With the development of IoT in smart cities, the electromagnetic environment (EME) in cities is becoming more and more complex. A full understanding of the characteristics of past spectrum resource utilization is the key to improving the efficiency of spectrum management. In order to explore the characteristics of spectrum utilization more comprehensively, this paper designs an EME portrait model. By checking the statistical information of the spectrum data, including changes in the noise floor and channel utilization in each individual wireless service, the correlation between the spectrum and time or space of different channels and the information is merged into a high-dimensional model through consistency transformation to form the EME portrait. The portrait model is not only convenient for storage and retrieval but also beneficial for transfer and expansion, which will become an important foundation for intelligent electromagnetic spectrum management.

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Radio frequency (RF) relative measurement provides an effective means of communication and autonomous navigation for the spacecraft formation flying. Multipath and Doppler are two main factors that affect the accuracy of RF relative measurement. In this study, a framework that exploits joint multipath-Doppler diversity is proposed to reduce multipath errors and improve the measurement accuracy. The proposed framework first utilizes an Extended Kalman Filter (EKF) estimator to estimate the parameters of Doppler and multipath. Different from existing research, the proposed framework reconstructs the received signal according to the estimated parameters, so as to mitigate multipath signals and enhance the direct signal. Numerical results demonstrate that the framework is suitable for both multipath and multipath-Doppler scenarios, and has a significant performance improvement over existing multipath mitigation methods.

Spectrum resources are becoming harder to come by for wireless communications. The spectrum environment map (SEM), which depicts the electromagnetic environment's current state and future trend, is a valuable technique for managing and allocating spectrum resources. Most SEM construction approaches only take static SEMs into account and cannot forecast time‐domain changes and trends of SEMs in dynamic scenes. In this paper, a brand‐new temporal SEM prediction method for the high dynamic spectrum environment is proposed. This method is based on knowledge of radiation source and the optical flow driven by propagation channel models. First, a novel radiation source localization strategy is designed to obtain the radiation source movement information. Then, the optical flow field of the available SEMs is combined with the information regarding radiation source movement. In order to forecast future SEMs, a propagation model driven reconstruction technique is developed. Simulation findings demonstrate how well the suggested strategy is tailored to capture the spatiotemporal correlation of SEMs. This technique performs better than the state‐of‐the‐art in terms of single‐ and multiple‐step SEM predictions.