Spectrum sensing remains a challenge in the context of cognitive radio networks (CRNs). Compared with traditional single-user sensing, cooperative spectrum sensing (CSS) exploits multiuser diversity to overcome channel fading, shadowing, and hidden terminal problems, which can effectively enhance the sensing performance and protect licensed users from harmful interference. However, for a large number of sensing nodes that need high bandwidth of the control channel for data transmitting, CSS increases cooperative overhead. To address this problem, we investigated the soft decision fusion strategy under a limited bandwidth of the control channel and proposed a simple quantization-based multibit data soft fusion rule for CSS for its simple structure and easily implementation. Under the quantization-based sensing strategy, each cooperative secondary user (SU) adopts an energy detector for local spectrum sensing. Each SU transmits quantized multibit data that sends local sensing information, instead of forwarding local one-bit hard decision results or original observation statistics, to the fusion center (FC). Furthermore, the closed-form expressions of the quantization levels and the quantization thresholds are analytically derived. Simulation results indicate that the detection performance of the proposed method approaches that of the conventional soft fusion rule with less cooperative overhead and outperforms the hard decision rules. Extensive simulations also show that multibit quantization fusion achieves a desirable tradeoff between the sensing performance and the control channel overhead for CSS.

Collaborative spectrum sensing as a promising approach is widely investigated for identifying available spectrum in cognitive radio networks. However, such cooperation is vulnerable to be threatened by spectrum sensing data falsification (SSDF) attacks. Many previous attacker detection schemes rely on some strong assumptions, such as prior knowledge of the local performance of each secondary user (SU) and higher attack probability. To overcome these drawbacks, in this article, we develop a Bayesian-inference-based sliding window trust model to identify and weed out independent and collaborative probabilistic SSDF attackers without any prior knowledge of the attack behaviors. Based on this model, the trust value of each SU is obtained by combining the weighted intermediate sensing information from multiple small sliding windows. A sigmoid-log-function-based scheme is introduced to mitigate trust value fluctuations caused by different attacking probabilities. Finally, extensive simulation results show that compared with existing schemes, the proposed approach can identify probabilistic SSDF attackers effectively and isolate them with a higher detection rate across a wide range of attacking scenarios.

Bioradar-based noncontact breathing detection technology has been widely studied due to its superior detection performance. In this paper, a breath detection mechanism based on the change in radar cross section (RCS) is proposed by using a forward scatter radar and the deduction of the mathematical model of the received signal. Furthermore, we completed human breathing detection experiments in an anechoic chamber and in an ordinary chamber; we obtained the breathing rate through envelope detection in cases where the human orientation angle was 0, 30, 60, and 90°. The analysis of the measured data shows that the theoretical model fits well with the measured results. Compared with the existing single-base radar detection schemes, the proposed scheme can detect human respiratory rates in different orientations.

Over the past few years, drones have been utilized in a wide range of applications. However, the illegal operation of drones may pose a series of security risks to sensitive areas such as airports and military bases. Hence, it is vital to develop an effective method of identifying drones to address the above issues. Existing drone classification methods based on radio frequency (RF) signals have low accuracy or a high computational cost. In this paper, we propose a novel RF signal image representation scheme that incorporates a convolutional neural network (CNN), named the frequency domain Gramian Angular Field with a CNN (FDGAF-CNN), to perform drone classification. Specifically, we first compute the time–frequency spectrum of raw RF signals based on short-time Fourier transform (STFT). Then, the 1D frequency spectrum series is encoded as 2D images using a modified GAF transform. Moreover, to further improve the recognition performance, the images obtained from different channels are fused to serve as the input of a CNN classifier. Finally, numerous experiments were conducted on the two available open-source DroneRF and DroneRFa datasets. The experimental results show that the proposed FDGAF-CNN can achieve a relatively high classification accuracy of 98.72% and 98.67% on the above two datasets, respectively, confirming the effectiveness and generalization ability of the proposed method.

Three-dimensional (3D) synthetic aperture radar (SAR) imaging provides complete 3D spatial information, which has been used in environmental monitoring in recent years. Compared with matched filtering (MF) algorithms, the regularization technique can improve image quality. However, due to the substantial computational cost, the existing observation-matrix-based sparse imaging algorithm is difficult to apply to large-scene and 3D reconstructions. Therefore, in this paper, novel 3D sparse reconstruction algorithms with generalized $L_q$-regularization are proposed. First, we combine majorization–minimization (MM) and $L_1$ regularization (MM-$L_1$) to improve SAR image quality. Next, we combine MM and $L_{1/2}$ regularization (MM-$L_{1/2}$) to achieve high-quality 3D images. Then, we present the algorithm which combines MM and $L_0$ regularization (MM-$L_0$) to obtain 3D images. Finally, we present a generalized MM-$L_q$ algorithm (GMM-$L_q$) for sparse SAR imaging problems with arbitrary $q\left(0\le q\le 1\right)$ values. The proposed algorithm can improve the performance of 3D SAR images, compared with existing regularization techniques, and effectively reduce the amount of calculation needed. Additionally, the reconstructed complex image retains the phase information, which makes the reconstructed SAR image still suitable for interferometry applications. Simulation and experimental results verify the effectiveness of the algorithms.

To resist probabilistic spectrum sensing data falsification (SSDF) attacks in cognitive radio networks (CRNs), we in this paper propose a simple and effective attacker identification scheme from the perspective of each secondary user's (SU) historical sensing data without any prior knowledge about the strategy of attackers. In the proposed algorithm, the inconsistency property of the historical data within two consecutive sensing slots during a sensing time window is extracted to characterize different attack behaviors. Further, an optimal identification threshold is obtained for each SU and the analytical expressions of identification performance are also derived. Finally, simulation results along with theoretical analysis show the validity of the proposed scheme to defend against massive probabilistic SSDF attacks with low computation cost.