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As navigation is a key to task execution of micro unmanned aerial vehicle (UAV) swarm, the cooperative navigation (CN) method that integrates relative measurements between UAVs has attracted widespread attention due to its performance advantages. In view of the precision and efficiency of cooperative navigation for low-cost micro UAV swarm, this paper proposes a sigma point belief propagation-based (SPBP) CN method that can integrate self-measurement data and inter-UAV ranging in a distributed manner so as to improve the absolute positioning performance of UAV swarm. The method divides the sigma point filter into two steps: the first is to integrate local measurement data; the second is to approximate the belief of position based on the mean and covariance of the state, and pass message by augmentation, resampling and cooperative measurement update of the state to realize a low-complexity approximation to traditional message passing method. The simulation results and outdoor flight test results show that with similar performance, the proposed CN method has a calculation load more than 20 times less than traditional BP algorithms. [ABSTRACT FROM AUTHOR]

Image filtering is to retain the details of the image as much as possible and meanwhile suppress the noise pollution to great extent. This study presents an image filtering using the truncated statistics and edge preserving. In the first step of our method, the alpha‐trimmed filter is utilized to remove a variety of types of noises; in the second step, taking the image after alpha‐trimmed filtering as a guide image, the local linear model between the guide image and the target image is established; in the third step, the obtained local linear model is further simplified to reduce the time complexity; and finally, using the relationship between image local variance and the global variance, the local linear model is modified to enhance the details of the image and meanwhile remove halo phenomenon. This method has three advantages: (i) it is flexible to deal with the images stained by various types of high‐intensity noise; (ii) it is effective to keep the image details and profile information, and remove the halo phenomenon; and (iii) it runs in time linear in the image size, thus its computation complexity is low. Experimental results show that the proposed filter is robust and efficient. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs), which are famous for the capability of learning complex underlying data distribution, are, however, known to be tricky in the training process, which would probably result in mode collapse or performance deterioration. Current approaches of dealing with GANs' issues almost utilize some practical training techniques for the purpose of regularization, which, on the other hand, undermines the convergence and theoretical soundness of GAN. In this article, we propose to stabilize GAN training via a novel particle-based variational inference-Langevin Stein variational gradient descent (LSVGD), which not only inherits the flexibility and efficiency of original SVGD but also aims to address its instability issues by incorporating an extra disturbance into the update dynamics. We further demonstrate that, by properly adjusting the noise variance, LSVGD simulates a Langevin process whose stationary distribution is exactly the target distribution. We also show that LSVGD dynamics has an implicit regularization, which is able to enhance particles' spread-out and diversity. Finally, we present an efficient way of applying particle-based variational inference on a general GAN training procedure no matter what loss function is adopted. Experimental results on one synthetic data set and three popular benchmark data sets-Cifar-10, Tiny-ImageNet, and CelebA-validate that LSVGD can remarkably improve the performance and stability of various GAN models.