The magnetic field analysis and thrust fluctuation optimization of a permanent magnet synchronous linear motor for electromagnetic launch are performed by establishing an analytical model of the magnetic field of a single-sided motor for electromagnetic launch. The magnetic fields of the secondary Halbach permanent magnet array and the primary six-phase winding are analyzed to obtain the air-gap magnetic field distribution by using the vector magnetic potential and boundary conditions, and then the functional relationship between the electromagnetic thrust and the main structural parameters of single-sided motor is established. The pole arc coefficient and thickness of permanent magnet array, and width and thickness of primary winding are selected as optimization variables, and the optimal combination of design variables is found by using genetic algorithm to minimize the ripple of the unilateral electromagnetic thrust of single-sided motor. The analysis shows that the accuracy of magnetic field analysis results is high, and the calculation error of electromagnetic thrust meets the needs of engineering analysis. The average thrust of the optimized single-sided motor is increased by 1.21%, and the peak-to-average force ratio is decreased by 62.688%. The correctness of theoretical derivation and the rationality of the optimization method are verified by comparing the analyzed and simulated results.

Timely and effective identification of aircraft flight patterns is crucial in monitoring task. However, the existing flight pattern recognition methods have limitations in practical applications due to strong subjectivity and single pattern, which limits the flight monitoring capability in complex situations, and in turn leads to imprecise pattern boundary positioning and low recognition accuracy. For this reason, a flight pattern intelligent recognition method based on sensitive boundaries and long flight sequences is proposed for the intelligent recognition of flight states. In order to better explore the spatial relationships of multi-modal flight parameters, an adaptive graph embedding is designed. A denoising depth multi-scale autoencoder is proposed for the flight patterns at different durations, as well as the classification-weighted focal point loss and regression-joint spatio-temporal intersection loss for mitigating model loss. In order to verify the superiority of the proposed method, the real parameters of several civil flights covering 11 flight patterns are collected, and a flight state dataset is constructed by manual labelling. The results show that the proposed model is able to automatically distinguish different flight patterns in consecutive flight sorties and accurately extract the mode boundaries without any pre-processing or post-processing, with an identification accuracy of 99.07%. The intelligent recognition method can effectively improve the recognition accuracy and the flight state recognition of sensitive boundaries.

In order to solve the problem of phase lag in the response of position tracking control applied by nonlinear active disturbance rejection control (NLADRC) technology, the filtering characteristics of nonlinear functions and the phase compensation mechanism are utilized to complete the design of phase modulation compensator, which solves the contradiction between the actual filtering of a tracking differentiator and the phase tracking. And then a phase compensation active disturbance rejection control (PCADRC) is proposed, which is applied to the attitude and trajectory tracking of quadrotor unmanned aerial vehicle (UAV)in flight-control operations. The flight-control performance advantages of PCADRC application are analyzed through the composite trajectory tracking consisting of airborne obstacle avoidance rounded moments and conical spirals, and Thedisturbance rejectionexperiment for UAV trajectory tracking is designed, and the improved phase modulation compensation effect of ADRC is verified. The simulated and experimental results show that, for trajectories with different properties of planar or spatial, flat or steep, PCADRC can improve the accuracy, timeliness, and robustness of attitude tracking under the premise of ensuring the disturbance rejection performance, which can better satisfy the requirements of the robust flight control.

The autonomous underwater vehicle (AUV) magnetic measurement platform can be used for marine geomagnetic field measurement, underwater magnetic target detection and identification, etc. The AUV magnetic measurement platform has broad application prospects. However, at present, the magnetic interference compensation technology of AUV carrier is not mature, which restricts the magnetic measurement accuracy of underwater vehicle. Based on the basic principle of anti-magnetic interference of magnetic measurement platform, a numerical simulation method based on success history-based adaptive differential evolution with linear population size reduction (L-SHADE) algorithm is proposed for the identification of magnetization interference parameters of AUV carrier. A hybrid model of magnetic dipole and ellipsoid of rotation is used to equivalently simulate the magnetic interference of AUV carrier. Multiple groups of magnetic measurement data are obtained through simulated navigation, a magnetic interference parameter identification model is established accordingly, and L-SHADE algorithm is used to solve the problem. The propagation law of the magnetic measuring accuracy of magnetic measurement platform with the errors of magnetic sensor, platform attitude and heading is studied through quantitative analysis of numerical simulation experiments. When the measuring accuracy of magnetic sensor is 10nT, the attitude measuring accuracy is 0.01°, and the course measuring accuracy is 0.1°, the measuring error can be less than 100nT. The anti-jamming test of the designed AUV magnetic measurement platform shows that the maximum relative error of the total geomagnetic field is 1.07%.

In response to the significant reduction in target positioning accuracy caused by severe nonlinear factors affecting UAV electro-optical platforms, an algorithm based on improved mutant firefly algorithm-particle filter (IMFA-PF) is proposed for UAVs to accurately locate ground targets. Firstly, the state equations and measurement equations for target observation from UAV electro-optical platform are established. And then the IMFA-PF algorithm is utilized to estimate the geographic locatio of a target, and the interaction patterns among particles are altered by introducing multiple mutation strategies and an elasticity mechanism, thereby addressing the particle degradation issues caused by severe nonlinear factors and excessive optimization. Finally, the effectiveness of the algorithm is verified through a one-dimensional nonlinear unstable simulation system and actual flight experiments. Experimental results indicate that the proposed algorithm can improve the particle distribution’s resilience to observational nonlinearity and effectively tackle particle degradation issues, showing better robustness and positioning accuracy compared to the existing positioning methods.

For the inaccurate track cost estimation in task allocation for multi-agent systems, a track cost calculation method based on extended rapidly-exploring random tree is proposed to rationally plan the motion trajectories of agents and improve the accuracy of track cost estimation. In order to solve the problem of premature contracting of dominant agents in improved contract net algorithm, an agent bidding transformation mechanism is proposed to make the dominant agents participate in the task bidding for multiple times and achieve the balance of task load between agents in a system. The simulated results show that the proposed track cost calculation method can be used to accurately calculate the trajectory between agent and target, and the trajectory between target and target. The agent bidding transformation mechanism solves the resource waste caused by the premature contracting of dominant agent, and the time of the agents to complete all tasks is reduced by 6.54%. However, when dealing with the dominant agent problem, the new mechanism will increase the bidding rounds of the entire task allocation.

Aiming at the strong nonlinear problem of the interaction between underwater-launched projectile and ice, the key dimensionless parameters affecting the ice-breaking of projectile are derived by similarity theory. The scaled model test is carried out for the high-speed penetration of projectile through the ice layer. Based on the Gaussian fitting function, an ice load prediction formula is proposed. A fluid-solid coupling model of ice-breaking is established. The ice-breaking phenomenon, the ice load and the motion characteristics of projectile are analyzed by changing the nose shape of projectile, the kinetic energy of projectile and the thickness of ice layer. The results show that the volume of projectile nose cavitation decreases during ice-breaking, the volume of shoulder cavitation gradually increases, and the asymmetry of shoulder cavitation increases with the increase in ice thickness. When the initial speed of projectile is 40m/s, the extreme values of the ice loads on projectiles with hemispherical, spherical conical and pointed conical noses are 35700kN, 33200kN and 18600kN, respectively. The speed loss rate of projectile with pointed conical nose is the lowest, and its ice breaking effect is the best. Under the condition that the ice thickness is 180mm and the ejection pressure is 3MPa and 5MPa, respectively, the speeds of projectile after icebreaking are reduced from 13.1m/s and 17.8m/s to 9.5m/s and 13.4m/s. The greater the ice-breaking speed of projectile is, the lower the speed loss rate is, and the greater the kinetic energy loss is.The extreme value of ice load and the speed loss rate of projectile increase with the increase in ice thickness, and the effect of the initial speed of projectile on the load characteristics and motion characteristics weakens with the decrease in ice thickness.

The influence of the free surface on the shape of supercavitation and the hydrodynamic characteristics of underwater vehicle is investigated. The motion process of a supercavitating vehicle near the free surface is numerically simulated using an adaptive mesh method and the volume of fluid method, and the effects of the free surface on the shape of supercavitation, and the lift, resistance and torque of the vehicle are analyzed. The research findings show that the presence of the free surface causes the tail of supercavitation to shift away from the free surface, resulting in the length of supercavitation being significantly shorter than that in an infinite water domain. The velocity of vehicle has a significant impact on its lift near the free surface. When the velocity of vehicle is less than 50m/s, its lift is negative; when the velocity of vehicle exceeds 60m/s, its lift becomes positive. When the vehicle is fully enveloped by the cavitation, only the cavitator head is wetted. A zero-torque point always appears near the front (x=1D, where D is the diameter of the cavitator) of the vehicle. When the interface of supercavitation tail intersects with the vehicle, it causes a change in the position of the zero-torque point. When the attack angle of the vehicle is positive, the torque is less affected by the water depth. However, when the attack angle is negative, the torque is significantly influenced by the water depth.

Remaining useful life (RUL) prediction is crucial for maintaining the reliability and safety of industrial equipment, but the existing RUL prediction methods still face many challenges in processing the high-dimensional sensor data and capturing the temporal degradation patterns. To address the above issues, this paper proposes a RUL prediction method based on bidirectional long short term memory network-variational auto encoder (BLSTMN-VAE) under the constraint of degradation trend smoothing. This method is used for data preprocessing, including data noise reduction, sliding window segmentation, and label correction. Then, a BLSTMN-based VAE type feature extractor is designed to effectively extract the nonlinear relationships and long-distance dependencies in time series data. Finally, a degradation trend smoothing constraint module based on manifold learning is proposed to enhance the robustness and generalization ability of the proposed model through the assumption of local invariance. The proposed RUL prediction method is verified using the aero-engine dataset. The results show that the proposed RUL prediction method outperforms various existing RUL prediction methods, and has lower prediction errors and higher stability.

Study on the reliability importance of multi-state complex system can help analyze and identify the potential technical weaknesses in system design, and is of great significance for achieving the refined management of system state performance. By introducing an improved generating function model and constructing a system-level generating function for specific state reachable threshold, a generalized closed solution system and a high-precision analysis algorithm for reliability importance that can adapt to complex task functional systems with multiple states are proposed. Engineering case verification shows that the proposed algorithm has a high accuracy in distinguishing reliability importance, and is more conducive to achieving the refined management of reliable states in complex system. Meanwhile, the proposed algorithm has low requirements for programmatic computing resources and can avoid the technical bottleneck of state differential calculation in high-dimensional complex system. Its engineering application value is more prominent. The research results can be used for the importance analysis and refined management of other general quality characteristics in multi-state complex system, and have important technical guidance for scientifically allocating support resources and reasonably planning preventive maintenance work.

The traditional detection methods have the disadvantages of inaccurate feature extraction and low detection efficiency when processing the complex and dynamic flight trajectory data with real-time change in data length. An proposed flight trajectory anomaly detection method using the gradient-based optimization of long short-term memory network and support vector data description model based on gradient training algorithm optimization (LSTM-GBSVDD)is proposed. The LSTM network is used to extract the key features of variable-length flight trajectories and convert them into a fixed-length sequence representation. A multidimensional hypersphere classifier is constructed using the SVDD algorithm, which is used to model the normal flight trajectories and identify the potentially abnormal flight trajectories. To further improve model performance, a gradient-based training algorithm (GB) is introduced to jointly train the parameters of LSTM and SVDD, which greatly improves the detection accuracy and computational efficiency. The simulated results show that the proposed flight trajectory anomaly detection method using the gradient-based optimization of long short-term memory network and support vector data description model based on gradient training algorithm optimization (LSTM-GBSVDD) has good effectiveness and superiority in dealing with complex and changeable flight trajectory anomaly detection tasks, and has good application prospects.

The effective detection and tracking of water columns at marine impact points using visible light images is key to automatically check a target at sea. The existing detection and tracking algorithms still have a high false alarm rate and identity switch times (IDs) due to the movement of camera, the adjustment of focal length, and the changes of water columns. To solve the above problems, this paper proposes a detection and tracking algorithm based on dynamic features for water columns at marine impact points. The YOLOv8 target detector is used to detect the static water columns, and a small target detection head is added to the shallow feature map to enhance the model’s ability to detect small water columns. An improved ByteTrack tracker is used to track the water columns, and the tracking offsets caused by camera movement is compensated by combining camera movement and Kalman filtering. And then, a support vector machine is used for comprehensive decision-making to judge the water columns according to the spatiotemporal features of the water columns formation stage. Compared with traditional detection and tracking algorithms, the proposed algorithm is used to improve the three key performance indicators of multiple object tracking accuracy (MOTA), identification F1 (IDF1), and multiple object tracking precision (MOTP) by 7.8%, 5.1%, and 0.9%, respectively, the number of false positives (FP) is reduced by 112 times, and the numbers of IDs and false detections are both reduced to zero. Experimental results show that the proposed algorithm can not only accurately detect and track the water columns but also effectively exclude other interfering factors, thus achieving a significant enhancement in overall performance.

In response to the problems of sonar operator having a heavy mental workload and the inability to ensure long-term effective working status in the process of underwater target recognition, a brain network feature-based underwater target recognition algorithm based on brain-computer interface (BCI) technology is proposed to assist sonar operators in achieving the rapid recognition of underwater targets. In order to enhance the extraction of brain neural activity information by the model and reduce the interference of brain irrelevant dependencies, the Granger causality (GC) and transfer entropy (TE) theories are used to reconstruct a brain network feature extraction algorithm, and a underwater acoustic target classification model is established by the proposed algorithm. A visual-auditory joint stimulation paradigm is designed for environmental simulation, and the experimental data is collected to complete the training and validation of the underwater acoustic target classification model. The analyzed results show that the proposed brain network feature algorithm can better capture the dependency information in neural activity. The validation of the underwater acoustic target classification model based on brain network features is verified by the visual-auditory joint stimulation paradigm, and the final recognition accuracy is over 90%.

Modern warfare highly relies on the carriers such as images to collect intelligence. The images obtained in foggy conditions can interfere with the clear presentation of a battlefield scene and also conceal the important features, thus affecting the acquisition of battlefield information. In view of the common issues, such as color distortion and image detail loss, of current image dehazing algorithms, this paper proposes a multi-scale feature interaction dehazing network (MFI-DehazeNet), which uses a hybrid architecture of convolutional neural network (CNN) and Transformers. The MFI-DehazeNet uses an encoder-decoder structure to achieve a single image dehazing in an end-to-end manner. First, a multi-scale feature interaction module that enables cross-scale fusion of CNN network features is introduced inMFI-DehazeNet. And then the Transformer structure is improved by using a global feature expression module to boost the network’s global expression capability, thus addressing the receptive field limitations of convolutional structures. The output from the encoder, which integrates the two heterogeneous architectures of CNN and Transformer networks, is processed through the feature reconstruction module (i.e., the decoder) to restore and reconstruct dehazed images. Experimental results indicate that MFI-DehazeNet outperforms other algorithms in dehazing both synthetic and real hazy images.

The surface absorptivity of optical elements is the main factor causing the abnormal temperature rise under continuous laser irradiation. Research has found that the surface absorptivity of optical elements is influenced by multiple factors and exhibits nonlinear variations. Therefore, a concept of equivalent surface absorptivity is proposed to characterize the comprehensive absorption performance of optical elements for laser. Firstly, a finite element model of the optical element irradiated by Gaussian continuous laser is established to simulate the temperature rise process of optical elements under laser irradiation, and a laser irradiation effect experimental system is established. The surface absorptivity, surface morphology and temperature rise process of surface center point of optical elements are measured, tested and analyzed, and the correctness of the prtoposed model is verified by the experimental results. Based on the experimental results, the model parameters are adjusted to obtain the equivalent surface absorptivity of the optical element for laser. The research show that the simulated error of equivalent surface absorptivity is smaller and its simulated precision is higher compared with the measured surface absorptivity, The research results provide reference for the state monitoring of optical element and the pollution prevention and control.

The deployment mode of distributed laser decoy jamming system directly determines its decoy effect. An evaluation method is proposed for evaluating the deployment effectiveness of distributed laser decoy jamming system. The proposed method combines analytic scoring method with analytic hierarchy process. Firstly, a hierarchical structure model of deployment effectiveness is established to clarify which functional units affect the deployment effectiveness and which deployment elements are included in the deployment of each functional unit. Then, an analytic scoring model of each deployment element is established. During the specific implementation, the deployment score of each element can be obtained as long as the deployment information of each functional unit and the protected target information are inputted into the scoring model. Next, the weight of each layout element is determined by the analytic hierarchy process. Finally, the deployment effectiveness can be evaluated by multiplying and summing the scores and weights of each deployment element. The analytical scoring method overcomes the influences of subjective factors and knowledge structure level in the common expert scoring method. The proposed evaluation method can be used for the automatic evaluation of the protection effectiveness of distributed laser decoy jamming system.

The path planning and dynamic collision avoidance techniques for aircraft are effective means and key capabilities to prevent collisions caused by flight path conflicts. The three-dimensional collision detection is used for flight conflict judgment, and an aircraft flight corridor conflict detection method based on multi-view geometry is proposed. The flight route information is obtained through the automatic dependent surveillance-broadcast (ADS-B) system, and an aircraft flight corridor model is designed to construct a local situation of potential conflict area. Then, the conflict situation in the aircraft flight corridor is detected based on the geometric relationships in the three views of the local situation. Simulated results show that the proposed method can intuitively and accurately judge the conflict situation in aircraft flight corridor. Compared to the traditional spatial grid analysis method, the efficiency of solving the conflict situation is improved by 66.35%, the efficiency of solving nin-conflict situation is improved by 98.17%, and the overall solving efficiency is increased by 84.38%.

At present, the determination of segmentation scale in the object-oriented seafloor acoustic image classification is empirical and significantly influenced by human factors. A spatially adaptive segmentation scale determination method using the confusion index as an objective index is proposed. The mean value and standard deviation of echo intensity corresponding to the segmentation objects are calculated by giving a set of segmentation scales. The unsupervised K-means clustering algorithm is then adopted to calculate the confusion indexes pf classification results at different segmentation scales, and the segmentation scale corresponding to the minimum confusion index is selected as the optimal scale to extract the seafloor image features. Based on the seafloor image features extracted at the optimal scale, a supervised classification model is established by combining the sampled data to predict the distribution of sediments in the whole surveying area. Experimental results prove that the spatially adaptive segmentation scales can be used to improve the classification accuracy significantly. The effectiveness of the proposed method is verified by cross-check in the experiment. Moreover, for thesegments that are with the relatively consistent the echo intensity characteristics, the classification accuracy can be further improved by introducing the terrain features.

Pressure field caused by ship sailing is an important information in the ocean battlefield. When a ship sails against waves, the pressure fluctuation caused by ship-wave interaction becomes the background interference of ship’s own hydrodynamic pressure field, which affects the accurate prediction and identification of ship target. Therefore, a fast algorithm for the target characterization of pressure field caused by ship sailing against regular waves in the shallow water is studied. The theoretical and numerical methods of pressure field suitable for regular waves environment and full speed of ship are established by using the wave source term method and moving pressure term method on the basis of shallow-water wave theory. Meanwhile, a fast and efficient numerical algorithm is developed, and an algorithmic program is compiled, which can simulate the regular waves in shallow water, the ship hydrodynamic pressure field in static water, and the temporal and spatial variation characteristics of pressure caused by ship sailing against waves. Based on the validation study, the characteristics of pressure field caused before and after a ship encounters with waves as well as the distribution characteristics of pressure at subcritical or supercritical speed are compared and analyzed, and the influence of waves interference on the temporal and spatial variation characteristics of pressure is revealed, which provides the theoretical basis and technical support for the prediction and identification of ship target under the disturbance of waves environment.

To verify the feasibility of equipping electric field sensors on fast motion platforms to detect ship targets, this manuscript analyzes the mechanism of background electric field generation on fast motion platforms, builds a high-speed motion platform electric field detection system based on surface speedboats, and conducts real boat sea measurement experiments. The background electric field of speedboats at different positions, engine operating conditions, and sailing speeds is measured, and the measurement results are analyzed. By analyzing the experimental data at sea, it is found that the background electric field comes from: 1) the motion induced electric field of the speedboat platform, 2) the corrosion and electromagnetic radiation of the speedboat itself. The background electric field has high energy in the frequency band below 1Hz (static electric field), so the ship’s electrostatic field is not suitable as a detection signal source. When the speed is below 20 knots, the background electric field spectral density of the speedboat detection platform in the 1-30Hz frequency band is about 0.4μV/$\sqrt{\mathrm{Hz}}$, so the ship’s shaft frequency electric field can be used as the target signal source. To verify the feasibility of electric field detection on the speedboat platform, electric field targets of 100A·m were detected at different speeds of 5~15kn, with a detection distance of 1500m. Therefore, it is practical to use a fast motion platform equipped with electric field sensors to detect the ship’s electric field.

As a common structure in engineering, axisymmetric structures have attracted widespread attention in the engineering community for their acoustic simulation calculations. A method to comprehensively utilize the equivalent surface sound source and point sound source for predicting the acoustic radiation from axisymmetric structure is proposed. In response to the non-uniqueness of solutions for the traditional equivalent point sound sources at the corresponding Dirichlet characteristic frequency inside the structure and the high sensitivity to acoustic parameters, a surface sound source is arranged inside the structure for the matching operations of external sound field, while the point sound source is located inside the surface sound source to ensure the uniqueness of the solution. Based on the symmetry of the structure, the surface sound pressure and vibration velocity are expanded into Fourier series form according to the rotation angle, and the orthogonality between the series is utilized to establish the expression of various undetermined coefficients. An equation for the relationship between the equivalent source strength and the undetermined coefficients of Fourier series is established based on the principle of wave superposition, and the virtual area integral equation is transformed into a product form of the outer boundary integral and rotation angle integral of axisymmetric structure. The shape function is utilized to interpolate the outer boundary of the structure in order to obtain the transfer function between the surface sound pressure and vibration velocity of axisymmetric structure. The accuracy of the proposed method is demonstrated by comparing the results of the proposed method with those of traditional equivalent point sound source method, surface sound source method, and analytical method.

To analyze the induced electric fields generated by moving magnetic objects and address the limitations of existing models, this paper proposes a novel model that is more suitable for calculating the induced electric fields by the objects moving in any direction. The applicabilities and limitations of current models for induced electric fields from moving magnetic objects are evaluated. On the basis of evaluation, a mathematical model for the induced electric fields from moving magnetic objects is derived by using a vector potential model of magnetic dipole as the theoretical foundation. The proposed model is verified through theoretical analysis and simulating calculations. And it is further demonstrated through the case studies involving the magnetic field characteristics of actual ships. The results show that the proposed model is suitable for calculating the electric fields induced by the magnetic objects moving in any direction and is more concise in derivation and calculation compared to the traditional models based on Coulomb’s law and the Biot-Savart law. Case study results indicate that the induced electric fields generated by the motion of magnetic ships have distinct regional characteristics, with the electric field strength reaching mV/m level, which constitutes a significant component of the ship’s static electric field. The induced electric field model proposed in this study provides a more accurate theoretical basis for the detection of electric fields from moving magnetic objects and can be effectively applied to the detection and analysis of ship electric fields.