Accurate tracking and predicting unmanned aerial vehicle (UAV) trajectories are essential to ensure mission success, equipment safety, and data accuracy. Maneuverable UAVs exhibit complex and dynamic motion, and conventional tracking algorithms that rely on predefined models perform poorly when unknown parameters are used. To address this issue, this paper introduces a hybrid dual-scale neural network model based on the generalized regression multi-model and cubature information filter (GRMM-CIF) framework. We have established the GRMM-CIF filtering structure to differentiate motion modes and reduce measurement noise. Furthermore, considering trajectory datasets and rates of motion change, a neural network at different scales will be designed. We propose the dual-scale bidirectional long short-term memory (DS-Bi-LSTM) algorithm to address prediction delays in a multi-model context. Additionally, we employ scale sliding windows and threshold-based decision-making to achieve dual-scale trajectory reconstruction, ultimately enhancing tracking accuracy. Simulation results confirm the effectiveness of our approach in handling the uncertainty of UAV motion and achieving precise estimations.

Direction of arrival (DOA) estimation plays a critical role in remote sensing, where it aids in identifying and tracking multiple targets across complex environments, from atmospheric monitoring to resource mapping. Leveraging difference covariance array (DCA) for DOA estimation has become prevalent, particularly with sparse arrays capable of resolving more targets than the number of sensors. This paper proposes a new hole-free sparse array configuration for remote sensing applications to achieve improved DOA estimation performance using DCA. By symmetrically placing a minimum redundancy array (MRA) and its complementary MRA on both sides of a sparse uniform linear array (ULA), this configuration maximizes degrees of freedom (DOFs) and minimizes mutual coupling effects. Expressions for calculating sensor positions and optimal element allocation methods to maximize DOFs are derived. Simulation experiments in various scenarios have shown the advantages of the proposed array in DOA estimation, including a strong ability to estimate multi-targets, high angular resolution, low estimation error, and strong robustness to mutual coupling.