Modelling temporal variations by polynomial regression for classification of radar tracks. Jochumsen, L. W., Østergaard, J., Jensen, S. H., & Pedersen, M. Ø. In *2014 22nd European Signal Processing Conference (EUSIPCO)*, pages 1412-1416, Sep., 2014.

Paper abstract bibtex

Paper abstract bibtex

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.

@InProceedings{6952502, author = {L. W. Jochumsen and J. Østergaard and S. H. Jensen and M. Ø. Pedersen}, booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)}, title = {Modelling temporal variations by polynomial regression for classification of radar tracks}, year = {2014}, pages = {1412-1416}, abstract = {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.}, keywords = {Gaussian processes;mixture models;polynomials;radar tracking;regression analysis;signal classification;target tracking;Gaussian mixture model;conditional statistics;target heading;moving target acceleration;sampling rate;radar track classification;polynomial regression;temporal variation modelling;Radar tracking;Target tracking;Birds;Marine vehicles;Acceleration;Radar cross-sections;Automatic target classification;Machine learning;Radar;Surveillance}, issn = {2076-1465}, month = {Sep.}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569924707.pdf}, }

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