Технологии интеллектуальной обработки информации для задач навигации и управления беспилотными летательными аппаратами

The paper presents the results of research in the field of the development of technol-ogies for processing heterogeneous information from the UAV onboard ma-chine vision system with the aim of UAV navigation and control. The main problems of information processing for UAV navigation and control are considered; general tasks to be solved for mission planning and performing are formulated. The key problems of the unmanned aerial vehicle (UAV) machine vision system are multiband image processing and fusion (both for flight planning and onboard processing), object detection and localization, object tracking, object recognition. Modern methods of object detection, recognition and tracking are analyzed. Advanced techniques and algorithms are compared, and the most effective ones are determined. New original methods are proposed for multiband images fusion based on diffuse morphology. The original methods are developed for deep machine learning, which provide high probabilities of given object detection and recognition. The database of model images for machine learning algorithms is created. The characteristics of the developed algorithms and results of their tests on model and real data are presented.Приведены результаты исследований по разработке технологий обработки разносенсорной информации, получаемой бортовой системой машинного зрения, для решения комплекса задач навигации и управления беспилотным летательным аппаратом (БЛА). Проведен анализ предметной области, выделены основные задачи, требующие решения для эффективного выполнения основных функций БЛА. Предложены оригинальные методы комплексирования, основанные на диффузной морфологии, разработаны методики подготовки обучающих выборок и глубокого машинного обучения, обеспечивающие высокое качество распознавания, создана база данных синтезированных изображений для обучения алгоритмов распознавания

Intrinsic dimension estimation for locally undersampled data

Identifying the minimal number of parameters needed to describe a dataset is a challenging problem known in the literature as intrinsic dimension estimation. All the existing intrinsic dimension estimators are not reliable whenever the dataset is locally undersampled, and this is at the core of the so called curse of dimensionality. Here we introduce a new intrinsic dimension estimator that leverages on simple properties of the tangent space of a manifold and extends the usual correlation integral estimator to alleviate the extreme undersampling problem. Based on this insight, we explore a multiscale generalization of the algorithm that is capable of (i) identifying multiple dimensionalities in a dataset, and (ii) providing accurate estimates of the intrinsic dimension of extremely curved manifolds. We test the method on manifolds generated from global transformations of high-contrast images, relevant for invariant object recognition and considered a challenge for state-of-the-art intrinsic dimension estimators