Abstract. Since image-based 3D model reconstruction can faithfully recover the real texture of cultural heritage with high accuracy, it is widely used in cultural heritage documentation. Given the complexity of manual image acquisition at present, we propose an object-oriented unmanned aerial vehicle (UAV) path planning method to obtain close-up and high-resolution images for the 3D reconstruction of cultural heritage. Four basic geometric classes are defined and can be automatically divided or interactively defined on the surface of an initial coarse model. We propose the concept of aerial strip unit in conventional photogrammetry to generate multiple regular strip units for photography. The optimal flight path connecting each unit is generated considering the obstacle avoidance and the shortest distance. Based on the self-developed 3D engine, we take the Ancient City of Ping Yao and Yellow Crane Tower in China as two cases to design the UAV 3D path planning. Experimental results show that, compared with general planning methods, our method can improve the flight efficiency of UAV and the visual fineness of the reconstruction results.

Abstract. Although Global Navigation Satellite System (GNSS) has achieved success in outdoor localization, it does not often work well in urban canyon, which is due to the weak signals and the loss of satellites. WiFi technology is widely used at present, and the crowdsourced WiFi data has the advantages of rich sources and low cost. Therefore, utilizing the crowdsourced WiFi data for localization may effectively improve the deficiency of GNSS in the urban canyon. In this paper, we propose a novel method of crowdsourced WiFi fingerprint localization in urban canyon. Considering that the crowdsourced data is noisy, discontinuous and unstable, we carry out pre-processes for data refining, and grid-based statistical method for noise smoothing. Then in order to quickly locate the terminals in large-scale area, the AP coverage intersection method is proposed, in which the coverage range, centers and density of all APs are inferred, and the personal hotspots as well as the mobile APs are removed. To further enhance the positioning accuracy, the fine localization is carried out, which is based on the iterative KWNN algorithm. Extensive field tests are carried out in a typical urban canyon, results show that the average positioning error of our method is 16.82 m, which shows the effectiveness of the proposed method for crowdsourced positioning in urban canyon.

Decapitation strike is an important part of modern war, which could achieve significant effect at a small cost. With the rapid improvement of unmanned aerial vehicle (UAV) swarm's ability to carry out reconnaissance and strike missions, this combat mission can be performed by UAV swarm instead of ground special forces or fighters. Based on the analysis of mission profiling, this article establishes the mission model and threat area model of decapitation strike. Then, combining Bézier curve with receding planning framework, an Optimized model for UAV swarm path planning under the condition of full coverage antiaircraft firepower is proposed. The simulation results show that the model and improved algorithm in this paper can effectively solve the path planning problem of UAV swarm in the face of full coverage threat. The research can make up for the defects of the traditional "Cross Gaps" path planning algorithm in solving the problem of crossing threat areas, and provide effective guidance for UAV swarm to perform the decapitation missions.