Solar eruptive events could affect radio communication, global positioning systems, and some high-tech equipment in space. Active regions on the Sun are the main source regions of solar eruptive events. Therefore, the automatic detection of active regions is important not only for routine observation, but also for the solar activity forecast. At present, active regions are manually or automatically extracted by using traditional image processing techniques. Because active regions dynamically evolve, it is not easy to design a suitable feature extractor. In this paper, we first overview the commonly used methods for active region detection currently. Then, two representative object detection models, faster R-CNN and YOLO V3, are employed to learn the characteristics of active regions, and finally establish a deep learning–based detection model of active regions. The performance evaluation demonstrates that the high accuracy of active region detection is achieved by both the two models. In addition, YOLO V3 is $4\%$ and $1\%$ better than faster R-CNN in terms of true positive (TP) and true negative (TN) indexes, respectively; meanwhile, the former is eight times faster than the latter.

The accuracy of space environmental warning information is the basic support for the response mitigation to impact. There are multiple environmental events before and after the solar eruption of the X9.3 megaflare, which has caused many satellite anomalies. In this case, the matching between the warning information and the occurrence levels of four space environmental events was investigated using the analysis methods of statistical analysis and decision tree. The results show that there is a significant correlation between high-energy electron storm events and high-Earth-orbit satellite anomalies, and the accuracy could achieve 67%. Other kinds of space environmental events are weakly related to satellite anomalies. The decision tree evaluation method has advantages in the comprehensive statistical analysis of long-term anomalies data and can effectively reduce the bias introduced by individual cases of solar events. The tree model using multi-environment parameters will be an effective way to reduce false positive rates, which can provide diagnostic methods for medium- and long-term space environment risks in orbit spacecraft in the future.

Combining the anomalies and environmental monitoring data of the early stage Beidou satellites, the space radiation environment spatial/temporal distribution and space radiation effect risk characteristics are analyzed, and the dynamic characteristics of space environmental factors, such as hot plasma, high-energy electron and solar proton are summarized. The systematic joint monitoring for surface charging, internal charging, single event effect and total dose effect with corresponding space environment factors is proposed. The detector types, measurement parameters range and engineering constraints are allocated, and the engineering application and scientific research of detection data are prospected.

The small number of pixels in the current linear mode avalanche photodiode (LM-APD) array limits its three-dimensional (3D) imaging resolution. We use an optical phased array-based beam array subdivided pixel method to improve the 3D imaging resolution, using an optical phased array to generate a beam array with the same number of pixels as the LM-APD array and matching positions and controlling each sub-beam in the beam array to scan in the field of view of the corresponding pixel. The sub-beam divergence angle in the beam array is smaller than the instantaneous field of view angle of a single pixel in the LM-APD array. The sub-beam scanning in a single pixel’s field of view realizes the multiple acquisition of the target 3D information by the LM-APD array, thus improving the resolution of the LM-APD array. The distribution of the beam array in the far field is simulated, and the main performance parameters of 3D imaging are analyzed. Finally, a liquid crystal phase modulator is used as an optical phased array device to conduct experiments on a target 20 m away, and the results prove that our method can improve the resolution from 4 × 4 to 8 × 8, which can be improved at least four times.

Geomagnetic storms are one of the space weather events. The radio signals transmitted by modern navigation systems suffer from the effects of magnetic storms, which can degrade the performance of the whole system. In this study, the performance of the BeiDou Navigation Satellite System (BDS) B1 frequency standard point positioning (SPP) in China and the surrounding area during different classes of storm was investigated for the first time. The statistical analysis of the results revealed that the accuracy of the BDS-2 B1 frequency SPP deteriorated during the storms. The probability of the extrema of the positioning error statistics was largest during strong storms, followed by moderate and weak storms. The positioning accuracy for storms of a similar class was found not to be at the same level. The root mean square error in positioning for the different classes of storm could be at least tens of centimeters in the east, north and up directions. The findings in this study could contribute toward the error constraint of BDS positioning accuracy during different classes of geomagnetic storm and be beneficial to other systems, such as BDS-3, as well.

Article Guest Editorial: Special Issue of New Technology for Space Environment Detection Based on Beidou Constellation was published on January 1, 2023 in the journal Open Astronomy (volume 32, issue 1).