To address the performance degradation caused by power fluctuations of tones, the multi-frame coherent integration-based track-before-detect (TBD) method is proposed. In the proposed method, the high gain advantage of coherent integration and the information accumulation ability of multi-frame TBD method based on batch processing technique are combined to improve the detection and tracking performance of fluctuating tones. Firstly, a new measurement model is established with batch processing technology and coherent integration across multiple data frames is achieved utilizing the tonal coherence, thus the SNR in measurement is greatly improved. Then, a new likelihood ratio function is derived to match the established measurement model, ensuring that TBD processing can be performed correctly. Finally, the effects of the newly established likelihood ratio and the batch length on the detection performance are analyzed in detail through theoretical derivations. The superiorities and robustness of the proposed method are demonstrated through simulation analysis and processing results for sea experimental data.

Abstract The detection of weak fluctuating spectral lines emitted by underwater and surface vehicles poses a challenging problem for passive sonar system. Therefore, a spectral line reconstruction algorithm based on deep learning called the DEDAN, is proposed. The DEDAN learns the time-frequency correlation of spectral lines through end-to-end training and then reconstructs the spatial location of spectral lines. Simulation results show that the DEDAN is robust to ambient noise, and outperforms other reconstruction algorithms at a mixed signal-to-noise ratio as low as -22 dB to -26 dB. Its reconstruction performance is also verified by the measured South China Sea data.

In order to achieve local accuracy and global drift free positioning capabilities of autonomous robots in complex large-scale environments, this paper proposes a visual inertial odometer (VIO) based on point line features and a global satellite navigation system (GNSS) multi-source information fusion algorithm for simultaneous positioning and map construction. Firstly, this system incorporates a line feature algorithm that can perform edge segment detection to more intuitively extract structural information in the environment, effectively improving the accuracy of pose estimation in weak texture situations; Secondly, using a multi-source fusion algorithm, a loose combination positioning model of VIO and GNSS is introduced to eliminate the cumulative error of VIO pose estimation results using GNSS positioning information; Finally, this system was tested on three different datasets. Experiments have shown that this system can improve the motion estimation accuracy of VIO between adjacent frame images in weak texture environments and suppress the cumulative error of VIO in large-scale environments, with strong real-time and robustness.

The detection of sinusoidal signals embedded in noise is an important topic in passive sonar signal processing. The performance of existing tone detection techniques is limited by the Doppler frequency shift, which is caused by the relative motion between moving targets and the receiver. In this study, an algorithm based on long-time coherent integration is proposed to achieve robust tonal signal detection under the influence of a Doppler frequency shift. First, the received narrowband signals radiated by moving targets are split into multiple segments through a window-length constraint. The results obtained by applying a discrete Fourier transform (DFT) to the data segments are modeled as a polynomial phase signal in the frequency domain. Then, the polynomial Radon-polynomial Fourier transform (PRPFT) is applied to simultaneously compensate the time-variant frequency drift and phase difference. To avoid the gain loss due to the discretization of the phase difference compensation in PRPFT, a phase compensation factor searching algorithm is proposed. The simulation results show that the proposed algorithm can provide higher frequency resolution and higher coherent integration gain even in the case of a severe Doppler shift. Furthermore, the results of sea trials demonstrate that the proposed method can perfectly achieve coherent integration for signals with a duration of several hundred seconds under different experimental conditions.

Non-Gaussian impulsive noise in marine environments strongly influences the detection of weak spectral lines. However, existing detection algorithms based on the Gaussian noise model are futile under non-Gaussian impulsive noise. Therefore, a deep-learning method called AINP+LR-DRNet is proposed for joint detection and the reconstruction of weak spectral lines. First, non-Gaussian impulsive noise suppression was performed by an impulsive noise preprocessor (AINP). Second, a special detection and reconstruction network (DRNet) was proposed. An end-to-end training application learns to detect and reconstruct weak spectral lines by adding into an adaptive weighted loss function based on dual classification. Finally, a spectral line-detection algorithm based on DRNet (LR-DRNet) was proposed to improve the detection performance. The simulation indicated that the proposed AINP+LR-DRNet can detect and reconstruct weak spectral line features under non-Gaussian impulsive noise, even for a mixed signal-to-noise ratio as low as −26 dB. The performance of the proposed method was validated using experimental data. The proposed AINP+LR-DRNet detects and reconstructs spectral lines under strong background noise and interference with better reliability than other algorithms.

With the rapid development of GNSS jamming technology, GNSS is facing increasingly severe challenges. Under the condition of GNSS rejection in complex electromagnetic environment, many devices cannot accurately perceive the surrounding environment and thus cannot work normally. Therefore, exploring autonomous navigation under the condition of GNSS rejection is one of the hot topics in the future research. With the rapid development of computer vision, visual inertial odometer (VIO), which is closely coupled with camera and inertial measurement unit (IMU), can obtain high precision local pose results in unknown environments, and is widely concerned for its low cost and miniaturization. In complex and changeable structured scenes, sparse and structured features are still the bottleneck of restricting the performance of visual navigation. In this paper, LSD line segment extraction algorithm is added on the basis of visual inertial odometer to extract more line features of environmental structure, and the sliding window strategy is used to achieve state optimization. In order to verify the effectiveness of this algorithm, this paper tests the proposed method using the open data set. The test results show that the proposed method can effectively provide position and attitude estimation when GNSS refuses. In the test, the error mean value of 0.119 m, the minimum error of 0.015 m, and the maximum error of 0.259 m can be achieved. Compared with the traditional point feature VIO, the precision is improved by 50.6

Seabed geoacoustic parameters play an important role in underwater acoustic channel modeling. Traditional methods to determine these parameters, for example, drilling, are expensive and are being replaced by acoustic inverse technology. An inversion method based on Bayesian theory is presented to derive the structure and geoacoustic parameters of a layered seabed in a shallow sea. The seabed was considered a layered elastic medium. The objective of this research was to use the sound pressure detected by underwater acoustic sensors at different positions and to use nonlinear Bayesian inversion to estimate the geoacoustic parameters and their uncertainties in the multi-layer seabed. Specifically, the thickness, density, compression wave speed, shear wave speed, and the attenuation of these two wave speeds were determined. The maximum a posterior (MAP) model and posterior probability distribution of each parameter were estimated using the optimized simulated annealing (OSA) and Metropolis-Hastings sampling (MHS) methods. Model selection was carried out using the Bayesian information criterion (BIC) to determine the optimal model that thoroughly explained the experimental data for different parameterizations. The results showed that the OSA is much more capable of delivering high-accuracy results in multi-layer seabed models. The compression wave speed and shear wave speed were less uncertain than the other parameters, and the parameters in the upper layer had less uncertainty than those in the lower layer.

With the increasing shortage of resources and energy, the traditional GDP cannot reflect the comprehensive economic strength of the country, so the GGDP has attracted great attention from the governments of all countries and the international community. This paper developed an evaluation model for the impact of human activity on climate change. The human activities affecting climate are summarized as industrial production activities, agricultural production activities, human reproduction, transportation and man-made disasters, and the index system was established from these five aspects. The objective weight of the index is calculated by DEA, the subjective weight of the index is calculated by AHP, the comprehensive weight is calculated by game theory empowerment, and finally the score of the impact of human activities on climate change is calculated by TOPSIS. Mining GGDP model indicators and the common influencing factors of human activities, to improve the influencing factors as a decision variable, establish linear planning model, update the calculation results of the price model index data, get new national scores and previous policy implementation, through paired sample t test proved that the climate change before and after the implementation of the significant differences.

This paper presents a modal-based geoacoustic inversion method adapted for a very-low-frequency leaky waveguide. It is applied to air gun data collected by a seismic streamer during the multi-channel seismic exploration experiment in the South Yellow Sea. The inversion is carried out by filtering the waterborne and bottom-trapped mode pairs from the received signal and comparing the modal interference features (waveguide invariant) to replica fields. The effective seabed models are inferred at two positions, and the two-way-travel time of basement interface reflected waves calculated using these models exhibit good agreement with geological exploration results.

In the new era of robustness and perception, the Visual-Inertial Odometry (VIO), which is tightly coupled by the camera and the Inertial Measurement Unit (IMU), can obtain high-precision local pose results in unknown environment. Its low cost and miniaturization have received widespread attention. However, due to the limitation of the measurement principle, in the long-term runs, error will still accumulate. In addition, the outdoor large-scale environment is also a major challenge facing VIO. The Global Navigation Satellite System (GNSS) can provide accurate global estimates for VIO in an open outdoor environment and correct drift caused by long-term operation. Similarly, VIO can still perform in environments where GNSS is denied, which makes it possible for seamless indoor and outdoor navigation. Therefore, this paper proposes a visual-inertial SLAM algorithm assisted by GNSS. Taking the optimized tightly coupled VIO as the main body, and the pose information obtained by GNSS is combined with the VIO solution result to enhance the global positioning while ensuring the accuracy of the local pose accuracy. To this end, a simulation experiment based on the KITTI data set was carried out. The results show that the VIO system with the aid of GNSS can achieve the accuracy of 1.687 m error average, 1.176 m standard deviation, and 2.056 m root mean square error, which is nearly 80% higher than that without assistance. And it can also play a role in the environment where GNSS is denied, and the robustness of the system is also enhanced.

This work is concerned with the characteristics of very low frequency sound propagation (VLF, ≤100 Hz) in the shallow marine environment. Under these conditions, the classical hypothesis of considering the sea bottom as a fluid environment is no longer appropriate, and the sound propagation characteristics at the sea bottom should be also considered. Hence, based on the finite element method (FEM), and setting the sea bottom as an elastic medium, a proposed model which unifies the sea water and sea bottom is established, and the propagation characteristics in full waveguides of shallow water can be synchronously discussed. Using this model, the effects of the sea bottom topography and the various geoacoustic parameters on VLF sound propagation and its corresponding mechanisms are investigated through numerical examples and acoustic theory. The simulation results demonstrate the adaptability of the proposed model to complex shallow water waveguides and the accuracy of the calculated acoustic field. For the sea bottom topography, the greater the inclination angle of an up-sloping sea bottom, the stronger the leak of acoustic energy to the sea bottom, and the more rapid the attenuation of the acoustic energy in sea water. The effect of a down-sloping sea bottom on acoustic energy is the opposite. Moreover, the greater the pressure wave (P-wave) speed in the sea bottom, the more acoustic energy remains in the water rather than leaking into the bottom; the influence laws of the density and the shear wave (S-wave) speed in the sea bottom are opposite.

A high entropy alloy (HEA) with a composition of Ti2ZrMo0.5Nb0.5 was prepared by vacuum induction melting technology. The mechanical properties at room temperature and elevated temperature (900 °C–1150 °C), microstructure evolution and hot deformation behavior during hot deformation are studied. The as-cast alloy is composed of equiaxed grains with the yield strength, the apparent plastic strain of 1306 MPa and 44%, respectively. By TEM analysis, the ZrTi2 Laves C15 phase is found corresponding the precipitation strengthening effect accompanied with the solid solution strengthening as a ramification of the high-entropy alloy. Then the hot compression tests were carried out. The deformed alloy still maintains the same single BCC structure as the as-cast state. It is noted that when the temperature is 900 °C, the structure of the alloy mainly exhibits dynamic recovery (DRV) characteristics, and when the temperature reaches 1000 °C, there are clear dynamic recrystallization (DRX) characteristics in the structure, which are analyzed as the features of continuous dynamic recrystallization (CDRX). Keeping the temperature at 1150 °C, when the strain rate is 0.1 s−1, the characteristics of discontinuous dynamic recrystallization (DDRX) appear, and as the strain rate decreases, CDRX becomes the main recrystallization mechanism again.

In a deep sea sound channel, rays will bend due to the sound speed profile, and convergence zone will occur when the rays are intensive. Transmission loss in the convergence zone is smaller and it is conducive to acoustic detection and communication. Therefore the study of acoustic characteristics in convergence zone is always the focus of deep-sea acoustics. A long-range sound propagation experiment is conducted in the South China Sea. An equivalent broadband explosive sound source of 1 kg is placed at a depth of 200 m, and the hydrophone receives the data at 3146 m far. The processing and analysis of the experimental data indicate that there is a convergence zone below the sound channel axis in the incomplete deep channel. Compared with the upper turning point convergence zone near the surface, this convergence zone has a high convergence gain at a long distance. The caustic lines of refracted type and refracted surface-refleted type are determined by means of ray-normal mode theory. It is found that the location of the deep convergence zone observed in the experiment is consistent with the position of the refracted caustic line. It is proved that the convergence zone is a lower turning point convergence zone formed by the superposition of a large number of normal modes in the same phase, and it has a convergence effect at a certain depth below the sound channel axis in the deep sea. The formation conditions of the convergence zone and the influence of sound source depth on the caustic structure of the convergence zone are studied. The comparisons of the transmission loss and the width between the upper and lower turning point convergence zone at a long distance aremade. The analysis shows that the convergence gain in the seventh lower turning point convergence zone is still no less than 10 dB. The influence of the vertical structure of sound velocity on the lower turning point convergence zone is studied. The theoretical analysis results are in good agreement with the experimental data.

Recent studies have illustrated that the Multichannel Analysis of Surface Waves (MASW) method is an effective geoacoustic parameter inversion tool. This particular tool employs the dispersion property of broadband Scholte-type surface wave signals, which propagate along the interface between the sea water and seafloor. It is of critical importance to establish the theoretical Scholte wave dispersion curve computation model. In this typical study, the stiffness matrix method is introduced to compute the phase speed of the Scholte wave in a layered ocean environment with an elastic bottom. By computing the phase velocity in environments with a typical complexly varying seabed, it is observed that the coupling phenomenon occurs among Scholte waves corresponding to the fundamental mode and the first higher-order mode for the model with a low shear-velocity layer. Afterwards, few differences are highlighted, which should be taken into consideration while applying the MASW method in the seabed. Finally, based on the ingeniously developed nonlinear Bayesian inversion theory, the seafloor shear wave velocity profile in the southern Yellow Sea of China is inverted by employing multi-order Scholte wave dispersion curves. These inversion results illustrate that the shear wave speed is below 700 m/s in the upper layers of bottom sediments. Due to the alternation of argillaceous layers and sandy layers in the experimental area, there are several low-shear-wave-velocity layers in the inversion profile.

By the analysis of the experimental data and model calculations, features of the propagation of hydroacoustic signals over a shelf of decreasing depth generated by a low-frequency hydroacoustic emitter at a frequency of 22 Hz and their transformations at the “water–bottom” interface into the Rayleigh waves recorded by a coastal laser strainmeter are studied. Energy estimates for the propagating hydroacoustic signals are presented at different points on the shelf; and such estimates for the transformed seismic–acoustic signals are obtained at the location of a laser strainmeter in the Earth’s crust.

Geoacoustic parameter inversion is a crucial issue in underwater acoustic research for shallow sea environments and has increasingly become popular in the recent past. This paper investigates the geoacoustic parameters in a shallow sea environment using a single-receiver geoacoustic inversion method based on Bayesian theory. In this context, the seabed is regarded as an elastic medium, the acoustic pressure at different positions under low-frequency is chosen as the study object, and the theoretical prediction value of the acoustic pressure is described by the Fast Field Method (FFM). The cost function between the measured and modeled acoustic fields is established under the assumption of Gaussian data errors using Bayesian methodology. The Bayesian inversion method enables the inference of the seabed geoacoustic parameters from the experimental data, including the optimal estimates of these parameters, such as density, sound speed and sound speed attenuation, and quantitative uncertainty estimates. The optimization is carried out by simulated annealing (SA), and the Posterior Probability Density (PPD) is given as the inversion result based on the Gibbs Sampler (GS) algorithm. Inversion results of the experimental data are in good agreement with both measured values and estimates from Genetic Algorithm (GA) inversion result in the same environment. Furthermore, the results also indicate that the sound speed and density in the seabed have fewer uncertainties and are more sensitive to acoustic pressure than the sound speed attenuation. The sea noise could increase the variance of PPD, which has less influence on the sensitive parameters. The mean value of PPD could still reflect the true values of geoacoustic parameters in simulation.

In research into various hydrophysical and hydroacoustic wave processes, it is extremely important to know the regularities of their propagation in the sea at decreasing depths, especially in the shelf areas, and also to know the regularities of their transformation into seismoacoustic processes in the earth crust. In the course of the processing and analysis of the experimental data of our complex experiment, in this paper we investigate these regularities. In our experiment, we used a low-frequency hydroacoustic transmitter that generated harmonic oscillations at the frequency of 22 Hz and received hydroacoustic systems with a shore laser strainmeter. It was established that hydroacoustic waves, propagating at the shelf of decreasing depth, transform into seismoacoustic waves at the depth of the sea equal to or less than a half-length of the hydroacoustic wave. A comparison of the results of this work with earlier-obtained results allows us to state that such regularities should be inherent to all hydrophysical and hydroacoustic processes.

Passive source localization is a challenging task for one receiver, and the pressure sensor provides relatively simple information. An ocean-bottom seismometer (OBS) sensor placed on the seafloor surface can provide more informationnot only pressure information, but also three-axis (x-, y-, and z-axis) velocity information at the seafloor interface. In this paper, an OBS sensor was used to estimate the position of the broadband sound source in a Pekeris shallow water waveguide with elastic bottom. As the dynamics that characterize ocean acoustic applications are inherently nonlinear, non-Gaussian, and non-stationary processes that quickly vary with space and time, sequential Bayesian filtering, such as particle filtering (PF), is able to adapt to these environmental changes. Simulation results show that the PF method with the vertical wave impedance (the ratio of the pressure and vertical particle velocity) in the frequency domain as a measurement vector is not affected by source depth and source spectrum information, making it more tolerant and more robust than that with pressure in positioning. Experimental data results verified the effectiveness of the PF method with the vertical wave impedance for the localization of the explosive source.