A cloud Bayesian network approach to situation assessment of scouting underwater targets with fixed-wing patrol aircraft

The battlefield situation changes rapidly because underwater targets'are concealment and the sea environment is uncertain.So,a great number of situation information greatly increase,which need to be dealt with in the course of scouting underwater targets.Situation assessment in sea battlefield with a lot of uncertain information is studied,and a new situation assessment method of scouting underwater targets with fixed-wing patrol aircraft is proposed based on the cloud Bayesian network,which overcomes the deficiency of the single cloud model in reasoning ability and the defect of Bayesian network in knowledge representation.Moreover,in the method,the cloud model knowledge deal with the input data of Bayesian network reasoning,and the advantages in knowledge representation of cloud theory and reasoning of Bayesian network are applied;also,the fuzziness and stochasticity of cloud theory in knowledge expression,the reasoning ability of Bayesian network,are combined.Then,the situation assessment model of scouting underwater targets with fixed-wing patrol aircraft is established.Hence,the directed acyclic graph of Bayesian network structure is constructed and the assessment index is determined.Next,the cloud model is used to deal with Bayesian network,and the discrete Bayesian network is obtained.Moreover,after CPT of each node and the transformation between certainty degree and probability are accomplished;the final situation level is obtained through a probability synthesis formula.Therefore,the target type and the operational intention of the other side are deduced to form the battlefield situation.Finally,simulations are carried out,and the rationality and validity of the proposed method are testified by simulation results.By this method,the battlefield situation can be gained.And this method has a wider application range,especially for large sample data processing,and it has better practicability.

An auditory periphery model for improving narrow-band noise recognition rate of underwater targets

The recognition rate of the auditory periphery features decreases when the model is used to identify underwater targets in practice. To solve this problem, an improved method based on Gammatone filter bank is proposed. Firstly, after the reason of the decreasing of the recognition results is analyzed, the mechanism of multichannel data acquisition in acoustic engineering may narrow down signal frequency range, which leads to time-frequency features distortion. Secondly, the Gammatone filter bank is implemented to simulate frequency decom- position characteristics of human ear basilar membrane. Since the class information of the underwater noise signal is mostly contained in low frequency range, the auditory features of the conventional model are interpolated and the channel number of the filter bank and the central frequency of each frequency band are adjusted accordingly to obtain a 27-dimensional feature vector of the narrow-band target signal. The adjusted model may reflect the target＇s time- frequency feature more precisely. Finally, the performance of the auditory features is tested by a Neural Network classifier. The experiment results show that the modified auditory model is more effective than the conventional ones. The major information contained in broadband signals is reserved and the classification ability for real targets is further enhanced. The recog- nition results are increased from 82.59% to 88.80%. The modified auditory features effectively improve the recognition rate for underwater target radiated noise signals.

UnderwaterWaste Recognition and Localization Based on Improved YOLOv5

With the continuous development of the economy and society,plastic pollution in rivers,lakes,oceans,and other bodies of water is increasingly severe,posing a serious challenge to underwater ecosystems.Effective cleaning up of underwater litter by robots relies on accurately identifying and locating the plastic waste.However,it often causes significant challenges such as noise interference,low contrast,and blurred textures in underwater optical images.A weighted fusion-based algorithm for enhancing the quality of underwater images is proposed,which combines weighted logarithmic transformations,adaptive gamma correction,improved multi-scale Retinex(MSR)algorithm,and the contrast limited adaptive histogram equalization(CLAHE)algorithm.The proposed algorithm improves brightness,contrast,and color recovery and enhances detail features resulting in better overall image quality.A network framework is proposed in this article based on the YOLOv5 model.MobileViT is used as the backbone of the network framework,detection layer is added to improve the detection capability for small targets,self-attention and mixed-attention modules are introduced to enhance the recognition capability of important features.The cross stage partial(CSP)structure is employed in the spatial pyramid pooling(SPP)section to enrich feature information,and the complete intersection over union(CIOU)loss is replaced with the focal efficient intersection over union(EIOU)loss to accelerate convergence while improving regression accuracy.Experimental results proved that the target recognition algorithm achieved a recognition accuracy of 0.913 and ensured a recognition speed of 45.56 fps/s.Subsequently,Using red,green,blue and depth(RGB-D)camera to construct a system for identifying and locating underwater plastic waste.Experiments were conducted underwater for recognition,localization,and error analysis.The experimental results demonstrate the effectiveness of the proposed method for identifying and locating underwater plastic waste,and it has good localization accuracy.

Underwater Noise Target Recognition Based on Sparse Adversarial Co-Training Model with Vertical Line Array

The automatic identification of underwater noncooperative targets without label records remains an arduous task considering the marine noise interference and the shortage of labeled samples.In particular,the data-driven mechanism of deep learning cannot identify false samples,aggravating the difficulty in noncooperative underwater target recognition.A semi-supervised ensemble framework based on vertical line array fusion and the sparse adversarial co-training algorithm is proposed to identify noncooperative targets effectively.The sound field cross-correlation compression(SCC)feature is developed to reduce noise and computational redundancy.Starting from an incomplete dataset,a joint adversarial autoencoder is constructed to extract the sparse features with source depth sensitivity,aiming to discover the unknown underwater targets.The adversarial prediction label is converted to initialize the joint co-forest,whose evaluation function is optimized by introducing adaptive confidence.The experiments prove the strong denoising performance,low mean square error,and high separability of SCC features.Compared with several state-of-the-art approaches,the numerical results illustrate the superiorities of the proposed method due to feature compression,secondary recognition,and decision fusion.

Method for Underwater Target Tracking Based on an Interacting Multiple Model

According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference （FIMM） is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model （CA）, the Singer mode~, and the nearly constant speed horizontal-turn model （HT） in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm.

Classification of Underwater Target Echoes Based on Auditory Perception Characteristics

In underwater target detection, the bottom reverberation has some of the same properties as the target echo, which has a great impact on the performance. It is essential to study the difference between target echo and reverberation. In this paper, based on the unique advantage of human listening ability on objects distinction, the Gammatone filter is taken as the auditory model. In addition, time-frequency perception features and auditory spectral features are extracted for active sonar target echo and bottom reverberation separation. The features of the experimental data have good concentration characteristics in the same class and have a large amount of differences between different classes, which shows that this method can effectively distinguish between the target echo and reverberation.

Bark-Wavelet Analysis and Hilbert -Huang Transform for Underwater Target Recognition

Recognizing the underwater targets by the radiated noise information is one of the most significant subjects in the area of underwater acoustics. Based on the theory of auditory perception, a novel recognition approach which consists of the algorithms of Bark-wavelet analysis, Hilbert-Huang transform and support vector machine is proposed. The performance of the proposed method is validated by comparing with traditional method and evaluated by the recognition experiments for SNRs of 0 dB, 5 dB, 10 dB, 15 dB and 20 dB.The results show that the average recognition rate of the method is above 88% and can be increased by 0.75 % to 6.25% under various SNR conditions compared to the baseline system.

Morphological Research on Geometrical Scattering Waves of an Underwater Target

In this paper, a new method based on morphologic research named reconstruction cross-component removal （RCCR） is developed to analyze geometrical scattering waves of an underwater target. Combining the origin of the cross-component in Wigner-ViUe distribution, the highlight model of target echoes and time-frequency features of linear frequency-modulated signal can remove cross-components produced by multiple component signals in Wigner-Ville distribution and recover the auto-components of output signals. This method is used in experimental data processing, which can strengthen the real geometric highlights, and restrain the cross components. It is demonstrated that this method is helpful to analyze the geometrical scattering waves, providing an effective solution to underwater target detection and recognition.

Underwater Target Detection Based on Reinforcement Learning and Ant Colony Optimization

Underwater optical imaging produces images with high resolution and abundant information and hence has outstanding advantages in short-distance underwater target detection.However,low-light and high-noise scenarios pose great challenges in un-derwater image and video analyses.To improve the accuracy and anti-noise performance of underwater target image edge detection,an underwater target edge detection method based on ant colony optimization and reinforcement learning is proposed in this paper.First,the reinforcement learning concept is integrated into artificial ants’movements,and a variable radius sensing strategy is pro-posed to calculate the transition probability of each pixel.These methods aim to avoid undetection and misdetection of some pixels in image edges.Second,a double-population ant colony strategy is proposed,where the search process takes into account global search and local search abilities.Experimental results show that the algorithm can effectively extract the contour information of underwater targets and keep the image texture well and also has ideal anti-interference performance.

Symmetric Learning Data Augmentation Model for Underwater Target Noise Data Expansion

An important issue for deep learning models is the acquisition of training of data.Without abundant data from a real production environment for training,deep learning models would not be as widely used as they are today.However,the cost of obtaining abundant real-world environment is high,especially for underwater environments.It is more straightforward to simulate data that is closed to that from real environment.In this paper,a simple and easy symmetric learning data augmentation model(SLDAM)is proposed for underwater target radiate-noise data expansion and generation.The SLDAM,taking the optimal classifier of an initial dataset as the discriminator,makes use of the structure of the classifier to construct a symmetric generator based on antagonistic generation.It generates data similar to the initial dataset that can be used to supplement training data sets.This model has taken into consideration feature loss and sample loss function in model training,and is able to reduce the dependence of the generation and expansion on the feature set.We verified that the SLDAM is able to data expansion with low calculation complexity.Our results showed that the SLDAM is able to generate new data without compromising data recognition accuracy,for practical application in a production environment.

Special algorithm of enhancing underwater targetradiated dynamic line spectrum

Performance of traditional adaptive line enhancer （ALE） in suppressing Gaussian noise is low and can get worse at low input signal-to-noise ratio（SNR）. For greatly overcoming these disadvantages, feature of fourth-order cumulant （FOC） different slices for quasi-stationary random process is analyzed, fourth order cumulant（FOC） different slice-based adaptive dynamic line enhancer is presented, and output SNR of the proposed enhancer is derived and bigger than that of the ALE via theoretical analysis. Simulation tests with the underwater moving target-radiated data have shown that the proposed enhancer outperforms the ALE in suppressing Gaussian noise and enhancing dynamic line spectrum feature.

Line spectrum detection algorithm based on the phase feature of target radiated noise

In order to improve the performance of line spectrum detection,according to the feature that the underwater target radiated noise containing stable line spectrum,the differences of the phase difference between line spectrum and background noise,a weighted line spectrum detection algorithm based on the phase variance is proposed in frequency domain.After phase difference alignment,the phase variance of line spectrum and the phase of background noise,respectively,are small and big in frequency domain,this method utilizes the weighted statistical algorithm to cumulate the frequency spectrum based on the phase variance,which can restrain the background noise disturbance,and enhance the signal to noise ratio（SNR）.The theory analysis and experimental results both verify that the proposed method can well enhance the energy of line spectrum,restrain the energy of background noise,and have better detection performance under lower SNR.

A new method of passive ranging for underwater target:distance information extraction based on wave guide invariant

The study of wave guide invariant in underwater acoustics is one of attracted topics in recent 30 years. The interferences of direct wave and reflect wave from sea surface and sea bottom of underwater target radiated noise inherent the information of target distance. Extraction of these distance information will provide a possible new way in passive ranging for underwater target. The theoretical analysis and the results of at sea experiments show that the LOFAR （Low Frequency Analysis Record） figure inherently contains the range and moving information of passive acoustic sources, even in the situation that the receiver is only one single hydrophone. The theoretical analysis of extraction of target distance information by using wave guide invariant is presented in this paper. It is shown that, based on the interference striation pattern of target, the hydrophone array system is possible to extract the distance information with quite high array gain. Although the mathematical constrain conditions in forming interference striation pattern are different for individual array element, but it is proved that the differences of time delays between array elements can be used in compensation of beamforming. The theoretical analysis, system simulation and some results of at sea experiment show a new way in passive ranging and target recognition.

The Thermal Character of the Underwater Heat Exhausting Source

The underwater heat exhausting source can cause the thermal difference of the surrounding and surface water.In this paper,the thermal character caused by the underwater heat exhausting source is studied by numerical simulation and experiment.The results show that the thermal floating distance is related with the sailing velocity of the underwater target.The higher the velocity is,the longer the hot wake is,and the broader the hot scope is.The relative distance of the thermal floating spot is almost in a logarithmic law with the velocity.The experimental results are accordant with the numerical simulation,and the obvious hot wake can be observed by the moving underwater heat exhausting source testing with temperature sensors and infrared camera.

On fast estimation of direction of arrival for underwater acoustic target based on sparse Bayesian learning

The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing speed. To solve this problem, a fast underwater acoustic target direction of arrival estimation was proposed. Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation, an algorithm was proposed to obtain the value of core hyper-parameter through MacKay＇s fixed-point method to estimate the DOA. By this process, it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm. Simulation results verified the feasibility and effectiveness of the proposed algorithm.

Underwater target feature recognition based on distribution of highlights

An algorithm for underwater target feature recognition is proposed using its highlights distribution.For an underwater target with large size and slender body,it is assumed that the heading course and the length of the target are both determined by the distribution of its highlights.By supposing that these highlights obey Gaussian mixture distribution,the feature recognition problem can be transformed into a clustering problem.Therefore,using the collinearly constrained expectation maximization algorithm,the clustering centers of these highlights can be calculated and then the estimation of the heading and length of the target can be obtained with high accuracy.The effectiveness of the proposed method is demonstrated using simulations.

Methods for bearings-only underwater target motion analysis

The problem of estimation of underwater target motion parameters via bearings only is the most of ten encountered and most difficult to solve in the underwater target motion analysis.As the bearings-only target motion analysis is a nonlinear and multiextremal global optimization problem, so most classical estimation methods often lead the solution to convergence to one of the local extremes other than the global extreme, especially, when the noise of target bearing observation is added. In this paper we propose to use the Generalized Least Square method on the rough estimation of target motion parameters, and then use the Sequential Uniform Design method to gain a more precise estimation on the bases of rough estimation.The latter ensures that the result convergences to the global extreme. The algorithm based on the above two methods is profitable for the bearings-only target motion analysis even under conditions of large bearing observation error.

Experimental researches on underwater target scattering properties in non-incident direction

In the bistatic case, theoretical analysis and experimental researches on underwater acoustic scattering properties of some submarine model are made in this paper. When sourcet target and receiver have complicated triangular configuration, the relationships among target strength, incidence angle and bistatic angle are obtained. The validity of this theory is verified by theoretical calculations and tank experiments. The research results can be directly used in bistatic or multistatic underwater acoustic detection systems.

A portable high precision three dimensional trace measuring system for underwater target with high speed

The paper describes a portable high precision three-dimensional trace measuring system for underwater target with high speed. The mathematical model for location, the main error sources, the calibration method for the underwater array and the way to correct its state are discussed. Problems about the distance ambiguity and multi-path interference are also analyzed. Part of experimental results on lake and at sea are given as well.

Prediction of underwater target strength

A model as well as its numerical method to calculate target strength of rigid body using Lighthill's acoustic analogy approach which developed from the propeller aircraft sound field study have been presented. The cases of ellipsoid target has been used to demonstrate the approach. The comparison of the numerical results with that of analytical formulation provides a satisfactory check for the validity of the approach. Some reasonable results have been discussed. The advantage of the present model is that it is suitable for any arbitrarily shaped rigid body moving with small Mach number.