Time delay estimation using wavelet denoising maximum likelihood method for underwater reverberant environment

B, Marxim Rahula Bharathi; Mohanty, Amiya Ranjan

doi:10.1049/iet-rsn.2020.0079

Cited by 10 publications

(5 citation statements)

References 48 publications

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“…To improve the robustness of GCC in the case of low signal-to-noise ratio (SNR) situations, Jing et al [14] suppressed the interference of noises with quadratic correlation before SCOT weighting. With the underwater reverberant environment, Boopathi Rajan et al [15] enhanced the signal with the wavelet transform in the preprocessing stage, and estimated the time delay in a maximum likelihood estimator. Yang et al [16] transformed the spectral correlation function into frequency estimation, and searched the time delay by dichotomy.…”

Section: Time Delay Estimationmentioning

confidence: 99%

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Geometry estimation for multiple autonomous underwater vehicles using active sound source

Song

Liu

et al. 2023

IET Radar Sonar & Navi

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Underwater acoustic target detection with multiple autonomous underwater vehicles (AUVs) has the advantage of scalable aperture, however, it depends on the accurate estimation of the shape and structure of the swarm. In this study, a method based on an active sound source is proposed for geometry estimation of distributed nodes. Pulses or chirp signals, transmitted by the source, arrive AUV nodes with different time delays and are collected by the hydrophones, from which the formation geometry could be estimated. Firstly, the frequency-sliding generalised cross-correlation (FS-GCC) feature matrix between the signals received by each node and the reference node is extracted. Secondly, an autoencoder network is employed to enhance the time delay line implied in the FS-GCC matrix, from which a time delay between two nodes is obtained. A time delay vector could be constructed by concatenating the delays between the reference node and all other nodes when the source lies at different orientations. Lastly, the time delay matrix, which is composed of time delay vectors in a time window when AUV circularly moves around the detection nodes, is further decomposed by singular value decomposition to obtain the formation geometry. The feasibility and effectiveness are verified by the simulation dataset, anechoic pool experiment, and lake trial. The root mean square error of the proposed geometry estimation method drops by 9% and 54% for two typical geometries when compared with ref.[1] for the simulation dataset, which becomes 45% and 37% for the anechoic pool experiment. Though the estimation error increases in lake trials, the proposed method achieved a relatively better precision. It is found that the proposed method has better performance when the formation geometry of AUVs approaches a regular polygon and when the source works in a stop-and-go mode. Furthermore, when the source distance meets the far-field assumption, the azimuthal span of source movement relative to AUVs should be larger than 30°.

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Section: Time Delay Estimationmentioning

confidence: 99%

“…With the underwater reverberant environment, Boopathi Rajan et al. [15] enhanced the signal with the wavelet transform in the preprocessing stage, and estimated the time delay in a maximum likelihood estimator. Yang et al.…”

Section: Introductionmentioning

confidence: 99%

Geometry estimation for multiple autonomous underwater vehicles using active sound source

Song

Liu

et al. 2023

IET Radar Sonar & Navi

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“…For the interference suppression, Nahma et al [16] proposed a robust beamforming algorithm based on the room impulse response, which improved the algorithm's robustness in different reverberant environments. Rajan et al [17] used the wavelet denoising method for time delay estimation, which effectively reduced the influence of underwater reverberation on sound source localisation. Jiang et al [18] proposed a new algorithm combining deep fusion and Convolutional Neural Network in response to the problem of inaccurate sound source localisation in a reverberant environment.…”

Section: Introductionmentioning

confidence: 99%

An improved deconvolution beamforming algorithm for acoustic imaging of low signal-to-noise ratio sound sources in reverberant field

Guo¹,

Chen²,

Xia³

et al. 2023

J. vibroeng.

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Most of the existing acoustic imaging studies in reverberant field ignore the influence of signal-to-noise ratio. As a result, commonly used beamforming algorithms in reverberant backgrounds have poor imaging accuracy for low signal-to-noise ratio sound sources. In response to that problem, an improved adaptive beamforming algorithm called SC-DAMAS is put forward in this paper. The algorithm replaces the free-field Green's function with the impulse response function, making the algorithm more suitable for acoustic imaging of low signal-to-noise ratio in a reverberant environment. Besides, the comparative simulation results with the conventional beamforming method and orthogonal matching pursuit algorithm-based DAMAS, as well as sound source acoustic imaging experiments are carried out to analyze its effectiveness. It is indicated that, in the reverberation field, the SC-DAMAS has no obvious sidelobes and achieves higher positioning accuracy for acoustic imaging of low signal-to-noise ratio sound source than the abovementioned counterparts, and its imaging test result is consistent with the actual situation, which verifies the effectiveness of the algorithm.

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“…This problem is further exacerbated by a large number of relays that act as sensors in IoUTs. At the receiver, this increases the complexity of the time-domain equalization (TDE) as additional RAKE fingers are required to resolve these multipath components [22,24,25]. As a result, the TDE for IoUTs becomes unattractive as compared to the frequency-domain methods [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Low Complexity Single Carrier Frequency Domain Detectors for Internet of Underwater Things (IoUT)s

Aljanabi¹,

Alluhaibi²,

Ahmed³

et al. 2021

Preprint

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This paper proposes low complexity detection for internet of underwater things (IoUT)s communication. The signal is transmitted from the source to the destination using several sensors. To simplify the computational operations at the transmitter and the sensory nodes, a single carrier frequency domain equalizer (SC-FDE) is proposed and amplify-and-forward (AF) protocols are employed. Fast Fourier transform (FFT) and use of cyclic prefix (CP) are also proposed to simplify these algorithms when compared to time-domain equalization. As precise channel data is difficult to capture in underwater communications, the adaptive implementation of FDE is proposed as a solution that can be employed when the channel experiences a fast doppler shift. The two adaptive detectors are based on the least mean-square (LMS) and recursive least square (RLS) principles. Numerical simulations show that the performance of the bit error rate (BER) performance of the proposed detectors is close to that of the ideal minimum mean square error (MMSE).

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Time delay estimation using wavelet denoising maximum likelihood method for underwater reverberant environment

Cited by 10 publications

References 48 publications

Geometry estimation for multiple autonomous underwater vehicles using active sound source

Geometry estimation for multiple autonomous underwater vehicles using active sound source

An improved deconvolution beamforming algorithm for acoustic imaging of low signal-to-noise ratio sound sources in reverberant field

Low Complexity Single Carrier Frequency Domain Detectors for Internet of Underwater Things (IoUT)s

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