by measuring directly at a specific location and time. This paper presents the use of Linear Frequency Modulation (LFM) signal for channel impulse response estimation and power delay profile (PDP) measurement in a shallow water environment. Measurements were made in a laboratory-scale towing tank with three variations of distance and depth. Furthermore, the measurement results are compared with the existing underwater acoustic channel model. The measurement results show that the response impulses at the three distances have almost the same pattern and the resulting amplitude is similar to the existing underwater acoustic channel model simulation. The RMS delay spread generated from the three distances has a value of about 0,2 msec. Meanwhile, the maximum excess delay obtained at a distance of 120 m has the highest value, which is 29 msec.

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OFDM system with adaptive modulation for shallow water acoustic channel environment

Huda¹,

Putri²,

Santoso³

2017

2017 IEEE International Conference on Communication, Networks and Satellite (Comnetsat)

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The Evaluation of Probe Signals for Impulse Response Measurements in Shallow Water Environment

Cited by 11 publications

References 25 publications

Design of Deep Learning Acoustic Sonar Receiver with Temporal/ Spatial Underwater Channel Feature Extraction Capability

Design of Deep Learning Acoustic Sonar Receiver with Temporal/ Spatial Underwater Channel Feature Extraction Capability

Linear frequency modulation signal for channel impulse response measurement in shallow water environment

OFDM system with adaptive modulation for shallow water acoustic channel environment

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