This paper proposes a non-common-view axis alignment method for the alignment requirements of airborne laser communication systems. The system consists of a ground transmitting end and an airborne relay terminal. The ground transmitting end uses a camera and a pan-tilt for image tracking, while the airborne relay end uses a two-dimensional mirror to control the beam to achieve non-common-view axis alignment between the transmitting and receiving sides. The working principle and process of both the transmitter and receiver of the non-common-view axis alignment system for airborne laser communication were compared with traditional wireless optical alignment methods. The design process of the two-dimensional mirror used in this paper is introduced, the scanning trajectory of the two-dimensional mirror is simulated and analyzed according to the beam scanning principle, and the field experiment link is set up to carry out the airborne laser communication experiment. The experimental results show that when the link distance is 10 m, the tracking errors of the system in the azimuth and pitch directions are 19.02 µrad and 22.35 µrad respectively, and the amplitude of the electrical signal output by the signal detector is 84.0 mV; When the link distance is 20 m, the tracking errors of the system in the azimuth and pitch directions are 39.66 µrad and 33.94 µrad respectively, and the amplitude of the electrical signal output by the signal detector is 23.0 mV. Using this method, the alignment can be completed without data return, and the establishment of the reverse link can also be realized while the transmission link is quickly established, and there is no need for an air stability platform. The feasibility of the application of the non-common-view axis alignment method to the airborne laser communication system is verified.

Aiming at the problem of large positioning errors in the boundary area, a new location fingerprint location method based on a fusion clustering algorithm is proposed. This clustering-based method embodies the idea of rough location first and then fine location. Firstly, the edge regions of the received signal strength (RSS) samples which are greatly affected by reflection are divided using the k-medoids algorithm, and then the center part is clustered via density-based spatial clustering of applications with noise (DBSCAN). In the actual location estimation stage, the points to be measured can only be located in one of the classified areas, and combined with the optimal k-nearest neighbor algorithm (WOKNN) to match the location. The results show that the average positioning error of the algorithm is 13 cm in an indoor environment of 5 m × 5 m × 3 m. Compared with the traditional method without clustering, the positioning accuracy of the edge area is increased by 21%, and the overall improvement is 33.8%, which proves that the proposed algorithm effectively improves the efficiency of real-time positioning and indoor positioning accuracy.