In order to improve the safety needs of personnel in the dark environment under the well, this article adopts the improved YOLOV8 algorithm combined with the ray method to determine whether underground personnel are entering dangerous areas and to provide early warning. First of all, this article introduces the coordinate attention mechanism on the basis of YOLOV8 target detection so that the model pays attention to the location information of the target area so as to improve the detection accuracy of obstruction and small target areas. In addition, the Soft-Non-Maximum Suppression (SNMS) module is introduced to further improve accuracy. The improved model is then combined with the ray method to be deployed and applied under a variety of angles and different scenic information cameras. The experimental results show that the proposed method obtains 99.5% of the identification accuracy and a frame speed of 45 Frames Per Second (FPS) on the self-built dataset. Compared with the YOLOV8 model, it has a higher accuracy and can effectively cope with the changes and interference factors in the underground environment. Further, it meets the requirements for real-time testing in dangerous underground areas.

The spaceborne transmitter/missile-borne receiver (ST/MR) synthetic aperture radar (SAR) could provide several unique advantages, such as wide coverage, unrestricted geography, a small detection probability of the missile, and forward-looking imaging. However, it is also accompanied by problems in imaging, including geometric model establishment and focusing algorithm design. In this paper, an ST/MR SAR model is first presented and then the flight-path constraint, characterized by geometric configurations, is derived. Considering the impacts brought about by the maneuvers of the missile, a non-‘Stop-Go’ mathematical model is devised and it can avoid the large errors introduced by the acceleration, which is neglected in the traditional model. Finally, a two-dimensional (2-D) scaling algorithm is developed to focus the ST/MR data. Without introducing any extra operations, it can greatly remove the spatial variations of the range, azimuth, and cross-coupling phases simultaneously and entirely in the 2-D hybrid domain. Simulation results verify the effectiveness of the proposed model and focusing approach.

With the increasing requirement for resolution, the negligence of topography variations causes serious phase errors, which leads to the degradation of the focusing quality of the synthetic aperture (SAR) imagery, and geometric distortion. Hence, a precise and fast algorithm is necessary for high-resolution airborne SAR. In this paper, an extended back-projection (EBP) algorithm is proposed to compensate the phase errors caused by topography variations. Three-dimensional (3D) variation will be processed in the time-domain for high-resolution airborne SAR. Firstly, the quadratic phase error (QPE) brought by topography variations is analyzed in detail for high-resolution airborne SAR. Then, the key operation, a time-frequency rotation operation, is applied to decrease the samplings in the azimuth time-domain. Just like the time-frequency rotation of the conventional two-step approach, this key operation can compress data in an azimuth time-domain and it reduces the computational burden of the conventional back-projection algorithm, which is applied lastly in the time-domain processing. The results of the simulations validate that the proposed algorithm, including frequency-domain processing and time-domain processing can obtain good focusing performance. At the same time, it has strong practicability with a small amount of computation, compared with the conventional algorithm.

As an important supplement to traditional airborne synthetic aperture radar (SAR), multirotor unmanned aerial vehicle (UAV) SAR has advantages of low cost, high flexibility, and strong survival ability. However, due to its complex motion and flight characteristics of the multirotor UAV platform, multirotor UAV SAR faces challenges including the spatially-variant low-frequency (LF) errors and severe high-frequency (HF) errors. To deal with these problems, an improved motion compensation approach is proposed for multirotor UAV SAR imaging, which is implemented through two processing steps: (i) The LF errors are eliminated by an improved two-step MoCo approach, which takes into account the spatial variations of both envelope and phase, (ii) The HF errors are estimated and corrected by an extended phase gradient autofocus scheme. Different from the conventional solutions, our approach can effectively remove the complex motion errors of multirotor UAV SAR step-by-step with high robustness even in high-resolution scenarios. Computer simulation and experimental results verify the effectiveness of our approach.

For unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) imaging, motion errors cannot be obtained accurately when high precision motion sensors are not equipped on the platform. This means that traditional data-based motion compensation (MOCO) cannot be directly implemented due to large measurement errors. In addition, classic autofocusing techniques, such as phase gradient autofocus (PGA) or map-drift algorithm (MDA), do not perform well with spatially variant errors, greatly affecting the imaging qualities, especially for high-resolution and large-swath cases. In this study, an improved spatially variant MOCO approach based on an MDA is developed to effectively eliminate the spatially variant errors. Based on the coarse and precise MDA chirp rate error estimation, motion errors are optimally acquired by the random sample consensus (RANSAC) iteration. Two-dimensional (2D) mapping is used to decouple the spatially variant residual errors into two linear independent dimensions so that the chirp-z transform (CZT) can be performed for echo data correction. Unlike traditional approaches, the spatially variant components can be compensated without any measured motion information, which indicates that the proposed approach can be applied to the common UAV SAR system with significant measurement errors. Simulations and real data experiments were used to evaluate the performance of the proposed method. The simulation results show that the proposed algorithm is able to effectively minimize spatially variant errors and generate much better imaging results.