Biologically adaptive robust mean shift algorithm with Cauchy predator-prey BBO and space variant resolution for unmanned helicopter formation

Visual tracking technology can provide measurement information for unmanned helicopter formation and thus, more attention is being paid to this research area. We propose a novel mean shift （MS） algorithm that is both adaptive and robust for unmanned helicopter formation and apply it to the leading unmanned helicopter tracking. The movement of an unmanned helicopter is very flexible and changeable, which makes the tracking there of more difficulty than for common targets. In creating an algorithm that can adapt to the acceleration of the unmanned helicopter and estimates both the scale and orientation of the movement changes, we combine the traditional MS with the bio-inspired Cauchy predator-prey biogeography-based optimization （CPPBBO） evolutionary algorithm, and also the space variant resolution （SVR） mechanism of the human visual system （MS-CPPBBO-SVR）. To demonstrate the effectiveness and robustness of the proposed method and justify the importance of the CPPBBO algorithm and SVR mechanism at the same time, a series of comparative exper- iments were carried out. The experimental results of the proposed MS-CPPBBO-SVR method are compared with other competitive tracking methods, such as MS, MS with SVR （MS-SVR）, MS-SVR with several other optimization algorithms, and the robust particle filter algorithm. The experimental results demonstrate that our proposed tracking approach, MS-CPPBBO-SVR, is more adaptive, robust and efficient in target tracking than the other methods.     

Multipath effects on vector tracking algorithm for GNSS signal

The vector tracking algorithm uses a single extended Kalman filter （EKF） to predict the time-delays and the Doppler deviations of the GNSS signal, while also estimating the user＇s position, velocity, and clock state. In this paper, the effects of multipath on the tracking performance of the vector delay / frequency lock loop （VDFLL） is studied for better application in the multipath environment. The error expressions of the measurements are given in theory. The tracking error caused by multipath is reduced by VDFLL, which is proved by the tracking error of VDFLL through a new iterating method. The theoretical analysis is verified by the Monte Carlo simulation.     

Adaptive neural network control for a class of continuous stirred tank reactor systems

In this paper, the control problem of continuous stirred tank reactors （CSTR） is studied. The considered CSTR are required to contain unknown functions and unknown dead zone input. An adaptive controller that uses the neural networks （NNs） is provided to solve the unknown terms. The proposed approach overcomes the effect of the dead zone input. The dead zone input in the systems is compensated for by introducing a new Lyapunov form and Young＇s inequality. The backstepping procedure is exploited to implement controller design with adaptation laws. The stability is analyzed using Lyapunov method. The performance is examined for CSTR to confirm the effectiveness of the proposed approach based on computer simulation.     

A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways

Real-time highway traffic monitoring systems play a vital role in road traffic management, planning, and preventing frequent traffic jams, traffic rule violations, and fatal road accidents. These systems rely entirely on online traffic flow info estimated from time-dependent vehicle trajectories. Vehicle trajectories are extracted from vehicle detection and tracking data obtained by processing road-side camera images. General-purpose object detectors including Yolo, SSD, EfficientNet have been utilized extensively for real-time object detection task, but, in principle, Yolo is preferred because it provides a high frame per second (FPS) performance and robust object localization functionality. However, this algorithm’s average vehicle classification accuracy is below 57%, which is insufficient for traffic flow monitoring. This study proposes improving the vehicle classification accuracy of Yolo, and developing a novel bounding box (Bbox)-based vehicle tracking algorithm. For this purpose, a new vehicle dataset is prepared by annotating 7216 images with 123831 object patterns collected from highway videos. Nine machine learning-based classifiers and a CNN-based classifier were selected. Next, the classifiers were trained via the dataset. One out of ten classifiers with the highest accuracy was selected to combine to Yolo. This way, the classification accuracy of the Yolo-based vehicle detector was increased from 57% to 95.45%. Vehicle detector 1 (Yolo) and vehicle detector 2 (Yolo + best classifier), and the Kalman filter-based tracking as vehicle tracker 1 and the Bbox-based tracking as vehicle tracker 2 were applied to the categorical/total vehicle counting tasks on 4 highway videos. The vehicle counting results show that the vehicle counting accuracy of the developed approach (vehicle detector 2 + vehicle tracker 2) was improved by 13.25% and this method performed better than the other 3 vehicle counting systems implemented in this study.     

Hierarchical inversion‐based output tracking control for uncertain autonomous underwater vehicles using extended Kalman filter

In this study, a hierarchical inversion‐based output tracking controller (HIOTC) is developed for an autonomous underwater vehicle (AUV) subject to random uncertainties (e.g., current disturbances, unmodeled dynamics, and parameter variations) and noises (e.g., process and measurement noises). The proposed HIOTC respectively utilizes a combination of feedforward and feedback controls in a hierarchical structure based on the kinematic and dynamic models of the system. Moreover, to obtain uncontaminated or unavailable states for implementing the proposed control law, the extended Kalman filter (EKF) is employed to estimate the system states. Then, the position outputs, orientation, and velocity of the AUV are reached with guaranteed asymptotic stability. The robustness of the proposed HIOTC is verified through injection of random uncertainties into the system model. The closed‐loop stability of the proposed individual subsystems is respectively guaranteed to have uniformly ultimately bounded (UUB) performance based on the Lyapunov stability criteria. In addition, the asymptotic tracking of the overall system is demonstrated using Barbalat's lemma. Finally, the feasibility and effectiveness of the proposed control scheme are evaluated through computer simulations and it is shown that the overall system achieves good asymptotic tracking performance.     

基于检测与跟踪相互迭代的极暗弱目标搜索算法

针对极低信噪比（LSNR）情况下暗弱运动目标和背景噪声的强度难以区分的问题,提出了一种基于检测与跟踪相互迭代的极暗弱目标搜索算法,总体上采用将时域检测与空域跟踪的过程联合、迭代进行的新型策略。首先,在检测过程中计算检测窗口内信号片段与已经提取的背景估计特征的差别;然后,在跟踪过程中运用动态规划算法保留使得轨迹能量累积最大的轨迹;最后,自适应地调整下一检测过程中被保留轨迹的检测器阈值参数,使该轨迹内的像素能以更宽容的策略被保留到下一检测跟踪阶段。实验测试结果表明,所提算法可以在1%~2%的虚警率和约70%的检测率下探测到低至0 dB的暗弱运动目标。可见该算法可有效改善对LSNR暗弱目标的检测能力。     

基于场景元模型的软件产品线追踪方法

针对软件产品线需求追踪方法进行了研究,基于软件产品线领域需求分析、设计、实现三个阶段的信息传播,提出了一种基于场景元模型的多层次软件产品线追踪模型,完整描述了追踪信息变化的整体框架;建立了需求—用例追踪矩阵、用例—动作追踪矩阵、动作—构件追踪矩阵;根据矩阵运算,描述了具体追踪实现过程,通过一个ATM案例验证了该方法对于软件产品线领域需求获取以及需求变更等问题的解决有较好的效果。     

Robust lane recognition for structured road based on monocular vision

A robust lane detection and tracking system based on monocular vision is presented in this paper.First,the lane detection algorithm can transform raw images into top view images by inverse perspective mapping(IPM),and detect both inner sides of the lane accurately from the top view images.Then the system will turn to lane tracking procedures to extract the lane according to the information of last frame.If it fails to track the lane,lane detection will be triggered again until the true lane is found.In this system,θ-oriented Hough transform is applied to extract candidate lane markers,and a geometrical analysis of the lane candidates is proposed to remove the outliers.Additionally,vanishing point and region of interest(ROI)dynamically planning are used to enhance the accuracy and efficiency.The system was tested under various road conditions,and the result turned out to be robust and reliable.     

Global robust output tracking control for a class of cascade nonlinear systems with unknown control directions

We investigate the global robust tracking problem via output feedback for a class of cascade nonlinear systems with dynamic uncertainties and non-vanishing disturbances. It does not require a priori knowledge of the sign of the high-frequency gain. A recursive design scheme is presented using the ideas of pseudosign function, Nussbaum-type gain technique and the deadzone method. It is shown that under some conditions, the tracking error can be guaranteed asymptotic to the interval [?ε, ε]?? with arbitrary prescribed ε>0 after a finite time, while keeping all signals of the resulting closed-loop systems bounded. The simulation results demonstrate the effectiveness of the proposed algorithm.     

Object tracking based on an online learning network with total error rate minimization

This paper presents a visual object tracking system which is tolerant to external imaging factors such as illumination, scale, rotation, occlusion and background changes. Specifically, an integration of an online version of total-error-rate minimization based projection network with an observation model of particle filter is proposed to effectively distinguish between the target object and the background. A re-weighting technique is proposed to stabilize the sampling of particle filter for stochastic propagation. For self-adaptation, an automatic updating scheme and extraction of training samples are proposed to adjust to system changes online. Our qualitative and quantitative experiments on 16 public video sequences show convincing performances in terms of tracking accuracy and computational efficiency over competing state-of-the-art algorithms.