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

• Published in: EURASIP journal on advances in signal processing Vol. 2016; no. 1; pp. 1 - 12
• Main Authors: Jochumsen, Lars W., Østergaard, Jan, Jensen, Søren H., Clemente, Carmine, Ø. Pedersen, Morten
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 28.07.2016; Springer; Springer Nature B.V
• Subjects: Advanced Techniques for Radar Signal Processing; Classification; Classifiers; Doppler effect; Engineering; High speed; Kinematics; Quantum Information Technology; Radar; Signal,Image and Speech Processing; Spintronics; Two dimensional; Uncertainty; Kinematic; Random forest; Alpha beta filter; Radar; Classification
• ISSN: 1687-6180; 1687-6172; 1687-6180
• DOI: 10.1186/s13634-016-0378-3

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 implicitly 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 at 79.7 %. Additional to the confusion matrix, we also show recordings of real-world data.