Pseudo-Zernike moments based radar micro-Doppler classification

Reliable micro-Doppler signature classification requires the use of robust features describing uniquely the micromotion. Moreover, future applications of micro-Doppler classification will require meaningful representation of the observed target by using a limited set of values. In this paper the application of the pseudo-Zernike moments for micro-Doppler classification is introduced demonstrating the effectiveness of the proposed approach by classifying real data. The use of pseudo-Zernike moments allows invariant features to be obtained that are able to discriminate the content of two-dimensional matrices with a small number of coefficients.

[1]  B. Bondzulic,et al.  Feature Extraction Related to Target Classification for a Radar Doppler Echoes , 2010 .

[2]  C.J. Baker,et al.  Template Based Micro-Doppler Signature Classification , 2006, 2006 European Radar Conference.

[3]  J.-M. Nicolas,et al.  Micro-Doppler analysis of wheels and pedestrians in ISAR imaging , 2008 .

[4]  M. Teague.  Image analysis via the general theory of moments , 1980 .

[5]  Blockin,et al.  !∀#∃% Blockin &∋ ( Blockin Correspondence Robust Micro-doppler Classification Using Svm on Embedded Systems , 2022 .

[6]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[7]  S. Bjorklund,et al.  Evaluation of a micro-Doppler classification method on mm-wave data , 2012, 2012 IEEE Radar Conference.

[8]  A. Bhatia,et al.  On the circle polynomials of Zernike and related orthogonal sets , 1954, Mathematical Proceedings of the Cambridge Philosophical Society.

[9]  K. Egiazarian,et al.  Classification of ground moving radar targets by using joint time-frequency analysis , 2012, 2012 IEEE Radar Conference.

[10]  H. Wechsler,et al.  Micro-Doppler effect in radar: phenomenon, model, and simulation study , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[12]  Milenko S. Andric,et al.  The database of radar echoes from various targets with spectral analysis , 2010, 10th Symposium on Neural Network Applications in Electrical Engineering.

[13]  Karl Woodbridge,et al.  Radar Micro-Doppler Signature Classification using Dynamic Time Warping , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[14]  Carmine Clemente,et al.  Robust PCA micro-doppler classification using SVM on embedded systems , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[15]  Carmine Clemente,et al.  Developments in target micro-Doppler signatures analysis: radar imaging, ultrasound and through-the-wall radar , 2013, EURASIP J. Adv. Signal Process..

[16]  L. Cohen,et al.  Time-frequency distributions-a review , 1989, Proc. IEEE.