Jianguo Liu

The dynamic nature of acoustic environments—particularly the fluctuation of underwater channels and time-varying target observation angles—poses significant challenges for active sonar target recognition, a problem further aggravated by the scarcity of labeled training samples. To address these limitations, this paper proposes a novel recognition method enabling deep fusion of multi-domain temporal features extracted from target echoes. First, complementary features are extracted across spatial, time–frequency, and Doppler domains to achieve a comprehensive and discriminative representation of targets. Subsequently, we introduce a feature vector-level fusion mechanism designed specifically for few-shot learning, integrating a meta-knowledge-driven multi-stream feature extractor with an internal memory module within the feature tensor framework. This architecture constitutes the Multi-domain Temporal Feature Fusion Recognition Network (MTFF-RNet). The proposed approach is evaluated on a hybrid dataset combining simulated and experimental data, achieving a high recognition accuracy of 96.2% for both targets and interferents. Experimental results demonstrate that MTFF-RNet significantly enhances robustness and adaptability under varying underwater acoustic conditions and dynamic viewing geometries.

Underwater wireless optical communication (UWOC) has emerged as a high-bandwidth, low-latency technology for marine applications, such as sensor networks, autonomous underwater vehicles (AUVs), and the Internet of Underwater Things (IoUT). However, the underwater channel faces severe impairments from absorption, scattering, and turbulence due to temperature and salinity gradients, and bubbles from wave breaking or biological activity. These cause beam wandering, scintillation, beam spreading, link misalignment, inter-symbol interference (ISI), and signal loss, limiting effective ranges in turbid waters. Accurate modeling of these combined effects is vital for optimizing modulation, error correction, and system reliability. This study combines experimental and numerical approaches to evaluate the performance of UWOC channels under the influence of bubbles, turbulence, absorption, and scattering. Experimentally, small bubbles are produced via microporous ceramics, while turbulence is induced by propeller rotation to mimic real disturbances. Numerically, a composite model employs Monte Carlo simulations (MCS) and ray tracing, integrating phase screen methods and Mie theory to simulate scattering from multisize particles in turbulent conditions. UWOC systems are tested with intensity modulation/direct detection (IM/DD) using on–off keying (OOK), binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), and 16-quadrature amplitude modulation (16-QAM) modulations. Findings show synergistic degradation: turbulence exacerbates scattering by changing photon angular distributions, heightening scintillation, and path distortion. Bubbles have a more pronounced impact than turbulence, leading to higher channel unreliability. The study quantifies how parameters like bubble count, turbulence strength, scattering level, distance, and aperture size affect scintillation index and bit error rate (BER). These results provide critical insights for enhancing underwater optical networks, wake tracking, and IoUT in harsh environments.

To tackle the challenges of relative attitude adaptability and limited sample availability in underwater moving target recognition for active sonar, this study focuses on key aspects such as feature extraction, network model design, and information fusion. A pseudo-three-dimensional spatial feature extraction method is proposed by integrating generalized MUSIC with range–dimension information. The pseudo-WVD time–frequency feature is enhanced through the incorporation of prior knowledge. Additionally, the Doppler frequency shift distribution feature for underwater moving targets is derived and extracted. A multidimensional feature information fusion network model based on meta-learning is developed. Meta-knowledge is extracted separately from spatial, time–frequency, and Doppler feature spectra, to improve the generalization capability of single-feature task networks during small-sample training. Multidimensional feature information fusion is achieved via a feature fusion classifier. Finally, a sample library is constructed using simulation-enhanced data and experimental data for network training and testing. The results demonstrate that, in the few-sample scenario, the proposed method leverages the complementary nature of multidimensional features, effectively addressing the challenge of limited adaptability to relative horizontal orientation angles in target recognition, and achieving a recognition accuracy of up to 97.1%.

Geomagnetic navigation has been widely applied in various fields, with one of its pivotal challenges being the calibration of misalignment error between the triaxial magnetometer and inertial navigation systems (INS). Traditional methods are not suitable for scenarios such as submarines and warships, where it is inconvenient to change large-range attitudes. Therefore, a novel misalignment error model is developed in the paper. Then, based on this model, an objective function containing deviation angles between magnetometer and INS is constructed. Finally, the deviation angles are solved through optimization algorithms, and the calibrated azimuth can be obtained. Moreover, the issue of unknown actual geomagnetic field is resolved using the differential approach during parameter estimation. The method proposed in this paper does not require data collection under large-range or strictly controlled attitudes, nor does it necessitate auxiliary equipment. Both simulation and experiment results confirm the validity and feasibility of the method. The experiment results show that the maximum error in azimuth is approximately 0.6° and the mean absolute deviation (MAD) of the horizontal and vertical components of the geomagnetic field are reduced from 1083.5639 nT, 933.2666 nT to 120.7572 nT, 127.9542 nT respectively. The results indicate that the method can effectively calibrate misalignment error under small-range attitudes, thereby achieving high-precision navigation.

Magnetic gradient tensor has enjoyed great popularity in magnetic anomaly detection recently. The detection method using the full magnetic gradient orthonormal basis function has been proven to be effective in extracting the target’s magnetic anomaly signal. However, the six energy components obtained by the FMG-OBF detector vary with true energy terms (influenced by the orientation of the target’s magnetic moment and the moving trajectories), noise energy terms and middle terms, which largely affects the performance of the total energy. In this paper, an adaptive weighting FMG-OBF method, which is based on the sigmoid function to assign appropriate weights to normalized energy components according to their detection performance, is proposed to tackle this concern. Experiments have demonstrated that the proposed method has a higher signal-to-noise ratio from the perspective of signal energy and is less affected by the OTMM compared with the original method.

Reducing the impact of underwater disturbance targets and improving the ability to recognize real moving targets underwater are important directions of active sonar research. In this paper, the highlight model of underwater targets was improved and a method was proposed to acquire highlight images of the echoes of these targets. A classification convolutional neural network called HasNet-5 was designed to extract the global features and local highlight features of the echo highlight images of underwater targets, which achieved the true/false recognition of targets via multi-classification. Five types of target highlight models were used to generate simulation data to complete the training, validation and testing of the network. Tests were performed using experimental data. The results indicate that the proposed method achieves 92% accuracy in real target recognition and 94% accuracy in two-dimensional disturbance target recognition. This study provides a new approach for underwater target recognition using active sonar.

Traditional methods of orthogonal basis function decomposition have been extensively used to detect magnetic anomaly signals. However, the determination of the relative velocity between the detection platform and the magnetic target remains elusive in practical detection. And, the non-ideal uniform motion of the magnetic target further complicates the process and adversely impacts the detector’s performance. To address this challenge, this paper introduces an adaptive scale factor method based on orthogonal basis function decomposition. This new method can be used to adjust the relative velocity between the detection platform and the magnetic target and to refine the characteristic time in the basis functions. In this paper, a mathematical relationship between the scale factor and the relative velocity is established, which is subsequently fitted into a Gauss function curve. The optimal scale factor, denoted as β, is adaptively chosen from the fitting curve when the magnetic target moves at a non-ideal uniform velocity with an unknown motion state. The results of simulations indicate that the scale factor improves the signal-to-noise ratio of the magnetic anomaly signals in a non-ideal state. And, this method can improve the energy value of OBF decomposition by 17.7%. Simultaneously, this method ensures that the moment the magnetic target passes the CPA coincides with the energy peak of the orthogonal basis detection, which improves the accuracy by 54.1% compared with the traditional method. The effectiveness and precision of the proposed method are verified using simulations and practical experiments.