With the development of modern electromagnetic stealth technology and ARM, traditional active radar detection cannot accomplish its detection mission, limited by its ability. Relying on such superior advantages such as imperceptibility, anti-electromagnetic interference and electromagnetic stealth, passive transducers are playing an indispensable and significant role in situation awareness. While, in addition to different passive transducer localization modes and solutions of target’s location, the reasonable planning and optimal layout of passive transducers’ location are other major factors affecting the precision of localization. Planning an optimal airspace for passive transducers is the key problem to improve the monitoring efficiency. This paper proposes the optimal layout algorithm for the cooperative platform in the space based on the geometrical relationship of cooperative localization. For example, the principle of direction location in traditional methods is simple: only two passive sensors can work, but the location accuracy of long-distance targets is low. At the same time, TDOA (Time Difference Of Arrival) location has high accuracy and good stability, but it needs at least three passive sensors to work together, which requires the most resources. In this paper, a platform optimization layout algorithm based on direction and TDOA hybrid positioning is proposed. Compared with direction positioning, it improves the long-distance positioning accuracy, reduces the number of sensors required for TDOA positioning, and reduces the resource occupancy rate. However, the TDOA positioning data mixed with direction positioning data inevitably leads to the decline of overall accuracy. In order to solve these difficulties, the weighted least square method is used to optimize the accuracy. The simulation shows that, within the designated target airspace, the optimal action airspace can be generated automatically based on the platforms’ cooperation mode. If there is no resource limitation, the airspace planning based on TDOA positioning has the highest accuracy for the target. However, in practical application, considering the resource limitation, the hybrid positioning of direction and TDOA can also meet the requirements of high accuracy and high stability. The average error is reduced by more than 45% compared with direction positioning, and the airspace occupancy is reduced by more than 30% compared with TDOA positioning. The goal of minimizing the scope of platform airspace planning is realized.

Global navigation satellite systems (GNSS), integrated with an inertial measurement unit (IMU) and visual sensors, are widely used for vehicular navigation. With the advancement of emerging vehicular technologies, the performance requirements for positioning, navigation, and timing (PNT) have become critical, emphasizing not only positioning accuracy but also high reliability. However, GNSS signals are susceptible to reflection and diffraction in urban environments, leading to multipath effects, such as non-line-of-sight (NLOS) reception and multipath interference. The GNSS positioning errors will increase significantly, causing the integrated navigation system to fail to meet the high-performance navigation requirements. To address this issue, we have proposed a robust GNSS/IMU/visual odometry (VO) fusion algorithm with a new GNSS weighting model and an adaptive VO velocity measurement update algorithm for urban navigation. In particular, a multipath inflation factor, based on real-time IMU and VO data, is proposed for the GNSS weighting model to mitigate multipath effects. A VO variance attenuation factor based on zero velocity detection in a robust extended Kalman filter (REKF) is also designed to adaptively adjust the covariance of VO measurements, enhancing the overall robustness of the integrated system. A field test was conducted in urban environments. The results show that the proposed algorithm achieves horizontal and 3-D positioning accuracy of 3.38 m and 5.00 m, respectively, outperforming the conventional GNSS/IMU/VO integration using C/N0-based weighting model. The improvements in horizontal and 3-D positioning accuracy are 63.4% and 56.1%, respectively.

In a closed environment lacking global positioning system (GPS) signals, how to achieve accurate navigation and positioning is a very challenging task. Zero velocity update (ZUPT) is a highly effective foot-mounted inertial pedestrian navigation systems in such environment. However, despite its effectiveness, the limitation of accurate detecting the zero-velocity-interval (ZVI) and heading drift are still the significant challenges of the ZUPT method. To address these issues, a deep learning method for adaptive ZVIs detection is established based solely on inertial sensors by comparing with the optical motion capture system. Additionally, an improved ZUPT-aided extend Kalman filter (EKF) divides the measurement updates of the ZVIs is established for multisensor data fusion, and the heading change with heuristic drift reduction (HDR) is also adopt as measurement, thereby yielding to limit the heading drift. Experimental results demonstrate that our method provides a better estimate of the heading angle, as well as more accurate ZVIs detection, leading to more precise dead-reckoning position estimates than other state-of-the-art methods.

Unmanned aerial vehicle (UAV) imagery detection faces challenges in preserving small object features during multi-level downsampling, handling angle and altitude-dependent variations in aerial scenes, achieving accurate localization in dense environments, and performing real-time detection. To address these limitations, we propose HSF-DETR, a lightweight transformer-based detector specifically designed for UAV imagery. First, we design a hybrid progressive fusion network (HPFNet) as the backbone, which adaptively modulates receptive fields to capture multi-scale information while preserving fine-grained details critical for small object detection. Second, building upon features extracted by HPFNet, we develop MultiScaleNet, which enhances feature representation through dual-layer optimization and cross-domain feature learning, significantly improving the model’s capability to handle complex aerial scenarios with diverse object orientations. Finally, to address spatial–semantic alignment challenges, we devise a position-aware align context and spatial tuning (PACST) module that ensures effective feature calibration through precise alignment and adaptive fusion across scales. This hierarchical architecture is complemented by our novel AdaptDist-IoU loss with dynamic weight allocation, which enhances localization accuracy, particularly in dense environments. Extensive experiments using standard detection metrics (mAP50 and mAP50:95) on the VisDrone2019 test dataset demonstrate that HSF-DETR achieves superior performance with 0.428 mAP50 (+5.4%) and 0.253 mAP50:95 (+4%) when compared with RT-DETR, while maintaining real-time inference (69.3 FPS) on an NVIDIA RTX 4090D GPU with only 15.24M parameters and 63.6 GFLOPs. Further validation across multiple public remote sensing datasets confirms the robust generalization capability of HSF-DETR in diverse aerial scenarios, offering a practical solution for resource-constrained UAV applications where both detection quality and processing speed are crucial.

In recent years, Low Earth Orbit (LEO) satellite Signals of Opportunity (SOP) positioning technology has emerged a significant alternative to the Global Navigation Satellite System (GNSS), effectively addressing navigation and positioning challenges in GNSS-denied environments. Among various LEO constellations, Iridium NEXT has garnered considerable attention due to its well-developed constellation architecture and extensive coverage. To achieve high-precision positioning using Iridium NEXT SOP, it is crucial to fully analyze both time-domain and frequency-domain observation data. Existing time-frequency joint estimation methods for Iridium NEXT signals typically employ a grid-based search strategy, which suffers from low accuracy and high computational complexity. Additionally, the high dynamic of LEO satellites introduces frequency shifts that significantly degrade bit synchronization accuracy and reduce the precision of signal estimation. To address these challenges, this paper proposes a joint high-precision frequency and code phase estimation algorithm. By incorporating a phase adjustment strategy during the bit synchronization stage, it mitigates the impact of satellite dynamics on signal estimation, enhances frequency estimation accuracy, and provides a precise synchronization sequences for code phase estimation. Additionally, leveraging the Early Minus Late Amplitude (EMLA) principle, the algorithm achieves efficient and accurate code phase estimation. It further integrates Frequency of Arrival (FOA) measurements for positioning, ensuring the effective utilization of SOPs to improve positioning accuracy. Experimental signal analysis demonstrates that the proposed algorithm achieves a frequency measurement error of approximately 1Hz, approaching the Cramér-Rao Lower Bound (CRLB). For both long-frame and short-frame signals, the accuracy is 25 m and 37 m, respectively, outperforming existing Time of Arrival (TOA) estimation methods for Iridium NEXT signals. Furthermore, in a 176m baseline differential scenario, Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) fusion positioning accuracy reaches 10m, compared to existing frequency estimation methods, the proposed approach improves FDOA-based positioning accuracy 74%.