Modelling temporal variations by polynomial regression for classification of radar tracks

The sampling rate of a radar is often too low to reliably capture the acceleration of moving targets such as birds. Moreover, the sampling rate depends upon the target´s acceleration and heading and will therefore generally be time varying. When classifying radar tracks using temporal features, too low or highly varying sampling rates deteriorates the classifier´s performance. In this work, we propose to model the temporal variations of the target´s speed by low-order polynomial regression. Using the polynomial we obtain the conditional statistics of the targets speed at some future time given its speed at the current time. When used in a classifier based on Gaussian mixture models and with real radar data, it is shown that the inclusions of conditional statistics describing the targets temporal variations, leads to a substantial improvement in the overall classification performance.