An Improved Particle Filtering Technique for Source Localization and Sound Speed Field Inversion in Shallow Water

Dai, Miao; Li, Yaan; Ye, Jinying; Yang, Kunde

doi:10.1109/access.2020.3027727

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…An upper limit fo the particle number is determined by the maximum number that can be processed wit limited computational resources, which is especially important for real-time filterin algorithms. Although not given here, the influence of different particle numbers on P tracking accuracy and computational cost is demonstrated and evaluated in our previou work [27]. In practice, sea state conditions are complicated and changeable.…”

Section: Asiaex Experiments Data In the Fast-changing Environmentmentioning

confidence: 94%

Joint Tracking of Source and Environment Using Improved Particle Filtering in Shallow Water

Dai

et al. 2021

JMSE

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Shallow water is a complex sound propagation medium, which is affected by the varying spatial–temporal ocean environment. Taking this complexity into account, the classical processing techniques of source localization and environmental inversion may be improved. In this work, a joint tracking approach for the moving source and environmental parameters of the range-dependent and time-evolving environment in shallow water is presented. The tracking scheme treats both the source parameters (e.g., source depth, range, and speed) and the environmental parameters (e.g., water column sound speed profile (SSP) and sediment parameters) at the source location as unknown variables that evolve as the source moves. To counter sample impoverishment and robustly characterize the evolution of the parameters, an improved particle filter (PF), which is an extension of the standard PF, is proposed. Two examples with simulated data in a slowly changing environment and experimental data collected during the ASIAEX experiment are utilized to demonstrate the effectiveness of the joint approach. The results show that we were able to track the source and environmental parameters simultaneously, and the uncertainties were evaluated in the form of time-evolving posterior probability densities (PPDs). The performance comparison confirms that the improved PF is superior to the standard PF, as it can reduce the parameter uncertainties. The tracking capabilities of the improved PF were verified with high accuracy in real-time source localization and well-estimated rapidly varying parameters. Moreover, the influence of different particle numbers on the improved PF tracking performance is also illustrated.

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Section: Asiaex Experiments Data In the Fast-changing Environmentmentioning

confidence: 94%

Joint Tracking of Source and Environment Using Improved Particle Filtering in Shallow Water

Dai

et al. 2021

JMSE

Self Cite

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“…Recently, several classical SSP inversion methods have been proposed, expanding on the MFP foundation. For instance, a method combining particle swarm optimization (PSO) with EOF for SSP inversion was introduced [29]. Another approach involves a single-benchmark assimilation method for ocean SSPs [30].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Few-Shot Prediction of Ocean Sound Speed Profiles through Hierarchical Long Short-Term Memory Transfer Learning

Lu,

Zhang,

et al. 2024

JMSE

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The distribution of ocean sound speed profiles (SSPs) profoundly influences the design of underwater acoustic communication and positioning systems. Conventional methods for measuring sound speed by instruments entail high time costs, while sound speed inversion methods offer rapid estimation of SSPs. However, these methods heavily rely on sonar observational data and lack the capacity to swiftly estimate SSPs in arbitrary oceanic regions, particularly in scenarios with few-shot data. Precisely estimating non-cooperative maritime SSPs under such conditions poses a significant challenge. To explore temporal distribution patterns of sound speed and achieve precise SSP predictions with limited data, we propose a hierarchical long short-term memory transfer learning (H-LSTM-TL) framework. The core idea involves pre-training the base model on extensive public datasets, transferring the acquired knowledge to task models, and fine-tuning the task model on few-shot data to predict future SSPs. Through H-LSTM-TL, it accelerates model convergence, enhances sensitivity to few-shot input data, alleviates overfitting issues, and notably improves the accuracy of SSP predictions. Experimental results demonstrate that the H-LSTM-TL model exhibits strong generalization capabilities in few-shot data scenarios, effectively reducing overfitting problems and proving its applicability for rapid prediction of SSPs.

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“…To improve the accuracy, Liu et al introduced an improved method for estimating SSP based on the single empirical orthogonal function (EOF) regression method [20]. Dai et al introduced an improved particle-filtering technique for sound speed field inversion [21]. Zhang proposed an inversion technique relying on three-dimensional (3D) spatial characteristic sound ray searching and sound propagation time calculation models [22,23].…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Future Full-Ocean Depth SSPs Utilizing Hierarchical Long Short-Term Memory Neural Networks

Lu,

Zhang,

et al. 2024

JMSE

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The spatial-temporal distribution of underwater sound speed plays a critical role in determining the propagation mode of underwater acoustic signals. Therefore, rapid estimation and prediction of sound speed distribution are imperative for facilitating underwater positioning, navigation, and timing (PNT) services. While sound speed profile (SSP) inversion methods offer quicker response times compared to direct measurement methods, these methods often focus on constructing spatial sound velocity fields and heavily rely on sonar observation data, thus imposing stringent requirements on data sources. To delve into the temporal distribution pattern of sound speed and achieve SSP prediction without relying on sonar observation data, we introduce the hierarchical long short-term memory (H-LSTM) neural network for SSP prediction. Our method enables the estimation of sound speed distribution without the need for on-site data measurement, significantly enhancing time efficiency. Compared to other state-of-the-art approaches, the H-LSTM model achieves a root mean square error (RMSE) of less than 1 m/s in predicting monthly average sound speed distribution. Its prediction accuracy has improved several-fold over alternative methods, which validates the robust capability of our proposed model in predicting SSP.

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An Improved Particle Filtering Technique for Source Localization and Sound Speed Field Inversion in Shallow Water

Cited by 3 publications

References 30 publications

Joint Tracking of Source and Environment Using Improved Particle Filtering in Shallow Water

Joint Tracking of Source and Environment Using Improved Particle Filtering in Shallow Water

Enhancing Few-Shot Prediction of Ocean Sound Speed Profiles through Hierarchical Long Short-Term Memory Transfer Learning

Predictive Modeling of Future Full-Ocean Depth SSPs Utilizing Hierarchical Long Short-Term Memory Neural Networks

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