Inversion of UEP signatures induced by ships based on PSO method

Yu, Peng; Jin-fang, Cheng; Jiang, Runxiang

doi:10.1016/j.dt.2019.06.015

Cited by 14 publications

(3 citation statements)

References 2 publications

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“…In recent years, an attempt to compute shallow water signatures from stationary signatures was published by a group in 2022, which is based on an analytical computational model [3]. For numerical models, a PSO optimization method was presented in 2020 to perform inversions of UEP signatures associated with the representation of vessels emitting only DC signatures [4].…”

Section: Introductionmentioning

confidence: 99%

Incoherent Shallow-Water Seabed Logging Using Numerical-Model-Based Optimization for the Prediction of Unperturbed UEP Signatures

Broecheler,

Thiel,

Ludwar

et al. 2023

JMSE

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Underwater electric potential (UEP) ship signatures are evaluated at a test site that provides only incoherent data from electric field sensors placed on the seabed. This paper shows how these data sets can be used to extract significant seabed parameters, which are evaluated by a numerical model combined with an optimization process in the frequency range from 1 Hz up to 100 kHz. This is in contrast to the controlled electromagnetic source technology used by geophysicists, which has to track both phase and amplitude, and, therefore, cannot simply be applied to data from per se stationary systems. Using the aforementioned frequency range within COMSOL Multiphysics (v6.0), the finite-element-based simulation software, a sensitivity analysis of seabed parameter behavior within a seabed depth range from 1 m to 20 m is evaluated for the highest sensitivity for each of the measurable field amplitudes of the locally available sensors. From this, the electrical conductivities of two seabed layers and the depth of the first layer can be extracted using a combination of a genetic algorithm and the Nelder–Mead method. In summary, phase information is not required to characterize the stratified seabed, providing a cost-effective approach to obtaining effective environmental conditions for free-water signature calculations.

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Section: Introductionmentioning

confidence: 99%

Incoherent Shallow-Water Seabed Logging Using Numerical-Model-Based Optimization for the Prediction of Unperturbed UEP Signatures

Broecheler,

Thiel,

Ludwar

et al. 2023

JMSE

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“…(2) The parameter optimization ability of the PSO algorithm [21] is used to obtain the best preference parameter and damping factor parameter of the AP algorithm, automatically eliminate the AP algorithm oscillation, converge to the optimal clustering result, and improve clustering accuracy…”

Section: Introductionmentioning

confidence: 99%

Radar Working State Recognition Based on the Unsupervised and Incremental Method

et al. 2021

Journal of Sensors

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Radar working state recognition is the basis of cognitive electronic countermeasures. Aiming at the problem that the traditional supervised recognition technology is difficult to obtain prior information and process the incremental signal data stream, an unsupervised and incremental recognition method is proposed. This method is based on a backpropagation (BP) neural network to construct a recognition model. Firstly, the particle swarm optimization (PSO) algorithm is used to optimize the preference parameter and damping factor of affinity propagation (AP) clustering. Then, the PSO-AP algorithm is used to cluster unlabeled samples to obtain the best initial clustering results. The clustering results are input as training samples into the BP neural network to train the recognition model, which realizes the unsupervised recognition. Secondly, the incremental AP (IAP) algorithm based on the K -nearest neighbor (KNN) idea is used to divide the incremental samples by calculating the closeness between samples. The incremental samples are added to the BP recognition model as a new known state to complete the model update, which realizes incremental recognition. The simulation experiments on three types of radar data sets show that the recognition accuracy of the proposed model can reach more than 83%, which verifies the feasibility and effectiveness of the method. In addition, compared with the AP algorithm and K -means algorithm, the improved AP method improves 59.4%, 17.6%, and 53.5% in purity, rand index (RI), and F -measure indexes, respectively, and the running time is at least 34.8% shorter than the AP algorithm. The time of processing incremental data is greatly reduced, and the clustering efficiency is improved. Experimental results show that this method can quickly and accurately identify radar working state and play an important role in giving full play to the adaptability and timeliness of the cognitive electronic countermeasures.

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“…Measuring the UEP signature at a specific measurement site gives insight in the shape and the maximum value of the UEP signature for given environmental conditions at said location but the UEP signature may alter significantly when the environmental conditions or the impressed currents of the corrosion protection system are changing and hence, the accurate prediction of the UEP signature is aggravated. Usually, to model the UEP signature of naval vessels inverse modeling based on the particle swarm optimization (PSO) [1] is used where multiple point-electrodes are positioned within the model of the naval vessel to adapt a known UEP signature at a specific depth below the vessel [2]. However, it could be shown that even without inverse modeling, UEP signature predictions for various environmental conditions using simple mathematical formulations are feasible [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Simple Superposition Formulation to Predict the Underwater Electric Potential Signature of Naval Vessels

Thiel

Broecheler

Ludwar

et al. 2020

JMSE

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The underwater electric potential (UEP) signature is an electric signal, which can be exploited by naval mines to be utilized as a possible trigger indicator and may cause severe damage to the vessel and the onboard crew. Hence, knowing the UEP signature as exactly as possible can help to evaluate a possible risk of the vessel being detected by naval mines or if the UEP signature is within a noncritical region. As the UEP signature differs for changes of the corrosion protection system, the UEP signature is usually unknown for new conditions. In this work, we present a simple mathematical formulation to predict the UEP signature based on the mere use of a single reference UEP signature, and the corresponding currents, which are excited by the impressed current cathodic protection (ICCP) system. With this methodology, deviations below 10% between the maximum of the simulated UEP signature and the predicted UEP signature can be achieved, even in the presence of the nonlinear corrosion process. Furthermore, a corrosion protective coating of the propellers can significantly reduce the influence of the nonlinear corrosion process on the total UEP signature to improve the prediction accuracy of the superposition formulation as presented in this work.

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Inversion of UEP signatures induced by ships based on PSO method

Cited by 14 publications

References 2 publications

Incoherent Shallow-Water Seabed Logging Using Numerical-Model-Based Optimization for the Prediction of Unperturbed UEP Signatures

Incoherent Shallow-Water Seabed Logging Using Numerical-Model-Based Optimization for the Prediction of Unperturbed UEP Signatures

Radar Working State Recognition Based on the Unsupervised and Incremental Method

A Simple Superposition Formulation to Predict the Underwater Electric Potential Signature of Naval Vessels

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