In disaster response, 2D wireless sensor networks fail to be deployed since a large number of sensors cannot be placed in harsh ground environments. In this case, as the best air carrier for sensors, unmanned aerial vehicles (UAVs) are always used to construct a 3D flying ad hoc network (FANET) to realize emergency communication. In FANET, however, node localization and data transmission are great challenges due to the increase in network dimensions and the high mobility of UAVs. In this paper, a novel FANET clustering routing protocol based on nodes location is proposed. Firstly, an improved particle swarm optimization (IPSO) algorithm is designed by fusing variable neighborhood search, and IPSO is used to improve the locating precision of UAVs. Furthermore, the base station (BS) clusters UAVs evenly according to nodes location, and the cluster head is determined by weighting the intra-cluster distance and residual energy of nodes. Finally, the optimal next-hop routing is selected by IPSO according to the link reliability and the distance between nodes and the BS to ensure data are transmitted to the BS timely and reliable. The simulation results show that routing protocol based on IPSO realize more efficient performance compared with the existing schemes in terms of positioning indicators, energy consumption, delay, and clustering lifetime.

To exploit the complementary strengths of WiFi positioning, pedestrian dead reckoning (PDR), and landmarks, we propose a novel fusion approach based on an extended Kalman filter (EKF). For WiFi positioning, unlike previous fusion approaches setting measurement noise parameters empirically, we deploy a kernel density estimation-based model to adaptively measure the related measurement noise statistics. Furthermore, a trusted area of WiFi positioning defined by fusion results of previous step and WiFi signal outlier detection are exploited to reduce computational cost and improve WiFi positioning accuracy. For PDR, we integrate a gyroscope, an accelerometer, and a magnetometer to determine the user heading based on another EKF model. To reduce accumulation error of PDR and enable continuous indoor positioning, not only the positioning results but also the heading estimations are recalibrated by indoor landmarks. Experimental results in a realistic indoor environment show that the proposed fusion approach achieves substantial positioning accuracy improvement than individual positioning approaches including PDR and WiFi positioning.

The integrated sensing and communication (ISAC) plays an increasingly pivotal role in numerous emerging applications. In this paper, we focus on the covert transmission of the confidential information within the ISAC system in the presence of multiple wardens, i.e., non-colluding and colluding scenarios. Specifically, we first develop the theoretical detection error probability (DEP) under the strategy of the separate detection and the maximal ratio combining (MRC) based cooperative detection, respectively. Secondly, we propose the robust beamforming optimization model comprised of the beampattern mean square error (MSE) based sensing metric and the Kullback-Leibler (KL) divergence based covertness measure under the sensing assisted imperfect channel state information (CSI) of wardens. In order to tackle the non-convexity of the proposed optimization problem, we propose the iterative algorithms comprised of the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques to achieve the robust beamforming design for the covert ISAC system under the CSI uncertainty. Due to the significant large complexity of the robust beamforming design for colluding wardens, we further propose the low complexity optimization algorithm based on the monotonicity of the theoretical DEP for the MRC based detection instead of KL divergence. Simulation results demonstrate the superior performance of proposed algorithms in terms of the average covert rate and the DEP over other baseline schemes under various simulation settings.

UAVs are flexible in action, changeable in shooting angles, and complex and changeable in the shooting environment. Most of the existing stitching algorithms are suitable for images collected by UAVs in static environments, but the images are in fact being captured dynamically, especially in low-altitude flights. Considering that the great changes of the object position may cause the low-altitude aerial images to be affected by the moving foreground during stitching, so as to result in quality problems, such as splicing misalignment and tearing, a UAV aerial image stitching algorithm is proposed based on semantic segmentation and ORB. In the image registration, the algorithm introduces a semantic segmentation network to separate the foreground and background of the image and obtains the foreground semantic information. At the same time, it uses the quadtree decomposition idea and the classical ORB algorithm to extract feature points. By comparing the feature point information with the foreground semantic information, the foreground feature points can be deleted to realize feature point matching. Based on the accurate image registration, the image stitching and fusion will be achieved by the homography matrix and the weighted fusion algorithm. The proposed algorithm not only preserves the details of the original image, but also improves the four objective data points of information entropy, average gradient, peak signal-to-noise ratio and root mean square error. It can solve the problem of splicing misalignment tearing during background stitching caused by dynamic foreground and improves the stitching quality of UAV low-altitude aerial images.

A desirable fingerprint-based indoor localization (FIL) system aims to achieve an accurate positioning result within an acceptable time consumption, which is still challenging for application. Building a practical FIL system is a composite task of feature extraction and location estimation, resulting in related methods that is often hard to consider both the positioning accuracy and time consumption. This article proposes a novel FIL system that uses a combination of distance metric learning (DML) and access point (AP) selection method to tradeoff the positioning accuracy and time consumption. Specially, we first abstract the localization process to develop a mathematical model from the perspective of probability theory and reveal the significant impact of the received signal strength (RSS) similarity comparison on FIL. Then, we propose a perturbation theory-based AP selection method to select the best-position-discrimination AP subset from all to reduce the positioning time consumption. Meanwhile, we propose a DML-based method to extract the RSS distribution which involves the indoor environmental information, and further use it in RSS fingerprint similarity comparison to improve the positioning accuracy. We introduce the signal path-loss model into the proposed method for training to get the best similarity metric function. Finally, experimental results show that both the positioning accuracy and the time consumption are comparatively improved in the online phase by the proposed FIL system.

Vision-based indoor positioning technology is a practical and effective method to solve the problem of indoor positioning and navigation. Compared to Bluetooth-based and WiFi-based positioning methods, vision-based positioning method can provide reliable and low-cost services using a camera without extra pre-deployed hardware. To improve the robustness and accuracy of traditional visual positioning algorithm, this paper proposes a pixel threshold based eight-point method and an improved epipolar constraint algorithm. The traditional eight-point method only uses Euclidean distance as a selection indicator for feature points. The pixel coordinates of some feature points are distorted when the positioning scene changes, which may cause mismatch. The proposed method introduces the pixel threshold constraint to improve the quality of output feature points. Further, the epipolar constraint algorithm is modified by adding a new cost function to improve the accuracy of fundamental matrix calculation, thereby improving the positioning precision. Performance simulation analysis shows that the proposed algorithm can effectively improve indoor positioning precision.