Robust Direction Finding via Acoustic Vector Sensor Array with Axial Deviation under Non-Uniform Noise

Wang, Weidong; Li, Xiangshui; Zhang, Kai; Shi, Juan; Shi, Wentao; Ali, Wasiq

doi:10.3390/jmse10091196

Cited by 6 publications

(2 citation statements)

References 44 publications

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“…Many significant studies have been investigated in the literature to achieve effective motion prediction of underwater dynamic target [9]. The initial attempt in this field involved the use of a single model Kalman filtering algorithm.…”

Section: Introductionmentioning

confidence: 99%

Scaled Conjugate Gradient Neural Intelligence for Motion Parameters Prediction of Markov Chain Underwater Maneuvering Target

Ali,

Zuberi,

Qing

et al. 2024

JMSE

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This study proposes a novel application of neural computing based on deep learning for the real-time prediction of motion parameters for underwater maneuvering object. The intelligent strategy utilizes the capabilities of Scaled Conjugate Gradient Neural Intelligence (SCGNI) to estimate the dynamics of underwater target that adhere to discrete-time Markov chain. Following a state-space methodology in which target dynamics are combined with noisy passive bearings, nonlinear probabilistic computational algorithms are frequently used for motion parameters prediction applications in underwater acoustics. The precision and robustness of SCGNI are examined here for effective motion parameter prediction of a highly dynamic Markov chain underwater passive vehicle. For investigating the effectiveness of the soft computing strategy, a steady supervised maneuvering route of undersea passive object is designed. In the framework of bearings-only tracking technology, system modeling for parameters prediction is built, and the effectiveness of the SCGNI is examined in ideal and cluttered marine atmospheres simultaneously. The real-time location, velocity, and turn rate of dynamic target are analyzed for five distinct scenarios by varying the standard deviation of white Gaussian observed noise in the context of mean square error (MSE) between real and estimated values. For the given motion parameters prediction problem, sufficient Monte Carlo simulation results support SCGNI’s superiority over typical generalized pseudo-Bayesian filtering strategies such as Interacting Multiple Model Extended Kalman Filter (IMMEKF) and Interacting Multiple Model Unscented Kalman Filter (IMMUKF).

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Section: Introductionmentioning

confidence: 99%

Scaled Conjugate Gradient Neural Intelligence for Motion Parameters Prediction of Markov Chain Underwater Maneuvering Target

Ali,

Zuberi,

Qing

et al. 2024

JMSE

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some traditional DOA estimation methods have some shortcomings. The traditional beamforming algorithm is affected by large side-lobe fluctuations [ 5 ]. While the MUSIC method has a strong dependence on the estimation accuracy of the array output covariance matrix and noise subspace, its performance seriously degrades in the case of low SNR and an insufficient number of snapshots [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor Array

Xie

Wang

2023

Sensors

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Acoustic vector sensor (AVS) is a kind of sensor widely used in underwater detection. Traditional methods use the covariance matrix of the received signal to estimate the direction-of-arrival (DOA), which not only loses the timing structure of the signal but also has the problem of weak anti-noise ability. Therefore, this paper proposes two DOA estimation methods for underwater AVS arrays, one based on a long short-term memory network and attention mechanism (LSTM-ATT), and the other based on Transformer. These two methods can capture the contextual information of sequence signals and extract features with important semantic information. The simulation results show that the two proposed methods perform much better than the multiple signal classification (MUSIC) method, especially in the case of low signal-to-noise ratio (SNR), the DOA estimation accuracy has been greatly improved. The accuracy of the DOA estimation method based on Transformer is comparable to that of the DOA estimation method based on LSTM-ATT, but the computational efficiency is obviously better than that of the DOA estimation method based on LSTM-ATT. Therefore, the DOA estimation method based on Transformer proposed in this paper can provide a reference for fast and effective DOA estimation under low SNR.

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Eigenstructure methods for DOA estimation of circular acoustic vector sensor array with axial angle bias in nonuniform noise

Shi,

Xu,

Zhang

et al. 2024

Digital Signal Processing

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Robust Direction Finding via Acoustic Vector Sensor Array with Axial Deviation under Non-Uniform Noise

Cited by 6 publications

References 44 publications

Scaled Conjugate Gradient Neural Intelligence for Motion Parameters Prediction of Markov Chain Underwater Maneuvering Target

Scaled Conjugate Gradient Neural Intelligence for Motion Parameters Prediction of Markov Chain Underwater Maneuvering Target

Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor Array

Eigenstructure methods for DOA estimation of circular acoustic vector sensor array with axial angle bias in nonuniform noise

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