Abstract
The complexity and computational complexity of the radar receiver signal processing are mainly concentrated on the signal sorting process. The precondition of radar signal sorting is to extract signal features and then to select key features for sorting. This paper discusses several aspects from feature extraction technology and feature selection technology.
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
Xiong, J., Wang, W.Q., Cui, C., Gao, K.D.: Cognitive FDA-MIMO radar for LPI transmit beamforming. IET J. Mag. 11(11), 1574–1580 (2017)
Murthy, C.A.: Bridging feature selection and extraction: compound feature generation. IEEE Trans. Knowl. Data Eng. 29(4), 757–770 (2017)
Han, N., Song, Y., Song, Z.: Bayesian robust principal component analysis with structured sparse component. Comput. Stat. Data Anal. 109, 144–158 (2017)
Huang, S., Yang, D., Zhou, J., Zhang, X.H.: Graph regularized linear discriminant analysis and its generalization. Pattern Anal. Appl. 18(3), 639–650 (2015)
Chen, L.F., Liao, H.Y.M., Ko, M.T., Lin, J.C., Yu, G.J.: A new LDA-based face recognition system which can solve the small sample size. Pattern Recogn. 33, 1713–1726 (2000)
Zhang, J., Liu, Y.: SVM decision boundary based discriminative subspace induction. Pattern Recogn. 38(10), 1746–1758 (2005)
Hild II, K.E., Erdogmus, D., Tokkola, K., Principe, J.C.: Feature extraction using information-theoretic learning. IEEE Trans. PAMI 28(9), 1385–1392 (2006)
Zhu, M., Matinez, A.M.: Subclass discriminant analysis. IEEE Trans. PAMI 28(8), 1274–1286 (2006)
He, X., Yan, S., Hu, Y., Zhang, H.J.: Learning a locality preserving subspace for visual recognition. In: Proceedings of 9th ICCV, Nice, France, pp. 385–392 (2003)
Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding and extension: a general framework for dimensionality reduction. IEEE Trans. PAMI 29(1), 40–51 (2007)
Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5), 1299–1319 (1998)
Yang, J., Frangi, A.F., Yang, J.Y., et al.: KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition. IEEE Trans. PAMI 27(2), 238–244 (2005)
Huang, N.E.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. J. Proc. R. Soc. Lond. A. 454, 903–995 (1998)
Li, H.G., Hu, Y., Li, F.C., Meng, G.: Succinct and fast empirical mode decomposition. Mech. Syst. Signal Process. 85, 879–895 (2017)
Wang, J., Xu, H.P., Wei, J.M.: Feature selection via vectorizing feature’s discriminative information. Lect. Notes Comput. Sci. 9931(1), 493–505 (2016)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Kohavi, R., Tohu, G.: Wrappers for feature selection. Artif. Intell. 97(1–2), 273–324 (1997)
Tibshirani, R.: Regression selection and shrinkage via the lasso. J. Roy. Stat. Soc. Ser. B 58(1), 267–288 (1996)
Bi, J., Bennett, K., Embrecht, M., Breneman, C., Song, M.: Dimensionality reduction via sparse support vector machines. J. Mach. Learn. Res. 3, 1229–1243 (2003)
Viola, P., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of CVPR 2001, Hawaii, vol. 1, pp. 511–518 (2001)
Bins, J., Draper, B.A.: Feature selection from huge feature sets. In: Proceedings of 8th ICCV, vol. 2, pp. 159–165 (2001)
Kudo, M., Sklansky, J.: Comparison of algorithms that select features for pattern classifiers. Pattern Recogn. 33(1), 25–41 (2000)
Battiti, R.: Using mutual information for selecting features in supervised neural net learning. IEEE Trans. Neural Networks 5(4), 537–550 (1994)
Pawlak, Z.: Rough sets. Int. J. Comput. Inform. Sci. 11, 341–356 (1982)
Jia, X.Y., Shang, L., Zhou, B., Yao, Y.Y.: Generalized attribute reduct in rough set theory. Knowl.-Based Syst. 91, 204–218 (2016)
Zhang, G.X., Hu, L.Z., Jin, W.D.: Discretization of continuous attributes in rough set theory and its application. Lect. Notes Comput. Sci. 3314, 1020–1026 (2004)
Acknowledgements
This paper was supported in part by the National Natural Science Foundation of China under the Grant no. 61601499, 61701527, and 61601503.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Sq., Gao, C., Zhang, Q., Zeng, Hy., Bai, J. (2020). Research Progress on Key Technologies of Radar Signal Sorting. In: Jain, V., Patnaik, S., Popențiu Vlădicescu, F., Sethi, I. (eds) Recent Trends in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-9406-5_92
Download citation
DOI: https://doi.org/10.1007/978-981-13-9406-5_92
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9405-8
Online ISBN: 978-981-13-9406-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)