合成孔径雷达射频干扰抑制技术进展及展望

射频干扰(RFI)是影响合成孔径雷达精确遥感的一个难点问题,严重阻碍了原始回波的采集、成像和后续解译过程。本文对适用于合成孔径雷达系统的RFI抑制技术进行了综述研究。首先分析了典型的星载和地面RFI源以及其对合成孔径雷达系统的影响,给出了典型RFI类型的信号模型和实测数据示例;然后系统地介绍了抑制RFI的先进信号处理技术,并从适用性的角度讨论了每种方法的优缺点;最后从认知、综合和自适应的角度探讨了未来干扰抑制技术的发展趋势与展望。     

动态跳波束策略中的自适应波束功率分配算法

摘要：多波束卫星发射机的成本很高，同时地面小区的通信需求和通信优先级一直处在动态变化中。通过研究一种动态波束跳跃策略，实现在卫星资源受限和较少发射机数量约束下的广域覆盖和按需服务。区别于传统的分簇波束跳变思想，为了提高卫星系统的频谱效率，考虑在整个频率带宽上应用全局波束跳变。因此考虑在共信道干扰背景下，提出一种新型服务质量指标来衡量波束跳变结果。在卫星资源有限的情况下，各波束提供的通信容量不能满足地面小区的业务请求，基于粒子群算法选择的波束跳动图案，提出了一种自适应波束功率分配算法。该算法通过优先级加权，最小化波束业务容量需求差值，进而提高服务质量水平。最后，通过仿真验证了所提算法的性能优越性。     

Local empirical model of ionospheric variability

The paper presents an analysis of the ionospheric variability as a function of local time, month, and geomagnetic activity level. The 2003–2020 dataset of peak electron densities (NmF2) from the Irkutsk DPS-4 Digisonde (52.3°N, 104.3°E) was converted into the dataset of the NmF2 disturbances (ΔNmF2) representing the relative (percentage) deviations of the NmF2 from the 27-day running median. The ΔNmF2 dataset was used to calculate root mean square values of ΔNmF2 (σNmF2) by 27-day running averaging. These σNmF2 values were considered as a measure of ionospheric variability. The σNmF2 as function of local time, day of year, and year was the input for building the local empirical model of ionospheric variability based on the linear regression of σNmF2 on the 27-day average daily Ap-index of geomagnetic activity. The paper demonstrates the diurnal-seasonal variations in σNmF2 under low geomagnetic activity (linear regression intercept) as well as the rate of increase/decrease in σNmF2 with increasing Ap (linear regression slope). The obtained diurnal, seasonal, and geomagnetic activity behavior of σNmF2 is compared with previous studies of ionospheric variability.