Cooperative spectrum sensing (CSS) has emerged as a promising solution to identify available spectrum resources in cognitive wireless sensor networks (CWSNs). However, such an open collaboration paradigm may suffer Byzantine attack from malicious sensors, leading to a dramatic decline in cooperative performance, meanwhile, in a large CWSN, the CSS process causes significant communication overhead in the common control channel to report local measurements, which limits the cooperative efficiency. On this account, a less sample-CSS, weighted sequential detection (WSD), is proposed to prevent large-scale Byzantine attack. First, the channel state information is used to verify the robustness of the global decision made by the fusion center and evaluate the trust value of sensors. Furthermore, the weight of each sensing sample related to trust value is integrated into a sequential detection within a time window. Finally, a sequential approach in a trust value descending order is formulated. A series of numerical simulation results show that compared with alternative fusion rules for CSS, the advantages of the proposed WSD in terms of the error probability and the average number of samples are evident in the presence of a large-scale Byzantine attack.

The surrounding environment of a GNSS observation station is changed during a flood, and this results in a more serious multipath than in a normal environment. Considering that the multipath error is largely related to the pseudo-range multipath and CNR (Carrier-to-noise ratio) of the GNSS signal, the influence of floods on a pseudo-range multipath and CNR is analyzed in theory and through experiment. To ensure the accuracy of the analysis results, the ground track repeat period of GPS, GLONASS, and BDS satellites is investigated from the perspective of theory and skyplots. Two real cases study collected in Zhengzhou and Xinxiang, China, in 2021, are used to demonstrate the influence of floods on a pseudo-range multipath and CNR in detail. Experimental results show that the pseudo-range multipath of a GPS satellite performs more seriously during a flood. The maximum RMS increase rate is approximately 17.85%, and the average of all other satellites with a whole arc is approximately 6.55%. In addition, the CNR of three GNSS systems performs a decrease during a flood. For GPS and GLONASS satellites, the decrease performs more seriously at a high elevation angle than that at a low elevation angle. The maximum decrease is approximately 5 dB-Hz for the GPS satellite and approximately 7 dB-Hz for the GLONASS satellite. In terms of the BDS system, the CNR of all three orbital type satellites decreases during a flood. The average decrease is approximately 2 dB-Hz for BDS MEO and GEO satellites, and about 1 dB-Hz for the BDS IGSO satellite.

The Wuhan Ionospheric Oblique Backscatter Sounding System (WIOBSS) was applied as a bistatic radar to record the ionospheric E-region responses to a solar eclipse on 22 July 2009. The transmitter was located in Wuhan and the receiver was located in Huaian. The receiver observed anomalous echoes with larger Doppler shifts at the farther ranges compared with the echoes reflected by Es. According to the simulated ray propagation paths of the reflected and scattered waves, we considered that the anomalous echoes were scattered by E-region field-aligned irregularities (FAIs). The locations of the FAIs recorded by the WIOBSS were estimated with the International Geomagnetic Reference Field (IGRF) and the observed propagation parameters. These irregularities occurred at around the eclipse maximum and lasted for ~20–40 min. The steep plasma density gradient induced by the fast drop photo ionization under the lunar shadow was beneficial to the occurrence of gradient drift instability to generate the FAIs. They were different from the gravity wave-induced irregularities occurring in the recovery phase of the solar eclipse.

The low-altitude quasi-periodic (LQP) E-region echoes observed with the upgraded Hainan COherent scatter Phased Array Radar (HCOPAR) located in Hainan Island of China (19.5°N, 109.1°E) are presented. With the interferometry technique, interferometry equations were developed to investigate the structures of the irregularities. The results show that the plasma structures of the LQP echoes with a negative slope mainly drifted southwestward almost horizontally, but they also drifted southeastward in some striations. According to the movement of the plasma structures, we found strong wind shear in the region in which the LQP echoes were produced. Meanwhile, the periodicity of the LQP echoes may have been related to the strength of the wind shear. It is proposed that the plasma structures were most likely produced by the Kelvin–Helmholtz instability (KHI).

Due to the rapid development of unmanned aerial vehicles (UAVs) communication technology, UAVs are gradually competing with primary users (PUs) for spectrum resources. Cognitive radio (CR) is a promising solution to meet the needs. Cooperative spectrum sensing (CSS) is considered as an effective method to detect the PU signal and identify available spectrum resources for UAVs in a cognitive UAV network (CUAVN). However, the cooperative mode among multiple UAVs may incur a high overhead, resulting in performance degradation. Therefore, we introduce a differential sequential 1 (DS1), which incorporates a differential mechanism and leverages the sequential idea based on the classical voting rule to enhance cooperative performance and efficiency. In view of this, we formulate three scenarios to characterize the PU activity and introduce a multi-slot cooperative mode within a single UAV to realize cooperative gain. Further, only the information change about the PU status is sequentially calculated in DS1, and combined with a sequential idea, the efficiency of the voting rule is greatly improved. Moreover, the application of support vector machines (SVM) in dynamic selection enables the selection of the most suitable voting rule based on diverse sensing parameters. This dynamic selection process ensures optimal performance and efficiency by adapting the voting rule to the specific characteristics of the given scenario. Finally, simulation results demonstrate that the superiority of our proposal with respect to the detection performance, sample size and the energy efficiency is evident, which proves the high performance of the proposed policy.

Publicly available ionograms are often in the form of pictures. This paper proposes a novel algorithm for extracting and repairing the F layer traces from pictorial ionograms. Extensive efforts have been invested in ionogram autoscaling and critical parameter identification to improve the efficiency of scaling algorithms. To obtain the parameters of the F layer automatically, it is necessary to accurately extract the F layer trace. However, research on F layer trace extraction with repair is relatively limited. The method employed in this study makes full use of the characteristics of different types of echoes on the ionograms, and the procedure includes noise preprocessing, coupling noise processing, and trace repair. To enhance the applicability of the repair, two different automatic filling algorithms are adopted to repair the F layer trace. The aim of this paper is to present an adaptive algorithm to automatically extract and repair F layer traces from different pictorial ionograms. The results of Hainan Fuke ionograms illustrate the reliability of the F layer trace extraction and trace repair.