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

Lars W Jochumsen,Carmine Clemente,Søren H Jensen,Morten Ø Pedersen,Jan Østergaard

doi:10.1186/s13634-016-0378-3

Lars W Jochumsen, Carmine Clemente + Show 3 more

Open Access

https://doi.org/10.1186/s13634-016-0378-3

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Abstract

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.

Highlights

The increasing demand for protection and surveillance of the coastal areas requires modern coastal surveillance radars
The difference between false and unwanted objects is that false objects do not originate from true objects but are mainly noise objects, whereas the unwanted objects originate from true objects but are unwanted in the surveillance image
These objects depend on the purpose of the radar; for coastal surveillance radars, the unwanted objects are normally birds, wakes from large ships, etc

Summary

Introduction

The increasing demand for protection and surveillance of the coastal areas requires modern coastal surveillance radars. The random forest is a bagging classifier [9] where multiple decision trees are used to reduce the variance of the classification results. For this reason, random forest is selected in this work. The random forest is a bagging algorithm, which means that the random forest consists of a number of weak classifiers [12], which has zero bias but high variance of the true value. A decision tree classifies the data by following a path through each node. We explain how we obtained (5) from the random forest to achieve a recursive update of the probability for the class given all the measurements. The reason for applying such a filter is to classify stationary targets, which have a high apparent speed due to measurement uncertainties

Alpha beta filter

Features

Discussion

Conclusions