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This paper focuses on the development of a guidance methodology for the planning of multiple ground target acquisition. Specifically, the work addresses the problem of the lack of an attitude guidance planner (AGP) aboard a remote sensing satellite. In general, the guidance is computed offline and uploaded by ground control to the space segment, i.e., satellites are not responsible for the guidance generation but they only perform control algorithms to track the guidance profiles provided by the ground segment. Overall, this limits the mission flexibility and efficiency, affecting the capability of autonomous satellite decisions. This choice is driven by the fact that the numerical algorithms used to optimize the attitude guidance trajectory require high computational effort to be implemented directly on the satellite computer. Therefore, the aim of this work is to design an analytical AGP solution to solve this problem by requiring low computational effort, making it suitable for real-time applications on on-board flight hardware. In this way, the satellite's guidance, navigation, and control (GNC) module would become completely autonomous and independent of ground control, which will only have to indicate the targets to be acquired so that the satellite can generate its own guidance for the GNC module. The AGP analytical solution for multiple ground target acquisition is evaluated by means of phases: the first phase is named the APPG (attitude point-to-point generator) and it aims to generate the point-to-point guidance to start the ground target acquisition. The second phase is named the ATPG (attitude target pointing generator) and it generates the reference guidance to maintain the payload view axis pointing toward the ground target. The two phases joined together give the whole guidance needed to observe ground target points by means of an analytical closed-form solution. [ABSTRACT FROM AUTHOR]

The existing target tracking algorithm of the tennis dynamic image serving path cannot correct the serving path in time according to the physical characteristics of the target, resulting in poor correction accuracy and stability. To this end, this paper proposes correction algorithm of tennis dynamic image serving path based on symmetric algorithm. Dynamic images are obtained using the visual acquisition model. On this basis, the contour of the moving target is obtained by the symmetric algorithm, and the complete moving target is obtained by the background difference method. The physical properties of moving objects are analyzed through a tennis serve visual model. The path correction model based on physical features is used to construct the relevant model so as to realize the path correction of the moving target. The experimental results show that the highest accuracy of the algorithm for tennis net and bottom line is 92.88% and 93.10%, respectively, and the average image correction accuracy is 95%. The tracking accuracy of service paths in complex backgrounds is 95%. These data show that the proposed algorithm has high correction accuracy and stability. [ABSTRACT FROM AUTHOR]

In this paper, a tracking algorithm based on the residual neural network model and machine learning is proposed. Compared with the widely used VGG network, the residual neural network has deeper characteristic layers and special additional layer structure, which break the symmetry of the network and reduce the degradation of the neural network. The additional layer and convolution layer are used for feature fusion to represent the target. The multi-features of the object can be captured by using the developed algorithm, so that the accuracy of tracking can be improved in some complex scenarios. In addition, we defined a new measure to calculate the similarity of different image regions and find the optimal matched region. The search area is delimited according to the continuity of the target motion, which improves the real-time performance of tracking. The experimental results illustrate that the proposed algorithm achieved a higher accuracy while taking into account the real time performance, especially in dealing with some complex scenarios such as deformation, rotation changes and background clutters, in comparison with the Multi-Domain Network (MDNet) algorithm based on a convolutional neural network. [ABSTRACT FROM AUTHOR]

Recently due to the good balance between performance and tracking speed, the discriminative correlation filter (DCF) has become a popular and excellent tracking method in short-term tracking. Computing the correlation of a response map can be efficiently performed in the Fourier domain by the discrete Fourier transform (DFT) of the input, where the DFT of an image has symmetry in the Fourier domain. However, most of the correlation filter (CF)-based trackers cannot deal with the tracking results and lack the effective mechanism to adjust the tracked errors during the tracking process, thus usually perform poorly in long-term tracking. In this paper, we propose a long-term tracking framework, which includes a tracking-by-detection part and redetection part. The tracking-by-detection part is built on a DCF framework, by integrating with a multifeature fusion model, which can effectively improve the discriminant ability of the correlation filter for some challenging situations, such as occlusion and color change. The redetection part can search the tracked object in a larger region and refine the tracking results after the tracking has failed. Benefited by the proposed redetection strategy, the tracking results are re-evaluated and refined, if it is necessary, in each frame. Moreover, the reliable estimation module in the redetection part can effectively identify whether the tracking results are correct and determine whether the redetector needs to open. The proposed redetection part utilizes a saliency detection algorithm, which is fast and valid for object detection in a limited region. These two parts can be integrated into DCF-based tracking methods to improve the long-term tracking performance and robustness. Extensive experiments on OTB2015 and VOT2016 benchmarks show that our proposed long-term tracking method has a proven effectiveness and high efficiency compared with various tracking methods. [ABSTRACT FROM AUTHOR]

Most trackers are only dependent on the first frame as a template to search for and locate the target location in subsequent videos. However, objects may undergo occlusions and deformation over time, and the original snapshot of the object can no longer accurately reflect the current appearance of the object, which greatly limits the performance improvement of the tracker. In this paper, we propose a novel Siamese tracking algorithm with symmetric structure called SiamRDT, which reflects the latest appearance and motion states of objects through additional reliable dynamic templates. The model decides whether to update the dynamic template according to the quality estimation score and employs the attention mechanism to enhance the reliability of the dynamic template, adopting the depth-wise correlation algorithm to integrate the initial template and the dynamic template and the search area. Through reliable dynamic templates and credible initial templates, the model can fuse initial-state information and the latest-state information of objects. We conduct sufficient ablation experiments to illustrate the effectiveness of the proposed key components, and the tracker achieves very competitive results on four large-scale tracking benchmarks, namely OTB100, GOT-10k, LaSOT, and TrackingNet. Our tracker achieves an AO score of 61.3 on GOT-10k, a precision score of 56.5 on LaSOT, a precision score of 69.3 on TrackingNet, and a precision score of 90.5 on OTB100. [ABSTRACT FROM AUTHOR]

Recently, Discriminative Correlation Filters (DCF) have shown excellent performance in visual object tracking. The correlation for a computing response map can be conducted efficiently in Fourier domain by Discrete Fourier Transform (DFT) of inputs, where the DFT of an image has symmetry on the Fourier domain. To enhance the robustness and discriminative ability of the filters, many efforts have been devoted to optimizing the learning process. Regularization methods, such as spatial regularization or temporal regularization, used in existing DCF trackers aim to enhance the capacity of the filters. Most existing methods still fail to deal with severe appearance variations—in particular, the large scale and aspect ratio changes. In this paper, we propose a novel framework that employs adaptive spatial regularization and temporal regularization to learn reliable filters in both spatial and temporal domains for tracking. To alleviate the influence of the background and distractors to the non-rigid target objects, two sub-models are combined, and multiple features are utilized for learning of robust correlation filters. In addition, most DCF trackers that applied 1-dimensional scale space search method suffered from appearance changes, such as non-rigid deformation. We proposed a 2-dimensional scale space search method to find appropriate scales to adapt to large scale and aspect ratio changes. We perform comprehensive experiments on four benchmarks: OTB-100, VOT-2016, VOT-2018, and LaSOT. The experimental results illustrate the effectiveness of our tracker, which achieved a competitive tracking performance. On OTB-100, our tracker achieved a gain of 0.8% in success, compared to the best existing DCF trackers. On VOT2018, our tracker outperformed the top DCF trackers with a gain of 1.1% in Expected Average Overlap (EAO). On LaSOT, we obtained a gain of 5.2% in success, compared to the best DCF trackers. [ABSTRACT FROM AUTHOR]

Research on trajectory tracking is crucial for the development of autonomous vehicles. This paper presents a trajectory tracking scheme by utilizing model predictive control (MPC) and preview-follower theory (PFT), which includes a reference generation module and a MPC controller. The reference generation module could calculate reference lateral acceleration at the preview point by PFT to update state variables, and generate a reference yaw rate in each prediction point. Since the preview range is increased, PFT makes the calculation of yaw rate more accurate. Through physical constraints, the MPC controller can achieve the best tracking of the reference path. The MPC problem is formulated as a linear time-varying (LTV) MPC controller to achieve a predictive model from nonlinear vehicle dynamics to continuous online linearization. The MPC-PFT controller method performs well by increasing the effective length of the reference path. Compared with MPC and PFT controllers, the effectiveness and robustness of the proposed method are proved by simulations of two typical working conditions. [ABSTRACT FROM AUTHOR]

Ship detection and tracking is an important task in video surveillance in inland waterways. However, ships in inland navigation are faced with accidents such as collisions. For collision avoidance, we should strengthen the monitoring of navigation and the robustness of the entire system. Hence, this paper presents ship detection and tracking of ships using the improved You Only Look Once version 3 (YOLOv3) detection algorithm and Deep Simple Online and Real-time Tracking (Deep SORT) tracking algorithm. Three improvements are made to the YOLOv3 target detection algorithm. Firstly, the Kmeans clustering algorithm is used to optimize the initial value of the anchor frame to make it more suitable for ship application scenarios. Secondly, the output classifier is modified to a single Softmax classifier to suit our ship dataset which has three ship categories and mutual exclusion. Finally, Soft Non-Maximum Suppression (Soft-NMS) is introduced to solve the deficiencies of the Non-Maximum Suppression (NMS) algorithm when screening candidate frames. Results showed the mean Average Precision (mAP) and Frame Per Second (FPS) of the improved algorithm are increased by about 5% and 2, respectively, compared with the existing YOLOv3 detecting Algorithm. Then the improved YOLOv3 is applied in Deep Sort and the performance result of Deep Sort showed that, it has greater performance in complex scenes, and is robust to interference such as occlusion and camera movement, compared to state of art algorithms such as KCF, MIL, MOSSE, TLD, and Median Flow. With this improvement, it will help in the safety of inland navigation and protection from collisions and accidents. [ABSTRACT FROM AUTHOR]

In this paper, to further improve the filtering performance and enhance the poor tracking capability of the conventional combined step-size affine projection sign algorithm (CSS-APSA) in system identification, we propose a simplified CSS-APSA (SCSS-APSA) by applying the first-order Taylor series expansion to the sigmoidal active function (of which the independent variable is symmetric) of CSS-APSA. SCSS-APSA has lower computational complexity, and can achieve comparable, or even better filtering performance than that of CSS-APSA. In addition, we propose a modification of the sigmoidal active function. The modified sigmoidal active function is a form of scaling transformation based on the conventional one. Applying the modified function to the CSS-APSA, we can obtain the modified CSS-APSA (MCSS-APSA). Moreover, the extra parameter of MCSS-APSA provides the power to accelerate the convergence rate of CSS-APSA. Following the simplification operations of SCSS-APSA, the computational complexity of MCSS-APSA can also be reduced. Therefore, we get the simplified MCSS-APSA (SMCSS-APSA). Simulation results demonstrate that our proposed algorithms are able to achieve a faster convergence speed in system identification. [ABSTRACT FROM AUTHOR]

A photovoltaic power supply with a simple structure and high tracking efficiency is needed in self-powered, wireless sensor networks. First, a maximum power point tracking (MPPT) algorithm, including the load current maximization-perturbation and observation (LCM-P&O) methods, with a fixed step size, is proposed by integrating the traditional load current maximization (LCM) method and perturbation and observation (P&O) method. By sampling the changes of load current and photovoltaic cell input current once the disturbance is applied, the pulse width modulation (PWM) regulation mode, i.e., increasing or reducing, can be determined in the next process. Then, the above algorithm is improved by using the variable step size strategy. By comparing the difference between the absolute value of the observed current value and the theoretical current value at the maximum power point of the photovoltaic cell with the set threshold value, the variable step size for perturbation is determined. MATLAB simulation results show that the LCM-P&O method, with a variable step size, has faster convergence speed and higher tracking accuracy. Finally, the two MPPT algorithms are tested and analyzed under constant voltage source input and indoor fluorescent lamp illumination through an actual circuit, respectively. The experimental results show that the LCM-P&O method with variable step size has a higher tracking efficiency, about 90%–92%, and has higher stability and lower power consumption. [ABSTRACT FROM AUTHOR]

In the context of the problem of water pollution, the movement characteristics and patterns of fish under normal water quality and abnormal water quality are clearly different. This paper proposes a biological water quality monitoring method combining three-dimensional motion trajectory synthesis and integrated learning. The videos of the fish movement are captured by two cameras, and the Kuhn-Munkres (KM) algorithm is used to match the target points of the fish body. The Kalman filter is used to update the current state and find the optimal tracking position as the tracking result. The Kernelized Correlation Filters (KCF) algorithm compensates the targets that are lost in the tracking process and collision or occlusion in the movement process, reducing the errors caused by illumination, occlusion and water surface fluctuation effectively. This algorithm can directly obtain the target motion trajectory, avoiding the re-extraction from the centroid point in the image sequence, which greatly improves the efficiency. In order to avoid the one-sidedness of the two-dimensional trajectory, the experiment combines the pixel coordinates of different perspectives into three-dimensional trajectory pixel coordinates, so as to provide a more authentic fish swimming trajectory. We then select a representative positive and negative sample data set; the number of data sets should have symmetry. The base classifier capable of identifying different water quality is obtained by training. Finally, support vector machine(SVM), eXtreme Gradient Boosting (XGBoost) and pointnet based classifiers are combined into strong classifiers through integrated learning. The experimental results show that the integrated learning model can reflect the water quality effectively and accurately under the three-dimensional trajectory pixel coordinates of fish, and the recognition rate of water quality is above 95%. [ABSTRACT FROM AUTHOR]

The conventional dynamic programming-based track-before-detect (DP-TBD) methods are usually intractable in multi-target scenarios. The adjacent targets may interfere with each other, and the computational complexity is increased with the number of targets. In this paper, a DP-TBD method using parallel computing (PC-DP-TBD) is proposed to solve the above problems. The search region of the proposed PC-DP-TBD is divided into several parts according to the possible target movement direction. The energy integration is carried out independently and parallel in each part. This contributes to reducing the computational complexity in each part, since the divided search region is smaller than the whole one. In addition, the target energy can only be integrated adequately in the part in which the search direction matches the target movement. This is beneficial to improve the ability to detect the targets with various movement directions in different parts with different search directions. The solution to the problem of the adjacent targets interfering with each other is discussed. The procedure of the parallel computing in the proposed PC-DP-TBD is presented in detail. Simulations are conducted to verify the superiority of the proposed PC-DP-TBD in terms of detection probability and computational complexity. [ABSTRACT FROM AUTHOR]