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Migration is a widespread strategy for escaping unfavourable conditions during winter, but the extent to which populations that segregate during the breeding season aggregate during the non-breeding season is poorly understood. Low non-breeding season aggregation may be associated with higher likelihood of overlap with threats, but with fewer populations affected, whereas high aggregation may result in a lower probability of exposure to threats, but higher overall severity. We investigated non-breeding distributions and extent of population aggregation in 2 sympatrically breeding auks. We deployed geolocation-immersion loggers on common guillemots Uria aalge and razorbills Alca torda at 11 colonies around the northern UK and tracked their movements across 2 non-breeding seasons (2017-18 and 2018-19). Using 290 guillemot and 135 razorbill tracks, we mapped population distributions of each species and compared population aggregation during key periods of the non-breeding season (post-breeding moult and mid-winter), observing clear interspecific differences. Razorbills were largely distributed in the North Sea, whereas guillemot distributions were spread throughout Scottish coastal waters and the North, Norwegian and Barents Seas. We found high levels of aggregation in razorbills and a strong tendency for colony-specific distributions in guillemots. Therefore, razorbills are predicted to have a lower likelihood of exposure to marine threats, but more severe potential impact due to the larger number of colonies affected. This interspecific difference may result in divergent population trajectories, despite the species sharing protection at their breeding sites. We highlight the importance of taking whole-year distributions into account in spatial planning to adequately protect migratory species.

Real-time high-precision navigation has many applications, such as pedestrian navigation, emergency rescue, and vehicle networks. In practice, the measurement models are often non-linear, and sequential Bayesian filters, such as Kalman and particle filter, suffer from accumulative errors, which cannot provide long-time high-precision services for localization. To solve arbitrary noise distribution, this paper proposes a Gaussian condensation filter algorithm to achieve high-precision localization in a non-Gaussian noise environment. To this end, we proposed an error-ellipse re-sampling-based Gaussian condensation filter (EER-GCF), which establishes error-ellipses with different confidence probabilities and implements a re-sampling algorithm based on the sampling points’ geometrical positions. Furthermore, a cooperative constrained Gaussian condensation filter (CEER-GCF) is proposed to enhance information fusion in the multi-target network. This study accomplishes cooperative tracking based on spatial-temporal constraints to enhance error correction. Experiment results show that CEER-GCF can effectively eliminate the accumulative error and optimize state estimation, which outperforms state-of-the-arts, such as unscented Kalman filter and particle filter.

This article proposes a dynamic model and a tracking control strategy for a dielectric elastomer (DE) actuator based on the model predictive controller (MPC). First, the dynamic model of the DE actuator is established, which can describe its asymmetric hysteresis, creep, and even rate-dependent hysteresis behaviors simultaneously. Then, on the basis of the established dynamic model, an inverse compensation controller (ICC) is designed to compensate the hysteresis and creep nonlinearities of the DE actuator on its tracking control. Moreover, the MPC is designed to cooperate with the ICC, which can overcome the influences of the modeling error and uncertainties on the control precision. Finally, several experiments demonstrate the effectiveness of the proposed dynamic model and control strategy.

This work considers the observability problem in bearings-only tracking (BOT). Although the rank criterion has always been used for observability analysis, the evident drawback that it can only give a qualitative result prevents us from exploiting more information from the observability matrix. In reality, we want to know not only if the system is observable but also the degree of the observability. In order to quantitatively describe the system observability, we propose a series of metrics for BOT. Being derived based on the condition number, the novel metrics can reflect the observability degree as well as evaluate the tracking performance. Besides, the metrics can be extended to be used for sensor trajectory optimization and sensor configuration, which are crucial to enhancing the tracking performance.

This paper addresses the noncooperative attitude tracking control problem for rigid bodies whose dynamics evolves on the rotation matrix (Formula presented.). First, based on the relative attitude measurement, a saturated angular velocity input is designed by a proposed saturated nonlinear function. Thereafter, the attitude tracking control torque for rigid body's dynamics with input saturation constraint and active disturbance rejection is developed by a modified dynamic surface control approach which can avoid large torque in practice. Furthermore, by using only relative attitude measurements, two types of velocity-free attitude control laws are considered, respectively. In contrast to those control schemes that require that the desired angular velocity is available to the rigid body, the proposed scheme is able to solve the attitude control in noncooperative scenarios, where the desired angular velocity and acceleration are not accessible. Since the attitude described by rotation matrix (Formula presented.) enables the controller to use local measurements in the body-fixed frame rather than global measurements in an inertial frame, the proposed robust noncooperative control schemes with input saturation constraint are easier to implement in practice. Finally, numerical simulations and SimMechanics experiments are provided to illustrate the effectiveness of the proposed theoretical results.

This brief studies event-triggered control problem for consensus tracking based only on relative output information. First, with the sporadic relative outputs, an event-triggered observer is designed to estimate the relative states. Second, based on the intermittent estimate, an event-sampled controller is constructed to achieve the consensus tracking. Compared with the existing observer-based protocols that are designed with absolute outputs to reconstruct each agent’s states, the current protocol is born to have two fascinating features. First, the relative states rather than agent’s states are estimated, and meanwhile no observer information is inter-exchanged through network. This structure can not only save communication resources, but also reduce the possibility of encountering cyber attacks. Second, the triggering instants associated with observer and controller are independent, showing that the updates of observer and controller are asynchronous. Numerical simulation illustrates the proposed control approach.

This paper focuses on the problem of adaptive fuzzy consensus tracking control with event-triggered input for a class of fractional-order multi-agent systems (FOMASs), where systems are considered to be with unmeasurable states, mismatched uncertain external disturbances and unknown nonlinearities. An event-triggered mechanism with a decreasing threshold function is proposed in this paper, which mitigates communication burden and avoids large control impulse. To achieve better tracking performance in distributed control strategy, a compensating fractional-order adaptation law associated with consensus tracking error is introduced when agents can not obtain the information of reference trajectory directly. Under the fractional-order stability theory, a novel distributed adaptive event-triggered controller with fuzzy observers is constructed, which ensures the boundedness of tracking errors. A simulation example demonstrates the correctness of the presented scheme.

The standoff tracking control of underwater glider to moving targets has non-negligible potentials in oceanic observations. To perform such operations, this study presents an integrated standoff tracking method that focuses on dealing with the coupled motions and under-actuated characteristics of the gliders as well as offsetting the dilemma caused by lacking of navigation system. The proposed approach contains guidance algorithm, basic controller, and reinforcement learning optimizer to improve the tracking accuracy. The verified simulation model for the OUC-III glider is utilized to develop the algorithm and evaluate the effectiveness of tracking. The adaptive navigation law called Lyapunov guidance vector field method generates the desired heading in this framework. The guidance only requires positioning information that is accessible through dead-reckoning. For the deficiency that the glider’s state controlled by the proportional integral differential controller can not immediately reach the required state, the reinforcement learning optimizer is introduced. The gliding status constitutes optimizer input while the compensating rotation signals are outputted to actuate the movable mass. The reward function of the optimizer is designed for maintaining the glider position error within 1 m with respect to the standoff circle. On this basis, the objective is set to maximize the rewards and consequently to guarantee the best accuracy. Three reinforcement learning algorithms are adopted and tested in our proposed framework. In the training phase, the training speeds of composite methods and pure reinforcement learning algorithms are compared. To test the stability, we design different types of moving targets, standoff radium and disturbance in the validation simulation, which have not appeared in the training. This study can provide patterns and solutions for the standoff tracking applications of underwater gliders. Also, it enlarges the glider’s scope of deployment based on the existing hardware.

Mobile target tracking with artificial intelligence (AI) approaches such as deep reinforcement learning (DRL) in edge-assisted Internet of Things (Edge-IoT) platform can be promising. In this article, we propose DRLTrack , a framework for target tracking with a collaborative DRL called C-DRL in Edge-IoT with the aim to obtain two major objectives: high quality of tracking (QoT) and resource-efficient network performance. In DRLTrack , a huge number of IoT devices are employed to collect data about a target of interest. One or two edge devices in the network coordinate with a group of IoT devices and collaboratively detect the target by using the C-DRL approach and form an area around the target by the group of IoT devices. To maintain such an area during the tracking time, we employ a deep Q-network to track the target from one group to another. An EdgeAI sitting on the top of the edge devices has the control of the C-DRL approach during tracking and can identify a sequence of tracks. DRLTrack is said to be trustworthy as it shows trustworthy performance in terms of QoT, dynamic environments, and even under certain cyberattacks. We validate the performance of DRLTrack considering the objectives through simulations and it demonstrates superior performance compared with existing work.

In this article, two new models, namely granulated RCNN (G-RCNN) and multi-class deep SORT (MCD-SORT), for object detection and tracking, respectively from videos are developed. Object detection has two stages: object localization (region of interest RoI) and classification. G-RCNN is an improved version of the well-known Fast RCNN and Faster RCNN for extracting RoIs by incorporating the unique concept of granulation in a deep convolutional neural network. Granulation with spatio-temporal information enables more accurate extraction of RoIs (object regions) in unsupervised mode. Compared to Fast and Faster RCNNs, G-RCNN uses (i) granules (clusters) formed over the pooling feature map, instead of its all feature values, in defining RoIs, (ii) only the positive RoIs during training, instead of the whole RoI-map, (iii) videos directly as input, rather than static images, and (iv) only the objects in RoIs, instead of the entire feature map, for performing object classification. All these lead to the improvement in real-time detection speed and accuracy. MCD-SORT is an advanced form of the popular Deep SORT. In MCD-SORT, the searching for association of objects with trajectories is restricted only within the same categories. This increases the performance in multi-class tracking. These characteristic features have been demonstrated over 37 videos containing single-class, two-class, and multi-class objects. Superiority of the models over several state-of-the-art methodologies is also established extensively, both qualitatively and quantitatively.

We study the consensus tracking problems of Lur’e multi-agent systems (MASs) with directed switching topology and a leader that is subject to unknown inputs. We firstly use the pure relative outputs to design a distributed unknown input observer (UIO) that can track the relative states exactly. Then we use this UIO to design a discontinuous controller which contains a consensus part and a discontinuous part, where the impacts of the leader’s unknown inputs can be completely removed by the discontinuous part. Furthermore, by using the average dwell time (ADT) method, we show that asymptotical consensus can be achieved if the ADT is larger than a positive threshold. Secondly, we design a continuous controller under which the consensus tracking error is uniformly ultimately bounded (UUB). Finally, we perform a simulation on the multiple Chua’s circuits to validate the theoretical result.

Objective: Catheters and wires are used extensively in cardiac catheterization procedures. Detecting their positions in fluoroscopic X-ray images is important for several clinical applications such as motion compensation and co-registration between 2D and 3D imaging modalities. Detecting the complete length of a catheter or wire object as well as electrode positions on the catheter or wire is a challenging task. Method: In this paper, an automatic detection framework for catheters and wires is developed. It is based on path reconstruction from image tensors, which are eigen direction vectors generated from a multiscale vessel enhancement filter. A catheter or a wire object is detected as the smooth path along those eigen direction vectors. Furthermore, a real-time tracking method based on a template generated from the detection method was developed. Results: The proposed framework was tested on a total of 7,754 X-ray images. Detection errors for catheters and guidewires are 0.56 0.28 mm and 0.68 0.33 mm, respectively. The proposed framework was also tested and validated in two clinical applications. For motion compensation using catheter tracking, the 2D target registration errors (TRE) of 1.8 mm 0.9 mm was achieved. For co-registration between 2D X-ray images and 3D models from MRI images, a TRE of 2.3 0.9 mm was achieved. Conclusion: A novel and fully automatic detection framework and its clinical applications are developed. Significance: The proposed framework can be applied to improve the accuracy of image-guidance systems for cardiac catheterization procedures.

Coordinated taxiing planning for multiple aircraft on flight deck is of vital importance which can dramatically improve the dispatching efficiency. In this paper, first, the coordinated taxiing path planning problem is transformed into a centralized optimal control problem where collision-free conditions and mechanical limits are considered. Since the formulated optimal control problem is of large state space and highly nonlinear, an efficient hierarchical initialization technique based on the Dubins-curve method is proposed. Then, a model predictive controller is designed to track the obtained reference trajectory in the presence of initial state error and external disturbances. Numerical experiments demonstrate that the proposed “offline planning + online tracking” framework can achieve efficient and robust coordinated taxiing planning and tracking even in the presence of initial state error and continuous external disturbances.

The wireless power transfer (WPT) system is widely applied in the underwater environment. A multitransmitter multireceiver WPT system is proposed in this article for the application of the submerged buoy, and numbers of beacons can be charged at the same time to send more data. Both series and parallel connection structures of several coils are analyzed in detail. In order to increase the system efficiency at ultracapacitors charging process, a maximum efficiency tracking with a primary current controller and an optimal resistance controller is studied and designed. All mentioned above analyses are verified in a three-receiver WPT system prototype. When ultracapacitors are charged from 10 to 25 V, the system efficiency is achieved at almost 70.5%, and the charging voltage of all three receivers can be stabled.

This article intends to address an adaptive asymptotic tracking control with prescribed performance function for a class of nonaffine systems with unknown disturbances. First, the nonaffine system is transformed into an affine system by using a set of alternative state variables. Subsequently, a prescribed performance function with predefined convergence rate, maximum overshoot and steady-state error is introduced. To achieve the asymptotic tracking control performance, the robust integral of the sign of the error (RISE) feedback term is utilized in the control design to reject the unknown external disturbances and NN approximation errors. Finally, an adaptive controller is presented so that the asymptotic tracking performance with guaranteed prescribed performance is achieved. Comparative experiments are provided to show the effectiveness of the proposed control scheme.

Linear oscillatory machines are becoming more widely used in refrigeration compressors due to advantages of compact volume, little maintenance, and so on. In order to further increase the efficiency and reduce the cost of the whole system, both resonant frequency tracking control and sensorless control strategies should be implemented simultaneously. In this article, an adaptive full-order observer with simple structure and less computation is proposed, which can not only provide accurate displacement estimation signals for the system piston stroke control, but also achieve resonant frequency tracking control through the estimation of machine parameters. The system stability analysis proves the quick convergence of the proposed observer, and comprehensive simulation and experimental results demonstrate the effectiveness of the proposed algorithm.

This brief focuses on the problem of fixed-time adaptive trajectory tracking control for a quadrotor unmanned aerial vehicle (QUAV) subject to error constraints. By virtue of the fixed-time command filter, the phenomenon of “explosion of complexity" (EOC) existing in the conventional backstepping method is successfully eliminated, meanwhile the effect of filtered error is skillfully removed via a new fractional power error compensation mechanism. By combining the prescribed performance control and backstepping design method together with command filter technique, a fixed-time adaptive control strategy is established. It is proved that all signals of the closed-loop system are fixed-time bounded, and the position and attitude tracking errors of the QUAV approach to an arbitrarily small region of the original point within the predefined performance bounds in a fixed time. Finally, simulation results are given to show the availability of the presented fixed-time control algorithm.

BACKGROUND: Hemophilia is an X-linked inherited disorder primarily affecting males, its major types are type A (deficiency in factor VIII) and B (deficiency in factor IX), and is considered to be the most common severe congenital coagulation factor deficiency. The present study was designed to test whether any differences in left ventricular (LV) rotational mechanics could be demonstrated between male patients with hemophilia and healthy controls using three-dimensional speckle-tracking echocardiography (3DSTE)-derived virtual LV models. METHODS: The present study consisted of 17 patients with hemophilia, however, 3 patients were excluded due to insufficient image quality. In the remaining patient population, 12 patients had hemophilia A and 2 patients had hemophilia B (mean age: 42.2±18.9 years, all males). The control group comprised 16 age-matched healthy subjects (46.0±5.9 years, all males). RESULTS: None of the routine two-dimensional echocardiographic data differ between patients with hemophilia and controls. None of the patients and controls showed ≥ grade 1 valvular regurgitations and had valvular stenoses. In one subject, the near absence of LV twist called as LV “rigid body rotation” could be detected, data of which were managed separately. While 3DSTE-derived apical LV rotation was 3.65 degrees, basal LV rotation proved to be 3.57 degrees leading to 0.08-degree LV apico-basal gradient suggesting counterclockwise LV “rigid body rotation”. In the remaining patients, both LV apical rotation (7.25±6.20 vs. 10.39±4.16 degrees, P

Introduction: An advanced, reliable and fast vehicle detection-and-tracking technique is proposed, implemented and tested. In this paper, an advanced-and-reliable vehicle detectionand- tracking technique is proposed and implemented. The Real-Time Vehicle Detection-and- Tracking (RT_VDT) technique is well suited for Advanced Driving Assistance Systems (ADAS) applications or Self-Driving Cars (SDC). Methods: The Real-Time Vehicle Detection-and-Tracking (RT_VDT) is proposed, and it is mainly a pipeline of reliable computer-vision and machine-learning algorithms that augment each other and take in raw RGB images to produce the required boundary boxes of the vehicles that appear in the front driving space of the car. The main emphasis is the careful fusion of the employed algorithms, where some of them work in parallel to strengthen each other in order to produce a precise and sophisticated real-time output. Results: The RT_VDT is tested and its performance is evaluated using actual road images and videos captured by the front-mounted camera of the car as well as on the KITTI benchmark. The evaluation of the RT_VDT shows that it reliably detects and tracks vehicle boundaries under various conditions. Discussion: Robust real-time vehicle detection and tracking is required for Advanced Driving Assistance Systems (ADAS) applications or Self-Driving Cars (SDC).

This paper proposes a fixed zone perturb & observe (FZPO) technique to achieve an improved steady-state efficiency as well as a fast and drift-free maximum power point tracking (MPPT) for photovoltaic (PV) systems without any additional sensors. In this technique, the PV array’s power-voltage curves are divided into multiple zones with unique zone boundary voltages for different irradiance conditions. This technique employs a combination of adaptive and fixed step-sizes to improve the performance, where the adaptive step-size is calculated from simple mathematical equations, resulting in a reduced computational burden. Besides, natural drift-free tracking is achieved without any additional sampling or computation, overcoming existing techniques’ limitations. The FZPO technique requires a PV panel’s information only at the initial design stage but not during real-time tracking, making the implementation possible using only low-cost processors. This paper presents the implementation and design of the proposed controller. The FZPO technique’s performance is validated through a comprehensive set of hardware experiments on a buck-boost full-bridge converter under various irradiance conditions per the EN50530 standard. For a step-change in irradiance, the FZPO technique is experimentally verified to be 42% and 20% more efficient than the conventional and VSS techniques, respectively. During the irradiance varying conditions using the FZPO technique, the peak power loss is one-sixth compared to the conventional and VSS techniques. Practical field-related considerations such as PV panel temperature effects are further investigated through experiments.

Parallel electrical manipulator systems have become prevalent in attitude simulation in recent years to achieve a high precision attitude tracking. However, the attitude tracking performance is affected by internal uncertainty, external disturbances, and uncoordinated performance of the electric actuators. In this article, a coordinated attitude control strategy that combined the distributed active disturbance rejection control (ADRC) framework with a consensus algorithm is proposed for the parallel electrical manipulators. We indicate that the proposed coordinated attitude control strategy solves the uncoordinated problem of the electric actuators, and compensates the disturbances that include internal uncertainty and external disturbances. Meanwhile, a closed-loop control is achieved with a guaranteed stable tracking performance. The control strategy is applied to a 3-DOF parallel electrical manipulator system, and comparative experimental results indicate that the proposed coordinated ADRC consensus control strategy can achieve a better tracking performance than the strategy with only ADRC.

In this article, we address the trajectory tracking problem using the learning from demonstration (LFD) method. By using the LFD method, the parameter adjusting problem in the tracking controller is avoided. Consequently, a strategy can be provided to users with limited parameter adjusting experience. The kinematic tracking problem is formulated as a second-order system and the objective is to simultaneously reduce the errors in position and velocity. The extreme learning machines (ELM) algorithm is applied in the controller design. The velocity and position are utilized as the inputs and the output is the robot corrected kinematic movement. The controller parameters are learned from the desired human or programming demonstrations taking into consideration the stability constraints. In this work, we analyze the system local and global asymptotic stability in detail. The effectiveness of the proposed strategy is demonstrated by simulation comparisons and a practical experiment using a KUKA robot manipulator.

In the last two decades, object tracking has been one of the prevalent fields in social media. Object tracking uses a dynamic model to track the same target, aiming to analyze the same social object and its behavior in a set of consecutive video frames. In this article, we provide a comprehensive survey on some representative and latest correlation-filter-based object tracking methods and compare their respective strengths and weaknesses under the theoretical and experimental analyses. First of all, we divide these correlation-filter-based object tracking methods into four categories according to their characteristics, including categorized features, space weight factors, scale factors, and expert strategies. Second, extensive experiments on benchmark datasets with 11 tracking challenges are performed and discussed. Finally, this article also attempts to provide some insights for the readers to comprehend and improve the involved tracking algorithms. It is expected that the overview would present a detailed framework and some enlightenments of object tracking to readers who are new in this area.

This paper is devoted to the fuzzy fault-tolerant tracking control of Markov jump systems with unknown mismatched faults. To reconstruct the faults and the system states, a sequence of proportional-integral observers are established via the system outputs. With the help of a structure separation technique, the proportional-integral gains and the observer gains are solved by a unified linear matrix inequality framework. Resorting to the rebuilt faults and states from an iterative estimation algorithm, a backstepping based fuzzy fault-tolerant tracking control scheme against the mismatched faults is established to make the resultant closed-loop system be uniform ultimate boundedness. Simulations are provided to verify the effectiveness of the proposed methods.

This paper aims to discuss fixed-time tracking control problem for a nonholonomic wheeled mobile robot based on visual servoing. At first, by making use of the pinhole camera model, the robot system model with uncalibrated camera parameters is given. Then, the tracking error system between the mobile robot and desired trajectory is proposed. Thirdly, on the basis of fixed-time control theory and Lyapunov stability analysis, fixed-time tracking control laws are proposed for the robot, which can make the robot achieve the desired value in a fixed time. Simulation results are given at the end.

Gantry cranes, which have attracted extensive attention, are mostly simplified as nonlinear single pendulum systems without load hoisting/lowering. However, in fact, due to the existence of the hook, gantry cranes produce double pendulum swing. With an extra underactuated degree of freedom, the anti-swing control of the double pendulum gantry cranes becomes more difficult than that of single pendulum gantry cranes, especially when load hoisting/lowering is considered simultaneously. Moreover, large swings, which lead to problems such as inaccurate positioning and low transportation efficiency, may be caused by double pendulum gantry cranes with load hoisting/lowering. In this paper, a nonlinear coupled tracking anti-swing controller is proposed to solve these problems. In this controller, a smooth tracking trajectory is introduced to ensure the stable start and run of the trolley, and a coupled signal is constructed to eliminate the residual swing angles of gantry crane system. The system stability is analyzed by using Lyapunov method and Barbarat theorem. Theoretical derivation, simulation and experimental results show that the proposed controller has excellent control performance, specifically, not only does it ensure accurate positioning of the load, but also it suppresses and eliminates the hook/load swing angle effectively. The proposed controller can still achieve good control effects and has strong robustness under the condition of changing the load mass, trolley target displacement, system initial swing angles and adding external disturbance.

This paper presents a finite-time command-filtered approximation-free attitude tracking control for rigid spacecraft. A novel finite-time prescribed performance function (FTPPF) is first constructed to ensure that the attitude tracking errors converge to the predefined region in finite time. Then, a finite-time error compensation mechanism is constructed and incorporated into the backstepping control design, such that the differentiation of virtual control signals in recursive steps can be avoided to overcome the singularity issue. Compared with most of approximation-based attitude control methods, less computational burden and lower complexity are guaranteed by the proposed approximation-free control scheme due to the avoidance of using any function approximations. Simulations are given to illustrate the efficiency of the proposed method.

This paper studies the iterative learning control (ILC) algorithm for first-order hyperbolic systems. Unlike most of the ILC literature of distributed parameter systems, in the iteration domain, that require identical desired trajectories. Here the desired trajectories are iteratively varying and described by a high-order internal model (HOIM). The HOIM-based P-type ILC design is firstly introduced in this paper to the first-order hyperbolic systems, which enable the systems to achieve the perfect tracking for the iteration-varying desired trajectories on L 2 space. Meanwhile, the convergence theorem of the proposed algorithm is established for first-order time-delay hyperbolic systems. Finally, simulation results testify the validity of the algorithm.

In this paper, an adaptive tracking control scheme is investigated for a medium scale unmanned autonomous helicopter (UAH) with unknown external disturbances and system uncertainties to achieve improvement on the flight performance. The neural networks (NNs) are employed to compensate the system uncertainties. The second-order disturbance observers are introduced to restrain the compound disturbances which are combined with the NN approximation errors and the external disturbances. Accordingly, the tracking control law is designed for the UAH. The closed-loop stability of the whole UAH system is proved by using Lyapunov function method. Simulation results show that the developed control scheme can effectively solve the tracking control problems of UAH and certainly accomplish strong robustness with respect to the external disturbances and system uncertainties.

This note addresses the design of a robust trajectory tracking controller based on active disturbance rejection for a quadrotor Unmanned Aircraft Vehicle (UAV). A standard state-feedback linearizing controller, designed using a nominal model, is applied for tracking reference signals. An active disturbance rejection technique based on high-order sliding modes compensates for the uncertainties and nonlinearities. Furthermore, this rejection technique provides robustness against uncertainties and external disturbances after a finite transient time. Outdoors experimental results in a prototype illustrate the workability of the proposed methodology.

Hierarchical deep features can provide multilevel abstractions of target objects, which play an important role in target localization and classification. How to effectively aggregate abstract information from different levels in RGB and thermal modal is the key to exploit their complementary advantages for robust RGBT tracking. However, existing RGBT tracking algorithms either focus on the semantic information of the last layer or aggregate the hierarchical deep features from each modal by simple operations (e.g., summation and concatenation), which limit the capability of multimodal tracker. To address these issues, in this paper, we propose a novel multimodal cross-layer bilinear pooling network for RGBT tracking. In our network, firstly, to further boost the performance of the tracker, we adopt the channel attention mechanism to implement the adaptive calibration of feature channels for all convolutional layer features before realizing the hierarchical feature fusion. Then, we perform the bilinear pooling operation on any two layers through the cross product, which is a second-order computation and aggregates the deep semantic information and shallow texture information of target effectively. Finally, we design a quality-aware fusion module to aggregate bilinear pooling features of different layer interactions between different modalities in an adaptive manner. A large number of experiments on two public benchmark datasets demonstrate the effectiveness of our tracker against other state-of-the-art tracking methods.

In this contribution, tracking control designs using output feedback are presented for a two-phase Stefan problem (SP) arising in the modeling of the vertical gradient freeze process. The two-phase SP, consisting of two coupled free boundary problems, is a vital part of many crystal growth processes due to the temporally varying spatial extent of the solid and liquid domains during growth. After discussing the special needs of the process, collocated and flatness-based state feedback designs are carried out. The quality of the provided approximations and the performance and robustness against parameter uncertainties of the open- and closed-loop control setups are analyzed in several simulations.

Coastal ship tracking is significant for cargo transportation and route determination. However, there are few effective tracking methods for specialized ship tracking. Although Siamese networks have been commonly used for object tracking in the field of deep learning, the results for ship are not accurate due to the lack of contour and edge information. In addition, scale variation and seawater cause unstable ship movements, which aggravates the reduction in tracking accuracy. Therefore, we propose an enhanced SiamMask network for coastal ship tracking. Compared to the previous Siamese network, our algorithm has the following three advantages. (1) We apply the unity of visual object tracking and semi-supervised object segmentation to the ship tracking task, which completes target tracking while outputting edge shape information. (2) We propose a refined feature pyramid network, which utilizes a feature fusion module and Enhanced Residual Module (ERM) to solve the problems of scale variation in datasets. (3) We propose an attention-wise cross correlation with Multi-Dimension Attention Module (MDAM) to focus more on ship targets and suppress non-targets through autonomous learning at width, height and channel level, which creates a trade-off between the accuracy and the robustness of tracking algorithms. The experimental results show that our method achieves leading performance in LMD-TShips, outperforming most of the state-of-the-art trackers. Code is available at https://gitee.com/EnhancedSiamShipTracking/code.

This paper investigates the problem of global practical tracking by adaptive event-triggered output feedback for a class of uncertain nonlinear systems with unknown time-varying control coefficients. The nonlinear systems under consideration allow more general growth restriction, e.g., unknown constant and output-polynomial function coupled to the unmeasurable states. Based on non-separation principle, an adaptive event-triggered output feedback controller is proposed by combining dynamic high-gain scaling approach with backstepping method. It is worth emphasizing that a novel updating law of the high-gain is introduced to overcome the system nonlinearities and the serious uncertainties mentioned above. The controller proposed guarantees that the states of the resulting closed-loop systems are globally bounded, while the tracking error converges to a prescribed arbitrarily small neighborhood of the origin after a finite time. Finally, a numerical example is given to illustrate the effectiveness of the proposed approach.

A new variational Bayesian‐based adaptive distributed fusion unscented Kalman filter (ADFUKF‐VB) algorithm is proposed for the problem of state estimation in a distributed fusion target tracking system with unknown sensor measurement losses. First, the Bernoulli random variables are introduced as the judgement factors of the measurement losses in each local unscented Kalman filter (UKF) of the distributed fusion target tracking system, and the probability density function (PDF) of the measurement loss probability is modelled as a Beta distribution. Then, the variational Bayesian (VB) approach is used to infer the estimated variables in each local UKF. The measurement losses are judged according to the expectations of the Bernoulli random variables, and three ways are adopted to estimate the state vector based on the obtained judgement results. Finally, the covariance intersection (CI) fusion strategy is used to achieve the fusion of all local state estimates. The simulation results show that the proposed ADFUKF‐VB algorithm can accurately judge whether the measurement is lost and estimate the unknown measurement loss probability, moreover, effectively improve the accuracy of state estimation compared with the existing filtering algorithms with unknown measurement losses.

Current state-of-the-art object tracking methods have largely benefited from the public availability of numerous benchmark datasets. However, the focus has been on open-air imagery and much less on underwater visual data. Inherent underwater distortions, such as color loss, poor contrast, and underexposure, caused by attenuation of light, refraction, and scattering, greatly affect the visual quality of underwater data, and as such, existing open-air trackers perform less efficiently on such data. To help bridge this gap, this article proposes a first comprehensive underwater object tracking (UOT100) benchmark dataset to facilitate the development of tracking algorithms well-suited for underwater environments. The proposed dataset consists of 104 underwater video sequences and more than 74 000 annotated frames derived from both natural and artificial underwater videos, with great varieties of distortions. We benchmark the performance of 20 state-of-the-art object tracking algorithms and further introduce a cascaded residual network for underwater image enhancement model to improve tracking accuracy and success rate of trackers. Our experimental results demonstrate the shortcomings of existing tracking algorithms on underwater data and how our generative adversarial network (GAN)-based enhancement model can be used to improve tracking performance. We also evaluate the visual quality of our model's output against existing GAN-based methods using well-accepted quality metrics and demonstrate that our model yields better visual data.

In this paper, the simplified electromagnetic force/torque model and coupled orbit-attitude dynamics modeling in spacecraft electromagnetic docking are investigated, and an improved sliding mode control scheme based on planned trajectory is proposed. In this scenario, the docking two spacecraft are equipped with four energized solenoids with iron cores fixed in the body frame, and small-angle hypothesis is used to derive the simplified electromagnetic force/torque model, based on which the coupled orbit-attitude dynamics equation is established. With trajectory planning of relative orbit and attitude, where the tracking process of coupled orbit-attitude is divided into three successive parts with predefined time using three characteristic time instants, a sliding mode control strategy is proposed to solve the tracking problem. Simulation results illustrate the simplification rationality of electromagnetic force/torque model, and the good tracking performance of coupled orbit-attitude tracking controller at predefined time.

Tracking moving objects in remote-sensing videos is becoming increasingly important in remote-sensing analysis. This letter presents a novel object tracking method for remote-sensing videos. We start with using the traditional robust principal component analysis (RPCA) model to extract the moving object from the background. To describe the continuity of moving objects in spatial and temporal directions, we incorporate a 3-D total variation (3DTV) regularization into the RPCA model. Considering that the background is not static and the captured videos will contain noise because of the instability of the sensing camera, our proposed method introduces a certain part of the function to model the noise and capture the changes in background. Experimental results on real videos provided by 2016 IEEE GRSS Data Fusion Contest and 2020 Hyperspectral Object Tracking Challenge demonstrate the advantages of the moving object-tracking method via 3-D TV.

This paper concentrates on tracking multiple targets using an asynchronous network of sensors with different sampling rates. First, a timely fusion approach is proposed for handling measurements from asynchronous sensors. In the proposed approach, the arithmetic average fusion of the estimates provided by local cardinalized probability hypothesis density filters is recursively carried out according to the network-wide sampling time sequence. The corresponding intersensor communication is conducted by a partial flooding protocol, in which either cardinality distributions or intensity functions pertinent to local posteriors are disseminated among sensors. Moreover, both feedback and non-feedback fusion-filtering modes are provided to meet the performance and real-time requirements, respectively. Second, an extension of the timely fusion approach referred to as robust bootstrap approach is presented, which can deal with unknown clutter and detection parameters by exploiting a local bootstrap filtering scheme. Finally, numerical simulations are performed to test the proposed approaches.