The security threats caused by the popularity of Unmanned Aerial Vehicles (UAVs) have received much attention. In this paper, a UAV detection and identification system based on WiFi signal and radio frequency (RF) fingerprint is proposed. The system firstly conducts UAV detection and after the UAV is detected, fractal dimension (FD), axially integrated bispectra (AIB), and square integrated bispectra (SIB) are extracted as UAV RF fingerprints due to their applicability and reliability. Furthermore, we propose weighted AIB and SIB fingerprints to identify UAVs. Since the high dimensionality of AIB and SIB features, the principal component analysis (PCA) algorithm is applied to reduce the dimensionality of these two features. Then the neighborhood component analysis (NCA) algorithm is utilized to weight the dimensions of the two features, AIB and SIB. The extracted UAV fingerprints are stored as training data and test data, respectively. Finally, machine learning algorithms are utilized to identify UAVs. The identification results are as follows: In the indoor scenario, the average identification accuracy of three features (FD, AIB, SIB) are 100%, 97.23%, and 96.11%, respectively. In outdoor scenario, the identification accuracy of three features are 100%, 95.00%, and 93.50%, respectively.

Channel state information (CSI) can provide phase and amplitude of multichannel subcarrier to better describe signal propagation characteristics. Therefore, CSI has become one of the most commonly used features in indoor Wi-Fi localization. In addition, compared to the CSI geometric localization method, the CSI fingerprint localization method has the advantages of easy implementation and high accuracy. However, as the scale of the fingerprint database increases, the training cost and processing complexity of CSI fingerprints will also greatly increase. Based on this, this article proposes to combine backpropagation neural network (BPNN) and adaptive genetic algorithm (AGA) with CSI tensor decomposition for indoor Wi-Fi fingerprint localization. Specifically, the tensor decomposition algorithm based on the parallel factor (PARAFAC) analysis model and the alternate least squares (ALSs) iterative algorithm are combined to reduce the interference of the environment. Then, we use the tensor wavelet decomposition algorithm for feature extraction and obtain the CSI fingerprint. Finally, in order to find the optimal weights and thresholds and then obtain the estimated location coordinates, we introduce an AGA to optimize BPNN. The experimental results show that the proposed algorithm has high localization accuracy, while improving the data processing ability and fitting the nonlinear relationship between CSI location fingerprints and location coordinates.

In recent years, unmanned aerial vehicles (UAVs) have received growing attention due to security threats issues. Though UAV detection and positioning systems are commonly used in various scenarios, most of the available systems are still suffering from low accuracy and susceptibility to the environment. Therefore, it is still necessary to design a highly accurate, versatile UAV detection and positioning system. In this paper, a UAV detection and positioning system based on multi-dimensional signal features is proposed. The first step of the system is to monitor the communication signal and channel state information (CSI) between the UAV and the controller. Subsequently the signal frequency spectrum (SFS), the wavelet energy entropy (WEE), and the power spectral entropy (PSE) are extracted as features. At the same time, machine learning algorithms combining the above features are applied to detect UAVs. After the UAV is successfully detected, the spatial features such as angle of azimuth (AOA) and angle of elevation (AOE) were extracted for UAV localization based on a super-resolution estimation algorithm. In conclusion, the Wireless Insite (WI) software was employed for long-distance positioning verification while the software-defined radio (SDR) was used for small-scale testing and verification. The experimental results show that the average detection rate of combining multiple features in the test environment is 95.58%, the median accuracy of 2D positioning is 0.76 m, and the median accuracy of 3D positioning is 1.2 m. In the WI simulation environment, the median accuracy of 2D positioning is 1.1 m, and the median accuracy of 3D positioning is 2.35 m.

With the in-depth development of the Internet of Things (IoT) and the constant discussion of 6G visions, the future development of indoor location-based services (LBSs) in the 6G-enabled IoT is attracting people’s attention. In fact, due to the continuous updating of network techniques, the complexity of the indoor environment and the number of connected wireless access points (APs) will increase dramatically, which leads to the diversity of the received signal strength (RSS) on the performance of signal propagation distance estimation, resulting in low positioning accuracy and poor robustness. In response to this problem, this article proposes a multisource information fusion-based indoor positioning approach via Gaussian kernel density estimation. First, the heuristic distribution model is used to establish the mathematical relationship between the RSS from different Wi-Fi APs and the signal propagation distance, and then the Gaussian kernel density estimation approach is applied to estimate the signal propagation distance distribution. Second, the normalized signal propagation distance distribution is set as the basic probability assignment in the Dempster–Shafer (D–S) evidence theory, and then the multisource RSS information is fused according to D–S evidence synthesis rules. Meanwhile, the trust function synthesized by the D–S evidence theory is used to select matching reference points (RPs). Finally, the fuzzy decision algorithm is used to select the ideal matching RPs from the matching RPs for the positioning. Experimental results show that the proposed approach has higher positioning accuracy and stronger positioning robustness compared to existing ones.

This paper proposes a compact $4 \times 4$ filtering microstrip patch antenna array with Dolph- Chebyshev distribution implemented by multilayer structure. The proposed antenna array features good filtering performance but contains no extra filtering parts in the feeding or the radiating element. The whole antenna array consists of four $1 \times 4$ antenna subarrays, each subarray is composed of four patch antennas which are coupled-fed by a single microstrip line. Inside the desired passband, by properly choosing the coupling area along the microstrip line, the patches are induced in-phase currents therefore achieving the excellent radiation characteristics. On the other hand, the four patches can’t be properly excited outside the passband which would enable radiation suppression performance. In order to implement a $4 \times 4$ filtering microstrip patch antenna array with low sidelobe level (SLL), four subarrays are combined and fed by Dolphy-Chebyshev excitation method. Finally, the proposed antenna array is implemented, fabricated and measured. The maximum measured gain in the passband is 14.7 dBi, and the radiation suppression outside the passband is better than 25 dB.

As one of the key technologies of the 5G, the device-to-device (D2D) can realize communication between terminals without any base station, thus achieving more convenience of cooperative positioning. In this article, we propose a D2D cooperative positioning approach via matrix completion and anchor selection, which tackles the positioning problem with inadequate distance information. Specifically, first, an incomplete Euclidean distance matrix (EDM) is constructed by using insufficient distance information between nodes, and then the singular value thresholding (SVT) algorithm is used to recovery this EDM to obtain completed information. Second, multidimensional scaling (MDS) is performed to reduce dimensions of recovered EDM, which aims to obtain the relative positions of nodes while maintaining the distance relationship among them. Third, a set of suitable anchor nodes is selected by using the Hodges–Lehmann (HL) test for position transformation. Finally, we apply the procrustes analysis (PA) to transform the relative positions to the global ones according to the selected set of suitable anchor nodes. From the extensive experimental results, it is evident that the proposed approach has high positioning accuracy even when a large proportion of elements are missing in the EDM.