Unmanned aerial vehicle (UAV) has become an appealing solution for a wide range of commercial and civilian applications thanks to its high mobility and flexible deployment. Due to the continuous UAV navigation, the channels between UAV based arial base station (BS) and the ground users are subject to the Doppler effect. Meanwhile, when UAV is equipped with massive number of antennas in high frequency band such as mmWave and Tera Hertz (THz), the non-negligible propagation delay across the array aperture would cause beam squint effect. In this paper, we first investigate the channel under both Doppler effect and beam squint effect for UAV communications with large antenna array and high frequency band. Then, we design a gridless compressed sensing (GCS) based channel tracking method, where the high dimension uplink channel can be derived by estimating a few physical parameters such as the direction of arrival (DOA), Doppler shift, and the complex gain information. Besides, with the Doppler shift reciprocity and angular reciprocity, the downlink channel can be derived by only one pilot symbol, which greatly decreases the downlink channel training overhead. Various simulation results are provided to verify the effectiveness of the proposed methods.

Integrated sensing and communication (ISAC) is recognized as a key technology in the multi-UAV-enabled 6G network. In this paper, we propose a simultaneous sensing and communication method for the multi-UAV network with multiple users and targets. Firstly, we investigate the ISAC channel model for the multiple users and targets, which can be determined by the physical parameters between the UAVs and the users or the targets. Then, we design the ISAC signal for the multi-UAV-enabled network and analyze the interference of the downlink signal for the multi-users and the echo signal for the multi-targets, respectively. On this basis, we further propose a multi-target sensing method with multi-UAV cooperation, where angle division multiple access (ADMA) is exploited to decrease the effect of the multi-user and multi-target interference. Finally, various simulation results are provided to verify the effectiveness of the proposed method.

As one of the vital research directions in hyperspectral image (HSI) processing, anomaly detection is dedicated to identifying anomalous pixels in HSIs that have significant spectral differences from the surrounding background, and it has attracted extensive attention from numerous scholars in recent years. Anomaly detectors based on collaborative representation have achieved favorable performance in this field. Based on CRD, scholars have proposed many different variants. However, most of these methods only focus on the spectral information of HSIs, and they suffer from slow detection speed and poor robustness. In this paper, we combine the Extended Multi-Attribute Profile (EMAP) with the CRD algorithm, propose a fast collaborative representation anomaly detection algorithm based on the extended multi-attribute profile. First, we use EMAP to extract the spatial structural information of the HSI. Then, before the anomaly detection, we employ the k-means clustering algorithm to separate anomalous pixels with similar features, and obtain a reconstructed background dictionary matrix. This further separates the background from anomalies and improves the robustness of anomaly detection. Finally, we apply a collaborative representation-based anomaly detector to detect anomalies. The proposed method is compared with other algorithms through experiments on four real HSI datasets and one synthetic HSI dataset. The experimental simulation results verify the effectiveness of our proposed method.

This paper exploits rotation invariance of matrix pencil based on two parallel uniform linear polarization sensitive arrays, and eigenvalues and the corresponding eigenvectors of the matrix pencil are the rotation matrix and time-space-polarization steering vector of the subarray, respectively. The eigenvalues and steering vector can be used to estimate frequency, two-dimensional angles and polarization of incident signal simultaneously without spectral search. This method is of virtual array element and comparatively high resolution. Moreover this method is equal to multi-dimensional ESPRIT that matrix pencils share the same common set of eigenvectors, and two methods exploit the same information such as eigenvalues and eigenvectors of the matrix pencil. The simulation results verify and analyse the performances of two methods