An efficient fusion based runway landing detection of unmanned aerial vehicles under low visibility condition

Abstract

The efficiency of transportation system has been degraded, due to the environmental factors which cause delay in landing. Automatic Recognition of Target is the dynamic area of research that employs processing of computer for the recognition and detection of targets from the sensor data. The aircraft landing system needs significant attention in landing as well as safety. To overcome these issues, in this thesis, as the first stage of the research work, an effective Morphological fusion based runway landing detection of Unmanned Aerial Vehicles (UAV) under low visibility condition has been employed for the prediction of virtual runway imagery to avoid accident in landing process. For this process, fusion of sensor data of DEM (Digital Elevation Map) Data, Infrared image (IR) and Navigation parameter are used. The performance of this research using Morphological fusion method gives a fused image of the runway prediction for UAVs under poor visibility condition. After obtaining the fused image, it is involved in Region of Interest (ROI) contour tracing process to get the clear location of landing of an UAV without harm. The virtual imaginary newlinemodel has been produced through contour tracing to predict the runway. By this method, less prediction time or setting time has been achieved and the maximum accuracy has been obtained through simulation using MATLAB tool. Then in the second stage of the research work K-means clustering method has been used for the purpose of efficient runway prediction. First, the image is preprocessed through Histogram equalization (HE) enhancement and Gaussian filtering based despeckling. Then the feature extraction has been carried out with the use of Independent Component Analysis (ICA) approach that extracts the best features. This stage is followed by segmentation which is an essential process for the recognition and detection of target. As a result, K-means clustering based segmentation approach has been employed. Finally, the performance analysis has been made by comparing with the ex