Abstract
Here we propose a method to classify radar clutter from radar data using an unsupervised classification algorithm. The data will be represented by Positive Definite Hermitian Toeplitz matrices and clustered using the Fisher metric. Once the clustering algorithm dispose of a large radar database, new radars will be able to use the experience of other radars, which will improve their performances: learning radar clutter can be used to fix some false alarm rate created by strong echoes coming from hail, rain, waves, mountains, cities; it will also improve the detectability of slow moving targets, like drones, which can be hidden in the clutter, flying close to the landform.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jeuris, B., Vandrebril, R.: The Kähler mean of Block-Toeplitz matrices with Toeplitz structured blocks (2016)
Chevallier, E., Forget, T., Barbaresco, F., Angulo, J.: Kernel Density Estimation on the Siegel Space with an Application to Radar Processing. Entropy (2016)
Haykin, S.: Adaptive Filter Theory. Pearson (2014)
Arnaudon, M., Barbaresco, F., Yang, L.: Riemannian medians and means with applications to radar signal processing. IEEE J. 7(4), 595–604 (2013)
Barbaresco, F.: Super resolution spectrum analysis regularization: burg, capon and AGO-antagonistic algorithms. In: EUSIPCO 1996, Trieste, Italy, pp. 2005–2008 (1996)
Barbaresco, F.: Information geometry of covariance matrix: cartan-siegel homogeneous bounded domains, mostow/berger fibration and fréchet median. In: Nielsen, F., Bhatia, R. (eds.) Matrix Information Geometry, pp. 199–256. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30232-9_9
Deza, M.M., Deza, E.: Encyclopedia of Distances. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-52844-0. ISBN 978-3-662-52844-0. http://www.springer.com/us/book/9783662528433
Bini, D., Iannazzo, B., Jeuris, B., Vandebril, R.: Geometric means of structured matrices. BIT 54(1), 55–83 (2014)
Barrie Billingsley, J.: Low-Angle Radar Land Clutter, Measurements and Empirical Models. William Andrew Publishing, Norwich (2002)
Greco, M.S., Gini, F.: Radar Clutter Modeling
Arnaudon, M., Barbaresco, F., Yang, L.: Riemannian medians and means with applications to radar signal processing. IEEE Trans. Sig. Proc.
Decurninge, A., Barbaresco, F.: Robust burg estimation of radar scatter matrix for mixtures of gaussian stationary autoregressive vectors. IET Radar Sonar Navig. 11(1), 78–89 (2016)
Barbaresco, F., Forget, T., Chevallier, E., Angulo, J.: Doppler spectrum segmentation of radar sea clutter by mean-shift and information geometry metric (2017)
Barbaresco, F.: Radar micro-doppler signal encoding in siegel unit poly-disk for machine learning in fisher metric space. In: IRS 2018, Bonn, June 2018
Acknowledgments
We thank the French MoD DGA MRIS for funding (convention CIFRE \(N^{\circ } 2017.0008\)).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Cabanes, Y., Barbaresco, F., Arnaudon, M., Bigot, J. (2019). Toeplitz Hermitian Positive Definite Matrix Machine Learning Based on Fisher Metric. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2019. Lecture Notes in Computer Science(), vol 11712. Springer, Cham. https://doi.org/10.1007/978-3-030-26980-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-26980-7_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26979-1
Online ISBN: 978-3-030-26980-7
eBook Packages: Computer ScienceComputer Science (R0)