水下无人系统学报

Numerical Analysis of the Effect of Stern Flap on the Hydrodynamic Performance of Amphibious Vehicles

ZHANG Guoqing, FENG Yikun, JIN Haobin, GE Qiqian, XU Xiaojun

, Available online , doi: 10.11993/j.issn.2096-3920.2025-0126

[Abstract](0) [FullText HTML] (0) [PDF 1846KB](0)

Abstract:
To explore the effect mechanism of stern flap on the hydrodynamic performance of amphibious vehicles, a combination of towing tests and numerical simulation methods was adopted to comparatively analyze the motion parameters, waveforms and pressure distribution of the vehicle at different speeds before and after the installation of stern flap base on STAR-CCM+. The results show that stern flap significantly alters the hydrodynamic characteristics of the vehicle. In terms of motion parameters, its introduction leads to a trend of first decreasing and then increasing in resistance, with a resistance reduction rate of 21.6% at Fr = 0.738. The regulation effect on sailing attitude is prominent, with a peak difference in pitch angle reaching 63.3% (Fr = 0.738), and it effectively suppresses the heave within the speed range. The stern flap significantly reconstructs the waveform of flow field around the amphibious vehicle and the pressure distribution characteristics of vehicle by changing the pitch angle and heave amplitude, and its effect is speed-dependent.

Phase Compensation Self-interference Cancellation Method for Full-duplex Single-carrier Underwater Acoustic Communication

WU Songwen, LU Yinheng, ZHOU Feng, QING Xin, LI Yanlong, ZHAO Zichen

, Available online , doi: 10.11993/j.issn.2096-3920.2025-0157

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Abstract:
Aiming at the problem that the self-interference cancellation performance is degraded due to the phase mismatch between the transmitting and receiving samples in the in-band full-duplex underwater acoustic communication, this paper proposes a method to improve the self-interference cancellation through two-stage phase compensation in single-carrier communication. The traditional method assumes that the phase of the reference signal is consistent with the phase of the received signal, and directly cancels under this premise. In contrast, this paper introduces phase compensation into the construction process of the reference signal, and takes the minimum residual energy as the optimization goal. This method first estimates the initial phase based on the correlation between the reference signal and the received signal, and further searches in the field of this result to find the optimal compensation phase and compensate, and combines the adaptive filtering algorithm to improve the self-interference cancellation ability. The effectiveness of the proposed method is verified by the simulation of single frequency signal and QPSK signal, the pool experiments and sea experiments. The results show that after phase compensation, the self-interference cancellation performance of the system is improved, the self-interference cancellation performance in the pool experiment is improved by 5.289 dB, and the self-interference cancellation performance in the sea trial experiment is improved by 1.986 dB. After compensation, the main side lobe ratio of the correlation peak of the far-end signal demodulation is optimized. The phase compensation method proposed in this paper can effectively improve the self-interference cancellation performance and filter convergence speed, thereby improving the accuracy of system demodulation, and providing key technical support for the practical application of full-duplex underwater acoustic communication.

A Fault-Tolerant Navigation Algorithm for AUV Based on Collaborative Fault Detection and Robust Estimation

XIAO Ruibin, MA Tiefeng, HU Youfeng

, Available online , doi: 10.11993/j.issn.2096-3920.2025-0156

[Abstract](4) [FullText HTML] (1) [PDF 2309KB](1)

Abstract:
When facing progressive faults in the Doppler velocity log (DVL), traditional adaptive filters fail to provide effective fault tolerance in autonomous underwater vehicle(AUV) integrated navigation systems due to conflicts between noise estimation and fault detection. To address this issue, this paper proposes a collaborative fault-tolerant navigation method that integrates long short-term memory (LSTM) networks for fault detection with variational bayesian adaptive Kalman filter (VBAKF) and IGG-III robust filtering. The proposed approach utilizes LSTM networks to effectively identify early characteristics of progressive faults. Upon fault confirmation, the filter switches from VBAKF to IGG-III robust filtering mode, dynamically adjusting weights to mitigate fault impact. Normal operation resumes using VBAKF after fault resolution. Experimental results demonstrate that, in the event of DVL progressive faults, the proposed method achieves higher navigation accuracy than several mainstream filtering algorithms, effectively suppresses state estimation distortion, and enhances the precision and robustness of AUV integrated navigation systems in uncertain underwater environments.

Research on Optimization of Acoustic Deception Countermeasure Based on Adaptive Mutation Particle Swarm

XIA Zhijun, REN Yunchong, HAN Yunfeng, JIANG Lei

, Available online , doi: 10.11993/j.issn.2096-3920.2025-0081

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Abstract:
In view of the lack of research on the cooperative combat system of multiple acoustic decoys in the decision-making system for surface ships to defend against underwater guidance device, as well as the problems of low efficiency and poor portability in the traditional exhaustive method, this paper introduces the particle swarm optimization algorithm to optimize the countermeasure model and improves the particle swarm algorithm by introducing adaptive inertia weight and multi-radius mutation mechanism. A multi-objective optimization function composed of defense success rate, minimum engagement distance and ship survival time is established. The optimization parameters include the ship's evasive course, the launch distance and angle of the first flying acoustic decoy, and the launch distance and angle of the second flying acoustic decoy. The simulation results show that the proposed improved particle swarm algorithm has higher efficiency, faster convergence speed and higher fitness value compared with the traditional algorithm. Through simulation, the differences in the optimal countermeasure strategies under different bearing angles and their tactical significance are revealed, which has important reference value for the formulation of defense strategies against underwater guidance device in real naval battles.

Unmanned Aerial Vehicle Aeromagnetic Positioning Method for Nearshore Submarine Cables Based on Power Frequency Magnetic Characteristics

SUN Yunkun, LI You, CAO Xiangdong, CHEN Mei, ZHANG Lei, LI Minyue, ZHAO Jie, HAN Qi

, Available online , doi: 10.11993/j.issn.2096-3920.2025-0083

[Abstract](10) [FullText HTML] (4) [PDF 6578KB](1)

Abstract:
Aiming at the technical bottleneck of high-precision detection and positioning of the "last mile" of nearshore submarine cables, this study proposes a comprehensive unmanned aerial vehicle(UAV) aeromagnetic detection method integrating the analysis of power frequency magnetic field characteristics. Firstly, a forward model of the power frequency magnetic field of submarine cables is established, and the propagation and attenuation law of the power frequency magnetic characteristic signal of the cables is revealed through numerical simulation. Secondly, an innovative frequency-domain signal extraction algorithm based on power frequency magnetic characteristics is constructed, effectively improving the recognition accuracy of weak magnetic signals in the background of strong environmental noise. Then, a reverse analytical positioning method combined with the geomagnetic direction is proposed to achieve the meter-level spatial inversion of the direction of submarine cables. The experiment adopted the self-developed rotorcraft ultra-low-altitude (flight altitude of 1 meter) magnetic measurement unmanned aerial vehicle system to conduct actual measurement and verification in the coastal waters of Wenzhou. The results show that the system conducts aerial magnetic detection operations of power frequency magnetic characteristics under the complex terrain conditions of the intertidal zone. Through comparative analysis, it is found that the power frequency characteristic positioning method has significant advantages over the conventional magnetic anomaly positioning method in the nearshore shallow water area. Its positioning error does not exceed 4 meters, and it can accurately track the cable burial path. This research provides a new technical paradigm for the inspection and positioning of submarine cable projects.

A Double-Layer Autonomous Decision-Making Method Based on Expert Knowledge and Deep Reinforcement Learning

Xiao Wenwen, Cai Qianya, Mao Lifu, Lin Yuan, Zhao Yuan, WANG Mianjin

, Available online , doi: 10.11993/j.issn.2096-3920.2025-0098

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Abstract:
Due to the complex and dynamic underwater environment, underwater unmanned systems face challenges of unpredictability and incomplete perception, which makes it difficult for them to accurately and efficiently accomplish autonomous decision - making tasks. Traditional methods highly rely on complete perception data and map information. However, limited by the dynamic characteristics of the underwater environment, it is difficult to construct effective map information in real - time, thus leading to limited efficiency of underwater unmanned systems in executing tasks such as underwater detection, resource exploration, and environmental monitoring. To address the above challenges, this paper proposes a double-layer decision-making method based on expert knowledge and deep reinforcement learning. This method can effectively enhance the adaptive ability of unmanned systems in underwater intelligent decision-making and significantly improve the efficiency of task execution. Specifically, an autonomous decision-making strategy generation method is first proposed to enhance the adaptive ability of underwater unmanned systems in unknown scenarios, further strengthening their autonomous decision-making level in complex environments. Secondly, a double-layer autonomous decision-making method is put forward. By enhancing the robustness of the system, it effectively ensures navigation safety. Finally, a multi - module design method is proposed to achieve the decoupling of each functional module, effectively improving the research and development efficiency of underwater unmanned systems. Taking the unmanned underwater vehicle (UUV) as the research object, experimental results show that the success rate and the convergence speed of the average reward value of the method in this paper outperform various benchmark methods in the simulation scenarios of UUV autonomous navigation and obstacle avoidance, providing a solid theoretical support for autonomous decision - making in real-world scenarios.

Analysis of the Impact of Shock Waves on the Safe Exit of the Rocket-assisted Vehicle Nose Cap during the Thermal Emission Process of a Concentric Canister Launcher

LIU Gangqi, YUAN Xin, GAO Shan, CUI Canli, YE Jianhong

, Available online , doi: 10.11993/j.issn.2096-3920.2024-0156

[Abstract](119) [FullText HTML] (59) [PDF 1529KB](10)

Abstract:
In response to the impact of shock waves on the safety of the vehicle nose cap during the concentric tube thermal launch process, computational fluid dynamics (CFD) software was used to numerically simulate the ignition and launch process. The propagation process of shock waves and gas generated by solid rocket motors in the concentric tube was analyzed in detail, and the force variation curve of the nose cap under the action of shock waves was obtained, revealing the force mechanism of the nose cap inside the tube under the action of shock waves. The test data illustrates the force variation process of the nose cap in the shock wave environment. The research results contribute to a clear understanding of the mechanism of force changes on the nose cap under the shock wave during the thermal emission process of concentric cylinders, and can be used to guide the safety design of the nose cap exiting the cylinder.

Research on Comprehensive Detection Methods for Weak Signals in Underwater Target Shaft Frequency Electric Fields

YU Pingyang, WANG Honglei, YANG Yixin

, Available online , doi: 10.11993/j.issn.2096-3920.2025-0079

[Abstract](56) [FullText HTML] (52) [PDF 1094KB](57)

Abstract:
To address the issue of weak target signals that are easily masked by noise in the detection of ship shaft frequency electric field signals, this paper proposes an electric field signal detection method based on the principle of ‘priority detection and selective enhancement.’ First, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is combined with narrowband power spectrum energy peak entropy ratio (EPER) features. Then, sliding window and dynamic threshold techniques are used to detect the target signal. After successful detection, the proposed method triggers a tri-stable stochastic resonance and variable step-size least mean P-norm (VSS-LMP) enhancement mechanism to further enhance the spectral characteristics of the target signal, thereby enabling the extraction of the target signal's characteristic frequency. Simulation results show that the proposed method achieves a detection accuracy rate exceeding 85% under a signal-to-noise ratio of -12 dB, with a false detection rate below 30%, and can accurately extract the target signal's characteristic frequency, providing a feasible technical approach for real-time monitoring of weak electromagnetic field signals from ships.

Ship Radiated Noise Recognition Based on Dual Low-Rank Adaptation Training

MA Zhixun, TANG Ning, LI Xuan, HAO Chengpeng

, Available online , doi: 10.11993/j.issn.2096-3920.2025-0114

[Abstract](78) [FullText HTML] (37) [PDF 1844KB](3)

Abstract:
To address the limited generalization capability of deep learning models in ship-radiated noise recognition, this paper proposes a dual low-rank transfer learning framework that simultaneously optimizes both model weights and feature representations. Specifically, in the weight space, the pretrained weights are frozen, and a lightweight low-rank weight adjustment(WLoRA) module is introduced to construct learnable low-rank increments. This strategy enables efficient fine-tuning with significantly fewer trainable parameters, thereby mitigating the risk of overfitting. In the feature space, considering the inherent low-rank properties of Mel spectrograms derived from ship-radiated noise, a low-rank feature adjustment(FLoRA) module is designed to compress and reconstruct the extracted features. This explicit low-rank constraint encourages the model to learn compact and discriminative representations that better capture the essential structures of ship-radiated noise. By jointly exploiting low-rank optimization in both the weight and feature dimensions, the framework maximizes the potential of pretrained models and improves transfer learning performance. The experimental results on two publicly available underwater acoustic datasets, ShipsEar and Deepship, demonstrate that the proposed method significantly enhances the performance of transfer learning in the classification model of ship-radiated noise compared to direct fine-tuning of pre-trained models. Furthermore, ablation studies validate the effectiveness of the two low-rank modules.

Optimized Smith Predictor Combined with HCOPSO Algorithm for Unman Surface Vehicle Heading Control

LI Zhiqi, LIU Lanjun, CHEN Jialin

, Available online , doi: 10.11993/j.issn.2096-3920.2025-0104

[Abstract](82) [FullText HTML] (38) [PDF 1857KB](7)

Abstract:
In the heading control of high-speed unmanned surface vessel(USV), the presence of time delay elements in both the forward channel and feedback loop significantly degrades the system's overall performance. Moreover, a larger delay to dynamic time ratio further exacerbates the control difficulty. Conventional Smith predictors can only effectively compensate for time delays in the forward channel and are ineffective against time delays in the feedback loop. In this paper, the time delay in the feedback loop is incorporated into the design of the Smith predictor, constructing a predictive model that accounts for time delays in both directions. This approach allows for simultaneous compensation of time delays in both the forward and feedback paths, thereby significantly reducing the erosion of the system's phase margin caused by bidirectional time delays. Furthermore, a hybrid mean center opposition based learning particle swarm optimization (HCOPSO) algorithm is introduced for the parameter tuning of the PID controller. This algorithm employs a mean center opposition - based learning strategy in the early stages of iteration to expand the search range and utilizes an adaptive compression factor in the later stages for fine-tuning. Thus, it combines the advantages of both global exploration and local exploitation. Simulation results based on a USV heading model demonstrate that the improved Smith predictor PID controller shows significant improvements in system overshoot and settling time compared to conventional PID controllers and traditional Smith predictor PID controllers, with a steady-state error of less than 0.1°. When the compensation model of the optimized Smith predictor contains parameter deviations, the system can still maintain good dynamic stability and steady-state accuracy. Additionally, when comparing the HCOPSO algorithm with other algorithms such as PSO, GA, and WOA for parameter optimization of the improved Smith predictor PID controller, the HCOPSO algorithm achieves an ITAE index that is respectively 55.38%, 22.47%, and 24.63% lower than those obtained by PSO, GA, and WOA, and it exhibits stronger disturbance suppression capability and faster heading recovery performance under different disturbance scenarios, which further verifies the effectiveness of the proposed method.

Research on the Extraction and Recognition of Space-Time-Frequency Features for Underwater Moving Targets

LIU Xiaochun, YANG Yunchuan, HU Youfeng, WANG Chenyu, LI Yongsheng

, Available online , doi: 10.11993/j.issn.2096-3920.2025-0067

[Abstract](90) [FullText HTML] (52) [PDF 1231KB](4)

Abstract:
Aiming at the issue of inadequate bearing-angle adaptability in active sonar target recognition, this paper elaborates on the physical mechanism of active sonar target information perception from wave equation theory. Based on generalized multiple signal classification(MUSIC) spatial spectrum estimation, a novel method is proposed for acquiring the pseudo three-dimensional spatial feature of underwater targets by incorporating distance information, thereby effectively enhancing the adaptability of spatial features across different bearing angles. Additionally, research is conducted on methods to enhance Pseudo Wigner-Ville Distribution(PWVD) time-frequency features and extract Doppler frequency shift distribution features of moving targets. By leveraging the complementary advantages of these two algorithms, the bearing-angle adaptability is further improved. To address the challenge of scarce and imbalanced underwater target samples, the concept of meta-learning is integrated to construct a data-level fusion target recognition network that incorporates spatial, time-frequency, and Doppler domain features. The network is trained and tested using simulation and experimental data. The results demonstrate that the fusion features significantly improve the bearing-angle adaptability and anti-interference capability, providing a novel approach for the development of intelligent underwater target recognition technology.

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