In this letter, we study the localization problem of a mobile robot with range measurement from a single beacon. Previous filtering-based studies usually required accurate statistics of noises to be theoretically sound and reliable, which is difficult to obtain in practical systems. To solve this problem, we propose an accurate and efficient localization method based on a set-membership filtering framework with constrained zonotopes. This method includes three novel steps. First, we design a convex optimization relaxation method to handle the non-convexity caused by the single-beacon range measurement. Then, a halfspace-intersection refinement is proposed which improves the estimation accuracy. Finally, we provide a sliding-window recursive method that simultaneously guarantees the accuracy and the efficiency. Simulations and field experiments corroborate the effectiveness of our proposed method.

This paper introduces a novel prediction model designed to mitigate the substantial data dependency associated with maneuver trajectory prediction in unmanned combat air vehicles (UCAVs) during air combat. Considering the characteristics of high noise, dynamic complexity, and variable data lengths inherent in short‐range air combat scenarios, we employ dynamic time warping (DTW) to assess the similarity of 3D trajectory data. This approach allows us to identify and select the most analogous historical data, which we then utilize as our training dataset. In pursuit of enhanced precision for online trajectory prediction, we propose an improved convolutional neural network (CNN) that not only offers “after‐zero” information but also incorporates delay compensation mechanisms. Our experimental findings indicate that the proposed prediction model not only satisfies the stringent timeliness requirements but also outperforms benchmark models in terms of prediction accuracy across various operating conditions.

Benefiting from its structural simplicity and low cost, the inertial/ranging integrated navigation system is widely utilized in multi-agent applications, particularly in unmanned aerial vehicles (UAVs). As the deployment of UAVs in complex environments becomes more prevalent, accurate positioning in sparse observation scenarios has become increasingly important. In satellite-denied environments with few anchors, traditional filtering methods for positioning suffer from poor effectiveness due to the lack of constraints. This article proposes a method to enhance positioning accuracy in such environments by optimizing the inertial outputs of each UAV. The optimization process is based on the range measurements between the UAVs and a single anchor. By solving the optimization function derived using Bayesian theory, the optimized inertial outputs of the UAVs can be obtained. These optimized inertial data are then used in place of the original measurements for position estimation in the filter, resulting in improved performance. Simulation and real-world experiments validate that the proposed method can enhance UAVs’ positioning accuracy in single-anchor environments, surpassing the performance of a single optimizer or filter. Furthermore, the positions estimated by cooperative agents demonstrate higher accuracy than those estimated by individual agents, as more ranging measurements are incorporated.