Multi-Innovation Gradient Iterative Locally Weighted Learning Identification for A Nonlinear Ship Maneuvering System

This paper explores a highly accurate identification modeling approach for the ship maneuvering motion with fullscale trial. A multi-innovation gradient iterative(MIGI) approach is proposed to optimize the distance metric of locally weighted learning(LWL), and a novel non-parametric modeling technique is developed for a nonlinear ship maneuvering system. This proposed method’s advantages are as follows: first, it can avoid the unmodeled dynamics and multicollinearity inherent to the conventional parametric model; second, it eliminates the over-learning or underlearning and obtains the optimal distance metric; and third, the MIGI is not sensitive to the initial parameter value and requires less time during the training phase. These advantages result in a highly accurate mathematical modeling technique that can be conveniently implemented in applications. To verify the characteristics of this mathematical model, two examples are used as the model platforms to study the ship maneuvering.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

Multiple model PHD filter for tracking sharply maneuvering targets using recursive RANSAC based adaptive birth estimation

An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles.The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper,we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.

Cooperative guidance law based on super-twisting observer for target maneuvering

To solve the problem that multiple missiles should simultaneously attack unmeasurable maneuvering targets,a guidance law with temporal consistency constraint based on the super-twisting observer is proposed.Firstly,the relative motion equations between multiple missiles and targets are established,and the topological model among multiple agents is considered.Secondly,based on the temporal consistency constraint,a cooperative guidance law for simultaneous arrival with finite-time convergence is derived.Finally,the unknown target maneuver-ing is regarded as bounded interference.Based on the second-order sliding mode theory,a super-twisting sliding mode observer is devised to observe and track the bounded interfer-ence,and the stability of the observer is proved.Compared with the existing research,this approach only needs to obtain the sliding mode variable which simplifies the design process.The simulation results show that the designed cooperative guidance law for maneuvering targets achieves the expected effect.It ensures successful cooperative attacks,even when confronted with strong maneuvering targets.

Maneuvering Modeling and Simulation of AUV Dynamic Systems with Euler-Rodriguez Quaternion Method

The purpose of this study is to develop maneuvering models and systems of a simulator to improve the motion performance of autonomous underwater vehicles （AUVs） at the preliminary design stages in advance. The AUVs simulation systems based on the standard submarine equations of motion in six-degree-of-freedom （6-DOF） integrated with the Euler-Rodriguez quaternion method for representing singularity-free AUV attitude and time-saving calculation, and with a nonlinear control model for maneuvering and depth control simulations, time-marching in the fourth-order Runge-Kutta scheme. For validation of the simulation codes, results of the ISiMI AUV open-loop tests including turning test and zigzag test as well as an AUV simulator on the basis of Euler-angle method were used to compare with the quaternion-based AUV simulator. The computational results from the proposed simulator agree well with those from both the ISiMI AUV experiments and the Euler-angle based simulations. Additionally, a new maneuvering procedure, namely ＂put-out＂ was implemented to test directional stability for a large-scale AUV in the proposed AUV simulator that can be considered for vehicles in space as well as in constrained planes.

A Maneuvering Target Tracking Filter in Mixed Coordinate System

A kalman filter in the spherical-rectangular coordinate system to track a maneuvering target is introduced. A dynamic model with extended state vector is proposed in this paper to improve the state estimation (i.e. position, velocity and acceleration) even the sensor data(i.e. range, azimuth angle and elevation angle ) is color contaminated. The Kalman filter equations are decoupled by proper coordinate transformation and using filter gain rotation algorithm. Monto Carlo simulation is performed for different kinds of target trajectories(with the same measurement noise) and the root mean square values of estimation errors are computed. Results show that there is significant improvement in tracking capability over the methods discussed by other researchers.

Optimal maneuvering strategy of spacecraft evasion based on angles-only measurement and observability analysis

Spacecraft orbit evasion is an effective method to ensure space safety. In the spacecraft’s orbital plane, the space non-cooperate target with autonomous approaching to the spacecraft may have a dangerous rendezvous. To deal with this problem, an optimal maneuvering strategy based on the relative navigation observability degree is proposed with angles-only measurements. A maneuver evasion relative navigation model in the spacecraft’s orbital plane is constructed and the observability measurement criteria with process noise and measurement noise are defined based on the posterior Cramer-Rao lower bound. Further, the optimal maneuver evasion strategy in spacecraft’s orbital plane based on the observability is proposed. The strategy provides a new idea for spacecraft to evade safety threats autonomously. Compared with the spacecraft evasion problem based on the absolute navigation, more accurate evasion results can be obtained. The simulation indicates that this optimal strategy can weaken the system’s observability and reduce the state estimation accuracy of the non-cooperative target, making it impossible for the non-cooperative target to accurately approach the spacecraft.

Situational continuity-based air combat autonomous maneuvering decision-making

In order to improve the performance of UAV's autonomous maneuvering decision-making,this paper proposes a decision-making method based on situational continuity.The algorithm in this paper designs a situation evaluation function with strong guidance,then trains the Long Short-Term Memory(LSTM)under the framework of Deep Q Network(DQN)for air combat maneuvering decision-making.Considering the continuity between adjacent situations,the method takes multiple consecutive situations as one input of the neural network.To reflect the difference between adjacent situations,the method takes the difference of situation evaluation value as the reward of reinforcement learning.In different scenarios,the algorithm proposed in this paper is compared with the algorithm based on the Fully Neural Network(FNN)and the algorithm based on statistical principles respectively.The results show that,compared with the FNN algorithm,the algorithm proposed in this paper is more accurate and forwardlooking.Compared with the algorithm based on the statistical principles,the decision-making of the algorithm proposed in this paper is more efficient and its real-time performance is better.

An Effective Multiple Model Least Squares Method in Tracking of a Maneuvering Target

A polynomial model, time origin shifting model(TOSM, is used to describe the trajectory of a moving target .Based on TOSM, a recursive laeast squares(RLS) algorithm with varied forgetting factor is derived for tracking of a non-maneuvering target. In order to apply this algorithm to maneuvering targets tracking ,a tracking signal is performed on-line to determine what kind of TOSm will be in effect to track a target with different dynamics. An effective multiple model least squares filtering and forecasting method dadpted to real tracking of a maneuvering target is formulated. The algorithm is computationally more effcient than Kalman filter and the percentage improvement from simulations show both of them are considerably alike to some extent.

Fish maneuver analysis system based on sequence images

The aim is to establish an automatic system to analyze the maneuver performance of fish. A high speed camera （1 000 frame/s） is employed to record fast-start maneuver. Three steps are taken to analyze the kinematics： first, the midline in the first image is partitioned into equal interval lengths and the coordinates of all inter segmental points are saved. Secondly, these points coordinates are searched in the next frame with the digital image correlation （DIC） method, then these points are fitted with a spline curve function. Repeat this step until all the midlines are figured out frame by frame. Finally, according to the variety of midlines, the kinematics of the fast-start is calculated. Using this system to test carp C-start, the duration is divided into two stages： stage 1 is defined as the formation of the C shape and stage 2 as the return flip of the tail followed with forward motion. By tracing the middle line, the kinematic parameters of turning rate, centre of mass （CM） turning rate, CM turning radius, etc. are obtained.

Transient state analysis of a rub-impact rotor system during maneuvering flight

Maneuvering flight substantially affects the dynamic behavior of rotors;particularly,such flight may cause rubbing between a rotor and stator,which is one of the most serious damages in aircraft engines.In this paper,a nonlinear dynamic model for describing the dynamic characteristics of a rub-impact rotor system during maneuvering flight is established based on the Lagrange equations.Subsequently,numerical simulations employing the Newmark method are performed,delving into the detailed discussion of the influence of parameters such as rotational speed and maneuvering flight on the transient and steady-state responses of the rotor system.The effect mechanism of maneuver load and its coupling with rub impact is revealed.The results show that the impact response induced by maneuvering flight is more obvious in the subcritical state than in the supercritical state.The additional stiffness and damping are also induced;in particular,the additional damping has a coupling effect.Moreover,the rub impact imposes an additional constraint on the rotor system,thereby weakening the influence of the maneuver load and becoming the major factor that determines the dynamic behavior of the rotor system at high speeds.

Design of integral sliding mode guidance law based on disturbance observer

With the increasing precision of guidance,the impact of autopilot dynamic characteristics and target maneuvering abilities on precision guidance is becoming more and more significant.In order to reduce or even eliminate the autopilot dynamic operation and the target maneuvering influence,this paper suggests a guidance system model involving a novel integral sliding mode guidance law(ISMGL).The method utilizes the dynamic characteristics and the impact angle,combined with a sliding mode surface scheme that includes the desired line-ofsight angle,line-of-sight angular rate,and second-order differential of the angular line-of-sight.At the same time,the evaluation scenario considere the target maneuvering in the system as the external disturbance,and the non-homogeneous disturbance observer estimate the target maneuvering as a compensation of the guidance command.The proposed system’s stability is proven based on the Lyapunov stability criterion.The simulations reveale that ISMGL effectively intercepted large maneuvering targets and present a smaller miss-distance compared with traditional linear sliding mode guidance laws and trajectory shaping guidance laws.Furthermore,ISMGL has a more accurate impact angle and fast convergence speed.

Multi-UAV cooperative maneuver decision-making for pursuitevasion using improved MADRL

Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning(MADRL) is proposed. In this method, an improved Comm Net network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit(GRU) is added to the actor-network structure to remember historical environmental states. Subsequently,another GRU is designed as a communication channel in the Comm Net core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.

An air combat maneuver pattern extraction based on time series segmentation and clustering analysis

Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.

Research on Maneuver Decision-Making of Multi-Agent Adversarial Game in a Random Interference Environment

The strategy evolution process of game players is highly uncertain due to random emergent situations and other external disturbances.This paper investigates the issue of strategy interaction and behavioral decision-making among game players in simulated confrontation scenarios within a random interference environment.It considers the possible risks that random disturbances may pose to the autonomous decision-making of game players,as well as the impact of participants’manipulative behaviors on the state changes of the players.A nonlinear mathematical model is established to describe the strategy decision-making process of the participants in this scenario.Subsequently,the strategy selection interaction relationship,strategy evolution stability,and dynamic decision-making process of the game players are investigated and verified by simulation experiments.The results show that maneuver-related parameters and random environmental interference factors have different effects on the selection and evolutionary speed of the agent’s strategies.Especially in a highly uncertain environment,even small information asymmetry or miscalculation may have a significant impact on decision-making.This also confirms the feasibility and effectiveness of the method proposed in the paper,which can better explain the behavioral decision-making process of the agent in the interaction process.This study provides feasibility analysis ideas and theoretical references for improving multi-agent interactive decision-making and the interpretability of the game system model.

WSN Mobile Target Tracking Based on Improved Snake-Extended Kalman Filtering Algorithm

A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.

Maneuvering target tracking using threshold interacting multiple model algorithm

To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model （TIMM） is proposed. This algorithm is based on the interacting multiple model （IMM） method and applies a threshold controller to improve tracking accuracy. It is also applicable to other advanced algorithms of IMM. In this research, we also compare the position and velocity root mean square （RMS） errors of TIMM and IMM algorithms with two different examples. Simulation results show that the TIMM algorithm is superior to the traditional IMM alzorithm in estimation accuracy.

Enhancing Safety in Autonomous Vehicle Navigation:An Optimized Path Planning Approach Leveraging Model Predictive Control

This paper explores the application of Model Predictive Control(MPC)to enhance safety and efficiency in autonomous vehicle(AV)navigation through optimized path planning.The evolution of AV technology has progressed rapidly,moving from basic driver-assistance systems(Level 1)to fully autonomous capabilities(Level 5).Central to this advancement are two key functionalities:Lane-Change Maneuvers(LCM)and Adaptive Cruise Control(ACC).In this study,a detailed simulation environment is created to replicate the road network between Nantun andWuri on National Freeway No.1 in Taiwan.The MPC controller is deployed to optimize vehicle trajectories,ensuring safe and efficient navigation.Simulated onboard sensors,including vehicle cameras and millimeterwave radar,are used to detect and respond to dynamic changes in the surrounding environment,enabling real-time decision-making for LCM and ACC.The simulation resultshighlight the superiority of the MPC-based approach in maintaining safe distances,executing controlled lane changes,and optimizing fuel efficiency.Specifically,the MPC controller effectively manages collision avoidance,reduces travel time,and contributes to smoother traffic flow compared to traditional path planning methods.These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios.Future research will focus on validating these results through real-world testing,addressing computational challenges for real-time implementation,and exploring the adaptability of MPC under various environmental conditions.This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation,paving the way for broader adoption of MPC in AV systems.

Hemihepatic versus total hepatic inflow occlusion during hepatectomy:A systematic review and meta-analysis

AIM： To evaluate the clinical outcomes of patients undergoing hepatectomy with hemihepatic vascular occlusion （HHO） compared with total hepatic inflow occlusion （THO）. METHODS： Randomized controlled trials （RCTs） co- mparing hemihepatic vascular occlusion and total he- patic inflow occlusion were included by a systematic literature search. Two authors independently assessed the trials for inclusion and extracted the data. A meta- analysis was conducted to estimate blood loss, transfu- sion requirement, and liver injury based on the levels of aspartate aminotransferase （AST） and alanine arni- notransferase （ALT）. Either the fixed effects model or random effects model was used. RESULTS- Four RCTs including 338 patients met the predefined inclusion criteria. A total of 167 patients were treated with THO and 171 with HHO. Metaanalysis of AST levels on postoperative day 1 indicated higher levels in the THO group with weighted mean dif- ference （WMD） 342.27; 95% confidence intervals （CI） 217.28-467.26; P = 0.00001; I2 = 16%. Meta-analysis showed no significant difference between THO group and HHO group on blood loss, transfusion requirement, mortality, morbidity, operating time, ischemic duration, hospital stay, ALT levels on postoperative day 1, 3 and 7 and AST levels on postoperative day 3 and 7. CONCLUSION： Hemihepatic vascular occlusion does not offer satisfying benefit to the patients undergoing hepatic resection. However, they have less liver injury after liver resections.

CFD Investigations of Ship Maneuvering in Waves Using naoe-FOAM-SJTU Solver

Ship maneuvering in waves includes the performance of ship resistance, seakeeping, propulsion, and maneuverability. It is a complex hydrodynamic problem with the interaction of many factors. With the purpose of directly predicting the behavior of ship maneuvering in waves, a CFD solver named naoe-FOAM-SJTU is developed by the Computational Marine Hydrodynamics Lab(CMHL) in Shanghai Jiao Tong University. The solver is based on open source platform OpenFOAM and has introduced dynamic overset grid technology to handle complex ship hull-propeller-rudder motion system. Maneuvering control module based on feedback control mechanism is also developed to accurately simulate corresponding motion behavior of free running ship maneuver. Inlet boundary wavemaker and relaxation zone technique is used to generate desired waves. Based on the developed modules, unsteady Reynolds-averaged Navier-Stokes(RANS) computations are carried out for several validation cases of free running ship maneuver in waves including zigzag, turning circle, and course keeping maneuvers. The simulation results are compared with available benchmark data. Ship motions, trajectories, and other maneuvering parameters are consistent with available experimental data, which indicate that the present solver can be suitable and reliable in predicting the performance of ship maneuvering in waves. Flow visualizations, such as free surface elevation, wake flow, vortical structures, are presented to explain the hydrodynamic performance of ship maneuvering in waves. Large flow separation can be observed around propellers and rudders. It is concluded that RANS approach is not accurate enough for predicting ship maneuvering in waves with large flow separations and detached eddy simulation(DES) or large eddy simulation(LES) computations are required to improve the prediction accuracy.