Moving target tracking is one of the most active applications in the field of unmanned aerial vehicles (UAVs). Due to their high adaptability, UAVs equipped with multisensor vision tracking techniques can perform various tasks in hazardous environments. However, varying application requirements and scenarios result in different tracking focuses. This article reviews the current research status of UAV moving target tracking with tracking requirements in mind. First, this article proposes a taxonomy that categorizes target tracking techniques into three categories: detection tracking, following tracking, and cooperative tracking, which based on the varying requirements of target tracking in different scenarios. Furthermore, the current UAV algorithms for tracking moving targets are classified and summarized based on different application scenarios. Finally, this article conducts research and analysis on experimental results, funded projects, and competitions to compare the real-world application effects and current progress of UAV tracking methods in relevant fields. In addition, it discusses the challenges and future research directions for UAVs in tracking.

With the advent of deep learning, self-driving schemes based on deep learning are becoming more and more popular. Robust perception-action models should learn from data with different scenarios and real behaviors, while current end-to-end model learning is generally limited to training of massive data, innovation of deep network architecture, and learning in-situ model in a simulation environment. Therefore, we introduce a new image style transfer method into data augmentation, and improve the diversity of limited data by changing the texture, contrast ratio and color of the image, and then it is extended to the scenarios that the model has been unobserved before. Inspired by rapid style transfer and artistic style neural algorithms, we propose an arbitrary style generation network architecture, including style transfer network, style learning network, style loss network and multivariate Gaussian distribution function. The style embedding vector is randomly sampled from the multivariate Gaussian distribution and linearly interpolated with the embedded vector predicted by the input image on the style learning network, which provides a set of normalization constants for the style transfer network, and finally realizes the diversity of the image style. In order to verify the effectiveness of the method, image classification and simulation experiments were performed separately. Finally, we built a small-sized smart car experiment platform, and apply the data augmentation technology based on image style transfer drive to the experiment of automatic driving for the first time. The experimental results show that: (1) The proposed scheme can improve the prediction accuracy of the end-to-end model and reduce the model’s error accumulation; (2) the method based on image style transfer provides a new scheme for data augmentation technology, and also provides a solution for the high cost that many deep models rely heavily on a large number of label data.

This paper addresses the problem of nonlinear height tracking control of an automobile active air suspension with the output state constraints and time-varying disturbances. The proposed control strategy guarantees that the ride height stays within a predefined range, and converges closely to an arbitrarily small neighborhood of the desired height, ensuring uniform ultimate boundedness. The designed nonlinear observer is able to compensate for the time-varying disturbances caused by external random road excitation and perturbations, achieving robust performance. Simulation results obtained from the co-simulation (AMESim-Matlab/Simulink) are given and analyzed, demonstrating the efficiency of the proposed control methodology.

Simultaneous localization and mapping (SLAM) has a wide range for applications in mobile robotics. Lightweight and inexpensive vision sensors have been widely used for localization in GPS-denied or weak GPS environments. Mobile robots not only estimate their pose, but also correct their position according to the environment, so a proper mathematical model is required to obtain the state of robots in their circumstances. Usually, filter-based SLAM/VO regards the model as a Gaussian distribution in the mapping thread, which deals with the complicated relationship between mean and covariance. The covariance in SLAM or VO represents the uncertainty of map points. Therefore, the methods, such as probability theory and information theory play a significant role in estimating the uncertainty. In this paper, we combine information theory with classical visual odometry (SVO) and take Jensen-Shannon divergence (JS divergence) instead of Kullback-Leibler divergence (KL divergence) to estimate the uncertainty of depth. A more suitable methodology for SVO is that explores to improve the accuracy and robustness of mobile devices in unknown environments. Meanwhile, this paper aims to efficiently utilize small portability for location and provide a priori knowledge of the latter application scenario. Therefore, combined with SVO, JS divergence is implemented, which has been realized. It not only has the property of accurate distinction of outliers, but also converges the inliers quickly. Simultaneously, the results show, under the same computational simulation, that SVO combined with JS divergence can more accurately locate its state in the environment than the combination with KL divergence.