Research On Key Technologies In Visual-based Aircraft Docking Automatic Guided

The implementation of aircraft docking auto-guide to improve airport the level of informationand automation is essential, a method based on visual docking auto-guide because theinformation-rich, intuitive effects and low cost has been subject to the attention of scholars at homeand abroad. Image pre-processing and identification and tracking of the various aircraft in differentweather conditions restrict multifaceted uncertain factors, so the difficulty of visual docking guidanceand key technologies, thisarticle focuses on four aspects of work.Firstly, the conducting depth studies of image preprocessing algorithms of image enhancementand denoising in special weather conditions. In order to better adjust the contrast of image, thepiecewise linear transformation algorithm is proposed, which is simple, fast operation, to meet thereal-time requirements of the system. For fog weather, a novel improved edge detection method isproposed based on dark color priority and morphology defogging algorithm, which improves theclarity of the goal, preserves image detail edge to natural and realistic defogging, while meeting thereal-time and automatic requirements.Secondly, the segmentation of guided objects is researched. In edge detection, a new adaptiveweighted edge detection algorithm is proposed which takes into account the details of the edge andnoise removal. In the segmentation, taking into account the shadow of a moving object is marked forthe prospects to increase object tracking difficulty, matching the complexity of the algorithm as wellas posture evaluation algorithm, proposed a power transformation of the morphology of andborderless active contour model combined with the shadow segmentation algorithm, which not onlyfilters noise and preserves image details, improves the shadow segmentation precision, but alsoreduces the times of iteration. It also exploits the potential that active edgeless contour can be used inimages with complex backgrounds. Consequently preferably solve docking aircraft image objectpartition question.Thirdly, for the features extraction and recognition of aircraft objects, this article analyzes theinvariant moments of rotation, translation, scale invariance, a fast and effective identification methodis proposed which apply weighted morphological to be preprocessing simultaneous extraction of thecharacteristics of the aircraft, to solve the problem of aircraft models to match. Morphologicalfeatures in the feature extraction to remove image noise, improved recognition accuracy andefficiency, to achieve the robustness, the neural network model identification to solve the problem in the docking system, and achieved good results and accuracy of low visibility automatic dockingguided system.Finally, this paper analyzed the advantages and disadvantages of the MS and the traditionalparticle filter algorithms, an improved particle filter tracking algorithm is proposed to accurately trackthe docking aircraft Algorithm uses incremental self-adjustment and posture estimation and allowsparticles to move at the optimum direction, it solves the loss of diversity in the resampling process.The algorithm realized the small number of sample particles and capable of "smart" to find theoptimal state, the experiments show that the algorithm has good robustness.Simulation results show that: the proposed algorithms for the detection and model identificationand tracking of visual docking guidance system have significantly improved; there is a high value inengineering.