The Bearing–Angle algorithm effectively improves the observability of vision-based motion estimation for moving targets by combining the dimensional information of target detection frames. However, the robustness of this algorithm will be significantly reduced when the observation error increases due to sudden changes in the target motion state. To address this shortcoming, this paper proposes a visual target motion estimation algorithm called the Dynamic Bearing–Angle, which aims to improve the accuracy and robustness of target motion analysis in dynamic scenarios such as unmanned aerial vehicle (UAV). The algorithm innovatively introduces a dual robustness mechanism of dynamic noise intensity adaptation and outlier suppression based on M-estimation. By adjusting the noise covariance matrix in real time and assigning low weights to the outlier observations using the Huber weight function, the Dynamic Bearing–Angle algorithm is able to effectively cope with non-Gaussian noise and sudden target maneuvers. We validate the performance of the proposed algorithm with numerical simulations and real sensor data, and the results show that the Dynamic Bearing–Angle maintains good robustness and accuracy under different noise intensities.

Estimating the relative position and velocity of micro aerial vehicles (MAVs) using visual signals is a critical issue in numerous tasks. However, traditional relative motion estimation algorithms suffer severely from non-Gaussian noise interference and have limited observability, making it difficult to meet the practical requirements of complex dynamic scenarios. To address this dilemma, this paper proposes a Multimodal Decoupled Spatiotemporal Adaptive Network (MDSAN). Designed for air-to-air scenarios, MDSAN achieves high-precision relative pose and velocity estimation of dynamic MAVs while overcoming the observability limitations of traditional algorithms. In detail, MDSAN is collaboratively composed of two core sub-modules: Modality-Specific Convolutional Normalization (MSCN) blocks and Spatiotemporal Adaptive State (STAS) blocks. Specifically, MSCN uses custom convolution kernels tailored to three modalities—visual, physical, and geometric—to separate their features. This prevents interference between modalities and reduces non-Gaussian noise. STAS, built on a state-space model, combines two key functions: it tracks long-term MAV motion trends over time and strengthens the synergy between different modal features across space. Adaptive weights balance these two functions, enabling stable estimation, even when traditional methods struggle with low observability. Furthermore, MDSAN adopts a full-vision multimodal fusion scheme, completely eliminating the dependence on wireless communication and reducing hardware costs. Extensive experimental results demonstrate that MDSAN achieves the best performance in all scenarios, significantly outperforming existing motion estimation algorithms. It provides a new technical path that balances high precision, high robustness, and cost-effectiveness for technologies such as MAV swarm perception.

Short-wave communication, operating within the frequency range of 3–30 MHz, is extensively employed for long-distance communication because of its extended propagation range and robustness. The ionosphere undergoes complex transformations when influenced by the geomagnetic field, evolving into an uneven and anisotropic electromagnetic medium. This complex property makes the transmission of electromagnetic fields within the ionosphere extremely complex, posing significant challenges for accurately evaluating electromagnetic scattering phenomena. To address the aforementioned challenges, this paper proposes a new method for calculating short-wave ionospheric scattering based on a complex anisotropic multilayer medium transmission matrix. Firstly, by utilizing the characteristic changes of ionospheric electron density with height, the ionization layer is divided into multiple horizontal thin layers, each with an approximately uniform electron density, forming a multilayer horizontal anisotropic structure. Subsequently, the scattering characteristics of electromagnetic waves in the ionosphere were calculated using the transmission matrix approach. The results calculated using this method are consistent with actual measurement values and superior to traditional short-wave ionospheric transmission calculation methods.

Limited computational resources prevent GNSS-based passive radar systems from processing all accessible signals, necessitating intelligent signal selection for efficient target tracking. This paper proposes an adaptive selection method based on Rényi divergence. Within the Cardinality Balanced Multi-Bernoulli (CBMeMBer) filter framework, the method establishes an optimization model that maximizes the expected information gain under a fixed signal-number constraint. To comprehensively validate performance, simulations are conducted under three scenarios: multi-target linear motion, single-target tracking (for comparison with the classical Geometric Dilution of Precision (GDOP) criterion), and multi-target nonlinear maneuvering. Results demonstrate that the proposed algorithm significantly reduces computational load while achieving tracking accuracy superior to random selection and comparable to using all satellites. Compared to the GDOP-based method, it exhibits improved steady-state tracking accuracy by leveraging its dynamic, information-driven selection mechanism. This work provides an effective solution for intelligent resource management in resource-constrained GNSS-based passive radar systems.