Picture of athlete cycling

Open Access research with a real impact on health...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

Explore open research content by Physical Activity for Health...

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

Jochumsen, Lars W. and Østergaard, J. and Jensen, Søren H. and Clemente, Carmine and Pedersen, Morten Ø (2016) A recursive kinematic random forest and alpha beta filter classifier for 2D radar tracks. EURASIP Journal on Advances in Signal Processing. ISSN 1110-8657 (In Press)

Text (Jochumsen-etal-EURASIP-2016-A-recursive-kinematic-random-forest-and-alpha-beta)
Jochumsen_etal_EURASIP_2016_A_recursive_kinematic_random_forest_and_alpha_beta.pdf - Accepted Author Manuscript
License: Unspecified

Download (357kB) | Preview


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.