Robust Data Fusion of UAV Navigation Measurements with Application to the Landing System

Abstract

To perform precise approach and landing concerning an aircraft in automatic mode, local airfield-based landing systems are used. For joint processing of measurements of the onboard inertial navigation systems (INS), altimeters and local landing systems, the Kalman filter is usually used. The application of the quadratic criterion in the Kalman filter entails the well-known problem of high sensitivity of the estimate to anomalous measurement errors. During the automatic approach phase, abnormal navigation errors can lead to disaster, so the data fusion algorithm must automatically identify and isolate abnormal measurements. This paper presents a recurrent filtering algorithm that is resistant to anomalous errors in measurements and considers its application in the data fusion problem for landing system measurements with onboard sensor measurements—INS and altimeters. The robustness of the estimate is achieved through the combined use of the least modulus method and the Kalman filter. To detect and isolate failures the chi-square criterion is used. It makes possible the customization of the algorithm in accordance with the requirements for false alarm probability and the alarm missing probability. Testing results of the robust filtering algorithm are given both for synthesized data and for real measurements.

Problems and prospects for the development of unmanned aerial vehicles in Russia

“…As of 2022, they are undergoing a revolutionary change. Russian researchers Kolosov K., Miller A., Miller B., believe that "The integration of data from various sensors is fundamental in the navigation of unmanned aerial vehicles [7]. The use of various sensors provides a comprehensive integrated observation of all-round objects.…”

“…This paper reviews and studies the works of foreign authors who study the possibility of introducing unmanned aerial vehicles in various fields of activity. A significant number of works are devoted to studies in the field of UAV infrastructure development, environmental and economic impact [1][2][3][4][5][6][7][8][9][10]. In the works of Ravil I. Mukhamediev, Adilkhan Symagulov, Yan Kuchin detail the variations of UAV application, listing and describing many options for their use, as well as describing the momentum from the introduction of drones in different areas [2].…”

“…Kirill Kolosov, Alexander Miller, Boris Miller describe the possibility of integrating drones with existing airfields and airports, as well as the use of a unified navigation system to avoid collisions [7]. Nourhan Elmeseiry, Nancy Alshaer, Tawfik Ismail in their work describe in detail the further development of UAV, possible new applications and prospects [9].…”

Robot Navigation Algorithm Based on Real-Time Ranging and Map Matrix

“…At present, the more widely used robot navigation methods include orbital navigation [1][2][3], inertial navigation [4][5], radar and visual navigation [6][7][8], etc. Most intelligent management robots of archives and books use orbits, markers and radar for navigation, such as laying tracks on the bottom of archival shelves, and the robot moves to the target archival shelf with the help of the tracks [9]; He Junjie et al studied a combination of vision and inertial navigation based on two-dimensional raster maps and inertial guidance combination of vision and inertial navigation to move by recognising QR codes in the storage room [10]; Hao Chen et al developed an intelligent book management robot that moves according to ground signs and marker lines, using sensors such as laser ranging and ultrasonic waves to achieve obstacle avoidance and line patrol [11][12]; Yan Chehai et al designed a book management robot whose path is identified by The recognition of the path of the library management robot designed by Yan Chehai et al is electromagnetically guided by pre-laying tracks on the ground and marked by installing RFID electronic tags at the locations of path nodes along the path [13]; in addition, the navigation of the robot can also be achieved by using cameras to detect landmarks [14].…”