A new concept of global navigation based on Doppler measurements from a large low Earth orbit (LEO) constellation is investigated that has potential to serve as a complement or backup to global navigation satellite systems (GNSSs) to provide navigation and positioning services in GNSS denial environments. In this work, we investigate the potential of LEO communication satellite opportunity signals in dynamic navigation, establish LEO satellite Doppler positioning equations, derive the main error sources affecting the Doppler positioning results of a dynamic target, and analyze the impact of each error source on positioning accuracy through a simulation. The results show that the orbit error and clock drift had a large impact on positioning accuracy. This navigation scheme would be more competitive if it could provide high-precision satellite orbits and accurate Doppler measurements. The obtained results show that the LEO satellite signals used as navigation opportunity signals are an attractive alternative in GNSS rejection environments for high dynamic targets.

In Global Navigation Satellite System (GNSS)-denied environments, opportunistic positioning using non-cooperative Low Earth Orbit (LEO) satellite signals has shown strong potential. However, dynamic platforms face challenges in maintaining sufficient satellite counts and favorable geometric distributions due to limited signal quality and short observation windows. To address this, we propose a fast satellite selection algorithm based on the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA) for dynamic, multi-constellation LEO opportunistic navigation. By introducing Pareto non-dominated solutions, the algorithm balances Doppler Geometric Dilution of Precision (DGDOP), signal strength, residual visibility time, and receiver sensitivity. Through iterative optimization, it constructs a subset of satellites with minimal DGDOP while reducing computational burden, enabling real-time fusion and switching at the receiver end. We validate the algorithm through UAV-based experiments in dynamic scenarios. Compared to GWO, PSO, and NSGA-II, the proposed method achieves computation time reductions of 27.06%, 27.05%, and 68.57%, respectively. It also reduces the overall navigation solution time to 54.96% of that required when using all visible satellites, significantly enhancing real-time responsiveness and system robustness. These results demonstrate that the NSWOA-based satellite selection algorithm outperforms existing intelligent methods in both computational efficiency and optimization accuracy, making it well-suited for real-time, multi-constellation LEO dynamic opportunistic navigation.

Remote sensing change detection (RSCD), a fundamental task in Earth observation, aims to automatically identify land-cover changes (e.g., building construction, vegetation degradation) by comparing multitemporal satellite or aerial images of the same region. With the explosive growth of high-resolution remote sensing data, achieving real-time accurate change detection on edge computing devices (e.g., drone-embedded chips, satellite on-board processors) has become an urgent challenge—existing deep learning methods, despite high accuracy, are hindered by massive parameters and computational costs that preclude deployment on resource-constrained embedded hardware. To address this, we focus on lightweight (i.e., low parameter count and low computational cost) RSCD network design, targeting three critical bottlenecks: blurred boundaries of changed regions, missed detection of small objects, and insufficient computational efficiency. We propose ATCFNet (Adjacent-Temporal Cross Fusion Network), featuring a three-step progressive feature optimization strategy: (1) the Adjacent Feature Aggregation Module (AFAM) enhances shallow geometric details via lateral three-stage fusion to compensate for lightweight backbones; (2) the Temporal Attention Cross Module (TACM) integrates cross-level feature propagation and Convolutional Block Attention Module (CBAM) for collaborative optimization of high-level semantics and low-level details; and (3) the Efficient Guidance Module (EGM) establishes long-range dependencies using shared change priors and lightweight self-attention to suppress internal voids in changed regions. Experiments on three public datasets (LEVIR-CD, HRCUS, SYSU-ChangeDet) demonstrate that ATCFNet achieves state-of-the-art accuracy with merely 3.71 million (M) parameters and 3.0 billion (G) floating-point operations (FLOPs)—F1-scores of 91.46%, 77.05%, and 83.53%, significantly outperforming 18 existing methods in most indicators. Notably, it excels in edge integrity (avoiding jagged blurring at change boundaries) and small-target detection in high-resolution urban scenes. This study provides an efficient and reliable lightweight solution for edge computing scenarios such as real-time drone inspection and satellite on-board intelligent processing.

Against the backdrop of complex features and spectral heterogeneity in high-resolution remote sensing imagery, traditional methods suffer from insufficient semantic understanding, while existing vision–language change detection models face low efficiency, poor spatial localization, and decoupled detection–description pipelines. To overcome these limitations, this paper proposes ChangeVLM, a language-guided semantic alignment framework for binary remote sensing change detection, enabling end-to-end, prompt-free, highly efficient, and interpretable change detection. Its key advantages include the following, (1) Higher detection accuracy with F1 scores of 91.52%, 83.56%, and 75.29% on LEVIR-CD, SYSU-ChangeDet, and HRCUS datasets, outperforming 18 state-of-the-art methods. (2) Stronger edge integrity and small-object detection capability; (3) practical deployment efficiency: the end-to-end FLOPs is 560.7G. Additionally, under an optimized inference setting with pre-extracted features, the effective computation can be reduced to 13.05G. (4) Language-guided semantic regularization to enhance visual discrimination, without requiring external text prompts. The Asymmetric Fusion Module (AFM), lightweight ChangeHead, and Change-Aware Cross-Modal Fusion Module (CACMF) jointly enhance spatial precision, efficiency, and interpretability. Extensive experiments validate that ChangeVLM achieves a superior accuracy–efficiency trade-off. This method provides an effective, deployable solution for high-resolution remote sensing binary change detection, where the language branch acts only as a regularization signal.

The threat posed by unauthorized drones to public airspace has become increasingly critical. To address the challenge of UAV detection in unaligned visible–infrared dual-spectral images, we present a novel framework that comprises two sequential stages: image alignment and object detection. The Speeded-Up Robust Features (SURF) algorithm is applied for feature matching, combined with the gray centroid method to remove mismatched feature points. A plane-adaptive pixel remapping algorithm is further developed to achieve images fusion. In addition, an enhanced YOLOv11 model with a modified loss function is employed to achieve robust object detection in the fused images. Experimental results demonstrate that the proposed method enables precise pixel-level dual-spectrum fusion and reliable UAV detection under diverse and complex conditions.

Self-location plays a crucial role in a framework of autonomous navigation, especially in a GNSS/radio-denied environment. At the current time, self-location for artificial agents still has to resort to the visual and laser technologies in the framework of deep neural networks, which cannot model the environments effectively, especially in some dynamic and complex scenes. Instead, researchers have attempted to transplant the navigation principle of mammals into artificial intelligence (AI) fields. As a kind of mammalian neuron, the grid cells are believed to provide a context-independent spatial metric and update the representation of self-location. By exploiting the mechanism of grid cells, we adopt the oscillatory interference model for location encoding. Furthermore, in the process of location decoding, the capacity of autonomous navigation is extended to a significantly wide range without the phase ambiguity, based on a multi-scale periodic representation mechanism supported by a step-wise phase unwrapping algorithm. Compared with the previous methods, the proposed grid-like self-location can achieve a much wider spatial range without the limitation imposed by the spatial scales of grid cells. It is also able to suppress the phase noise efficiently. The proposed method is validated by simulation results.