An Enhanced Data-Driven Array Shape Estimation Method Using Passive Underwater Acoustic Data

Beamforming-based signal enhancement technologies in passive sonar array processing often have poor performance due to array distortion caused by rapid tactical maneuvers of the towed platform, oceanic currents, hydrodynamic effects, etc. In this paper, an enhanced data-driven shape array estimation formulation is proposed using passive underwater acoustic data. Beamforming based on a hypothetically ideal array is firstly employed to perform the detection of narrow-band components from sources of opportunity, and the corresponding phases of these detected narrow-band components are subsequently extracted to acquire time-delay differences. Then, a weighted outlier-robust Kalman smoother is proposed to acquire enhanced estimates of the time-delay differences, since the underlying properties of slowly changing time-delay differences in the hydrophone array and diverse signal to interference and noise ratios in multiple narrow-band components are explored; and its Cramer–Rao Lower Bound is also provided. Finally, the hydrophone array shape is estimated based on the estimated time delay differences. The proposed formulation fully exploits directional radiated noise signals from distant underwater acoustic targets as sources of opportunity for real-time array shape estimation, and thus it requires neither the number nor direction of sources to be known in advance. The effectiveness of the proposed method is validated in simulations and real experimental data.

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A Nonlinear Data-Driven Towed Array Shape Estimation Method Using Passive Underwater Acoustic Data

Remote Sensing ◽

10.3390/rs14020304 ◽

2022 ◽

Vol 14 (2) ◽

pp. 304

Author(s):

Qisong Wu ◽

Youhai Xu

Keyword(s):

Time Delay ◽

Time Delays ◽

Narrow Band ◽

Data Driven ◽

Shape Estimation ◽

Underwater Acoustic ◽

Acoustic Data ◽

Towed Array ◽

Hydrophone Array ◽

Array Shape Estimation

Large-aperture towed linear hydrophone array has been widely used for beamforming-based signal enhancement in passive sonar systems; however, its performance can drastically decrease due to the array distortion caused by rapid tactical maneuvers of the towed platform, oceanic currents, hydrodynamic effects, etc. In this paper, an enhanced data-driven shape array estimation scheme is provided in the passive underwater acoustic data, and a novel nonlinear outlier-robust particle filter (ORPF) method is proposed to acquire enhanced estimates of time delays in the presence of distorted hydrophone array. A conventional beamforming technique based on a hypothetical array is first used, and the detection of the narrow-band components is sequentially carried out so that the corresponding amplitudes and phases at these narrow-band components can be acquired. We convert the towed array estimation problem into a nonlinear discrete-time filtering problem with the joint estimates of amplitudes and time-delay differences, and then propose the ORPF method to acquire enhanced estimates of the time delays by exploiting the underlying properties of slowly changing time-delay differences across sensors. The proposed scheme fully exploits directional radiated noise targets as sources of opportunity for online array shape estimation, and thus it requires neither the number nor direction of sources to be known in advance. Both simulations and real experimental data show the effectiveness of the proposed method.

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Real-time Array Shape Estimation Method of Horizontal Suspended Linear Array Based on Non-acoustic Auxiliary Sensors

IEEE Access ◽

10.1109/access.2021.3061446 ◽

2021 ◽

pp. 1-1

Author(s):

Zonglun Che ◽

Jun Wang ◽

Jing Zhu ◽

Bingbing Zhang ◽

Yang Zhang ◽

...

Keyword(s):

Real Time ◽

Linear Array ◽

Estimation Method ◽

Shape Estimation ◽

Array Shape Estimation

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A subspace-based array shape estimation method using a single reference source in nearfield

OCEANS '02 MTS/IEEE ◽

10.1109/oceans.2002.1191973 ◽

2005 ◽

Author(s):

Hee-Young Park ◽

Won Tchon Oh ◽

Dae-Hee Youn ◽

Chungyong Lee

Keyword(s):

Estimation Method ◽

Reference Source ◽

Shape Estimation ◽

Array Shape Estimation

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Underwater Acoustic Time Delay Estimation Based on Envelope Differences of Correlation Functions

Sensors ◽

10.3390/s19051259 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1259 ◽

Cited By ~ 1

Author(s):

Guodong Li ◽

Jinsong Wu ◽

Taolin Tang ◽

Zhixin Chen ◽

Jun Chen ◽

...

Keyword(s):

Time Delay ◽

Correlation Functions ◽

Estimation Method ◽

Digital Signal ◽

Low Complexity ◽

Time Delay Estimation ◽

Delay Estimation ◽

Estimation Errors ◽

Underwater Acoustic ◽

Amplitude Fluctuations

This paper proposes underwater acoustic time delay estimation based on the envelope differences of correlation functions (EDCF), which mitigates the delay estimation errors introduced by the amplitude fluctuations of the correlation function envelopes in the traditional correlation methods (CM). The performance of the proposed delay estimation method under different time values was analyzed, and the optimal difference time values are given. To overcome the influences of digital signal sampling intervals on time delay estimation, a digital time delay estimation approach with low complexity and high accuracy is proposed. The performance of the proposed time delay estimation was analyzed in underwater multipath channels. Finally, the accuracy of the delay estimation using this proposed method was demonstrated by experiments.

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Underwater acoustic array shape estimation and tracking using Kalman filters

IEEE Oceanic Engineering Society. OCEANS'98. Conference Proceedings (Cat. No.98CH36259) ◽

10.1109/oceans.1998.726344 ◽

2002 ◽

Author(s):

M. Bahadori ◽

M. Karimi ◽

K.K. Zadeh ◽

M. Kamarei

Keyword(s):

Kalman Filters ◽

Shape Estimation ◽

Underwater Acoustic ◽

Acoustic Array ◽

Array Shape Estimation

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Passive Towed Array Shape Estimation Using Heading and Acoustic Data

IEEE Journal of Oceanic Engineering ◽

10.1109/joe.2014.2318531 ◽

2015 ◽

Vol 40 (2) ◽

pp. 465-474 ◽

Cited By ~ 10

Author(s):

Jonathan L. Odom ◽

Jeffrey L. Krolik

Keyword(s):

Shape Estimation ◽

Acoustic Data ◽

Towed Array ◽

Array Shape Estimation

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Effect of Noise on Phase Spectrum Time-Delay Estimation Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.610.862 ◽

2014 ◽

Vol 610 ◽

pp. 862-866

Author(s):

Lei Lei Deng

Keyword(s):

Time Delay ◽

Target Location ◽

Estimation Method ◽

Poor Performance ◽

Time Delay Estimation ◽

Phase Spectrum ◽

Delay Estimation ◽

Signal Frequency ◽

The Difference ◽

Computation Load

The phase spectrum time delay estimation (TDE) method is being used widely in trajectory tracking and target location, which due to its superiority in computation load and accuracy. However, the precision of TDE had declined severely as the result of noise. This paper plans to analyze the factor of noise, which has effects on the performance of phase spectrum TDE algorithm based on the difference principle. Theoretical analysis and computer simulation show that the fluctuation of phase caused by noise leads to poor performance. Furthermore, the extent of the influence relates to the time delay and signal frequency.

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Underwater Acoustic Data Communications for Autonomous Platform Command, Control and Communications

10.21236/ada389546 ◽

2001 ◽

Cited By ~ 1

Author(s):

Michael Shinego ◽

Geoff Edelson ◽

Francine Menas ◽

Michael Richman ◽

Robert Nation

Keyword(s):

Data Communications ◽

Underwater Acoustic ◽

Acoustic Data

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Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles

Advances in Mechanical Engineering ◽

10.1177/16878140211027735 ◽

2021 ◽

Vol 13 (7) ◽

pp. 168781402110277

Author(s):

Yankai Hou ◽

Zhaosheng Zhang ◽

Peng Liu ◽

Chunbao Song ◽

Zhenpo Wang

Keyword(s):

Electric Vehicles ◽

Real World ◽

Regression Models ◽

Estimation Method ◽

Recursive Least Squares ◽

Data Driven ◽

Accurate Estimation ◽

Support Vector ◽

Battery Degradation ◽

Operational Data

Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.

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