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

Remote Sensing

Kirill Kolosov¹  Boris Miller¹  Alexander Miller¹ 

[1] Institute for Information Transmission Problems RAS, Bolshoy Karetny per. 19, Build.1, 127051 Moscow, Russia;

关键词: automatic landing;    data fusion;    Kalman filter;    least modulus method;    L₁ optimization;    M estimate;   

DOI  :  10.3390/rs12233849

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown