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

Joint target tracking and classification with particle filtering and mixture Kalman filtering using kinematic radar information

This paper considers the problem of joint maneuvering target tracking and classification. Based on recently proposed Monte Carlo techniques, a multiple model (MM) particle filter and a mixture Kalman filter (MKF) are designed for two-class identification of air targets: commercial and military aircraft. The classification task is carried out by processing radar measurements only, no class (feature) measurements are used. A speed likelihood function for each class is defined using a prior information about speed constraints. Class-dependent speed likelihoods are calculated through the state estimates of each class-dependent tracker. They are combined with the kinematic measurement likelihoods in order to improve the classification process. The two designed estimators are compared and evaluated over rather complex target scenarios. The results demonstrate the usefulness of the proposed scheme for the incorporation of additional speed information. Both filters illustrate the opportunity of the particle filtering and mixture Kalman filtering to incorporate constraints in a natural way, providing reliable tracking and correct classification. Future observations contain valuable information about the current state of the dynamic systems. In the framework of the MKF, an algorithm for delayed estimation is designed for improving the current modal state estimate. It is used as an additional, more reliable information in resolving complicated classification situations

Mejoras en el estudio y predicción de los campos de viento locales, especialmente en entornos aeroportuarios, con importante afectación a la seguridad del tráfico aéreo = Improvements in the study and prediction of local wind fields, particularly in airport surroundings, with implication in air traffic safety

197 p.El viento es un elemento esencial en la climatología. La predicción y la medición de los campos de vientos son los dos aspectos clave en torno a los cuales pivota nuestro conocimiento de los mismos, la predicción de vientos se realiza fundamentalmente mediante técnicas de cálculo numérico, con un modelo númerico modificado se han realizado nuevos ensayos de validación, pasando posteriormente a la realización de las simulaciones globales para el entorno del Aeropuerto de Leó