A recursive kinematic random forest and alpha beta filter classifier for 2D radar tracks

In this work, we show that by using a recursive random forest together with an alpha beta filter classifier it is possible to classify radar tracks from the tracks’ kinematic data. The kinematic data is from a 2D scanning radar without Doppler or height information. We use random forest as this classifier implicit handles the uncertainty in the position measurements. As stationary targets can have an apparently high speed because of the measurement uncertainty, we use an alpha beta filter classifier to classify stationary targets from moving targets. We show an overall classification rate from simulated data at 82.6 % and from real world data 79.7 %. Additional to the confusion matrix we also show recordings of real world data

Radar discrimination of small airborne targets through kinematic features and machine learning

This work studies binary classification problem for small airborne targets (drones vs other) by means of their trajectory analysis. For this purpose a set of the kinematic features extracted from drone trajectories using radar detections with a classification scheme that utilises Random Forests is proposed. The development is based on experimental data acquired from the Holographic radar from Aveillant Ltd. An approach for real-time classification is proposed, where an adaptive sliding window procedure is employed to make predictions over time from trajectories. Several models utilising different kinematic features (angle, slope, velocity, and their combination) are studied. The best model achieves an accuracy of more than 95%. In addition, fundamental issues with imbalanced datasets in the context of this topic are raised and illustrated using the collected data

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“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements

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