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Geodetic coordinate information and attitude information of the observation platform are necessary for multi-UAV position alignment and target tracking. In a complex sea environment, the navigation equipment of a UAV is susceptible to interference. High-precision geodetic coordinate information and attitude information are difficult to obtain. Aiming to solve the above problems, a low-precision geodetic coordinate real-time systematic spatial registration algorithm based on multi-UAV observation and an improved robust fusion tracking algorithm of multi-UAV to sea targets considering attitude error are proposed. The spatial registration algorithm obtains the observation information of the same target based on the mutual observation information. Then, geodetic coordinate systematic error is accurately estimated by establishing the systematic error estimation measurement equation. The improved robust fusion tracking algorithm considers the influence of UAV attitude error in the observation. The simulation experiment and practical experiment show that the algorithm can not only estimate systematic error accurately but also improve tracking accuracy.

This paper addresses a problem of optimal error quantification in the framework of robust control theory in the 𝓁1 setup. The upper bounds of biased external disturbance and the gains of coprime factor perturbations in a discrete-time linear time invariant SISO plant are assumed to be unknown. The computation of optimal data-consistent upper bounds under a known bias of external disturbance has been simplified to linear programming. This allows for the computation of optimal estimates in real-time and their application to achieve optimal robust steady-state tracking even when facing an unknown bias in the external disturbance. The presented results have been illustrated through computer simulations.

This paper presents a new nonlinear control scheme for a pulse-width modulated dc-dc Zeta converter operating in buck and boost modes. The averaged model of the dc-dc power converter is derived, based on which a robust control law is developed using a simplified sliding-mode control technique. The existence and stability conditions are introduced to select proper controller gains that ensure fast output voltage convergence towards reference voltage. A detailed design procedure is provided to realize the control scheme using low-cost discrete components. The proposed control method handles large disturbances, accommodates the non-minimum phase property, and maintains regulated output voltage during step-up and step-down operation modes. The control system also maintains constant switching frequency, improves the transient response, and eliminates the steady-state error at the output voltage. A MATLAB/SIMULINK model is developed to simulate the closed-loop dc-dc Zeta converter in continuous conduction mode and investigate the tracking and regulation performance. The simulation results confirm the robustness and stability of the nonlinear controlled power converter under abrupt line and load variations.

Precise and robust feature extraction plays a key role in high-performance tracking to analyse the videos from drones, surveillance and automatic driving, etc. However, most existing Siamese network-based trackers mainly focus on constructing complicated network models and refinement strategies, while using comparatively simple and heuristic conventional or deformable convolutions to extract features from the sampling positions that may be far away from a target region. Consequently, the coarsely extracted features may introduce background noise and degrade the tracking performance. To address this issue, we present a propose-and-refine tracker (PRTracker) that combines anchor-free style proposals at the coarse level, and alignment convolution-driven refinement at the fine level. Specifically, at the coarse level, we design an anchor-free model to effectively generate proposals that provide more reliable interested regions for further verifying. At the fine level, an alignment convolution-based refinement strategy is adopted to improve the convolutional sampling positions of the proposals, thus making the classification and regression of them more accurate. Through using alignment convolution, the convolution sampling positions of the proposals can be efficiently and effectively re-localized, thus improving the accuracy of the extracted features. Finally, a simple yet robust target mask is designed to make full use of the initial state of a target to further improve the tracking performance. The proposed PRTracker achieves a competitive performance against six tracking benchmarks (i.e., UAV123, VOT2018, VOT2019, OTB100, NfS and LaSOT) at 75 FPS.

The AI-aided variational Bayesian extended Kalman filter (AI-VBEKF)-based robust direction-of-arrival (DOA) technique is proposed to make reliable estimations of the bearing angle of an uncooperative underwater target with uncertain environment noise. Considering that the large error of the guess of the initial mean square error matrix (MSEM) will lead to inaccurate DOA tracking results, an attention-based deep convolutional neural network is first proposed to make reliable estimations of the initial MSEM. Then, by utilizing the AI-VBEKF estimating scheme, the uncertain measurement noise caused by the unknown underwater environment along with the bearing angle of the target can be estimated simultaneously to provide reliable results at every DOA tracking step. The proposed technique is demonstrated and verified by both of the simulations and the real sea trial data from the South China Sea in July 2021, and both the robustness and accuracy are proven superior to the traditional DOA-estimating methods.

A ferromagnetic vehicle, such as a submarine, magnetized by the Earth’s magnetic field produces a magnetic anomaly field, and the tracking of moving targets can be realized through real-time analysis of magnetic data. At present, there are few tracking methods based on magnetic field vectors and their gradient tensor. In this paper, the magnetic field vector and its gradient tensor are used to calculate equivalent magnetic force. It shows the direction of the vector between the detector and the tracking targets for controlling the direction of motion of the detector and achieving the purpose of tracking. Compared with existing positioning methods, the proposed method is relatively less affected by instrument resolution and noise and maintains robustness when the velocity vectors of multiple magnetic targets change randomly.

This manuscript presents a robust tracking (servomechanism) controller for linear time-invariant (LTI) islanded (autonomous, isolated) microgrid voltage control. The studied microgrid (MG) consists of many distributed energy resources (DERs) units, each using a voltage-sourced converter (VSC) for the interface. The optimal tracker design uses the ellipsoidal approximation to the invariant sets. The MG system is decomposed into different subsystems (DERs). Each subsystem is affected by the rest of the system that is considered as a disturbance to be rejected by the controller. The proposed tracker (state feedback integral control) rejects bounded external disturbances by minimizing the invariant ellipsoids of the MG dynamics. A condition to design decentralized controllers is derived in the form of linear matrix inequalities. The proposed controller is characterized by rapid transient response, and zero error in the steady state. A robustness analysis of the control strategy (against load changes, load unbalances, etc.) is carried out. A MATLAB/SimPowerSystems (R2017b, MathWorks, Natick, MA, USA) simulation of the case study confirm the robustness of the proposed controller.

Although recently developed trackers have shown excellent performance even when tracking fast moving and shape changing objects with variable scale and orientation, the trackers for the electro-optical targeting systems (EOTS) still suffer from abrupt scene changes due to frequent and fast camera motions by pan-tilt motor control or dynamic distortions in field environments. Conventional context aware (CA) and deep learning based trackers have been studied to tackle these problems, but they have the drawbacks of not fully overcoming the problems and dealing with their computational burden. In this paper, a global motion aware method is proposed to address the fast camera motion issue. The proposed method consists of two modules: (i) a motion detection module, which is based on the change in image entropy value, and (ii) a background tracking module, used to track a set of features in consecutive images to find correspondences between them and estimate global camera movement. A series of experiments is conducted on thermal infrared images, and the results show that the proposed method can significantly improve the robustness of all trackers with a minimal computational overhead. We show that the proposed method can be easily integrated into any visual tracking framework and can be applied to improve the performance of EOTS applications.

Sparse coding is an emerging method that has been successfully applied to both robust object tracking and recognition in the vision literature. In this paper, we propose to explore a sparse coding-based approach toward joint object tracking-and-recognition and explore its potential in the analysis of forward-looking infrared (FLIR) video to support nighttime machine vision systems. A key technical contribution of this work is to unify existing sparse coding-based approaches toward tracking and recognition under the same framework, so that they can benefit from each other in a closed-loop. On the one hand, tracking the same object through temporal frames allows us to achieve improved recognition performance through dynamical updating of template/dictionary and combining multiple recognition results; on the other hand, the recognition of individual objects facilitates the tracking of multiple objects (i.e., walking pedestrians), especially in the presence of occlusion within a crowded environment. We report experimental results on both the CASIAPedestrian Database and our own collected FLIR video database to demonstrate the effectiveness of the proposed joint tracking-and-recognition approach.