Digital Signal Processing Reference
In-Depth Information
Table 1. The correct rate of recognition under different fighters number
Target number
1
2
3
4
correct rate
1
100
0
0
0
100%
2
0
100
0
0
100%
3
0
6
91
3
91%
4
0
6
11
81
81%
SNR
=
10
dB
and the formation target is 1 or 2
fighters, the correct rate of recognition is 100%; when the formation target is 4
fighters, the correct rate falls, which is caused by the complexity of the signal
structure of the target. But even so, the correct rate has reached more than 80%. It
indicates that the proposed algorithm based on HHT not only have a good recognition
for the formation target that contains less fighters, but also is adaptable for the
formation target that contains more fighters.
Table 1 shows that when
6
Conclusions
A target number recognition method for low-resolution radar based on Hilbert-Huang
transform is proposed in the paper. The simulation test shows that the method has
better anti-noise performance and multi-target recognition ability, which has
applicable value in engineering project.
Acknowledgments. This paper was supported by Province Science Foundation of
Jiangsu ( No. BK2011837 ).
References
1. Lizhong: Wireless Network and Communication Signal Processing. Intelligent Automation
and Soft Computing 1, 1019-1021 (2011)
2. Huibin, W.: An approach for target detection and extraction based on biological vision.
Intelligent Automation and Soft Computing 17, 909-921 (2011)
3. Ding Xiaofeng, X.L.: Robust Visual Object Tracking Using Covariance Features in Quasi-
Monte Carlo Filter. Intelligent Automation and Soft Computing 17, 571-582 (2011)
4. Jiang, Z.: A new method of resolving multiple targets of low resolution radar. Presented at
National Aerospace and Electronics Conference, NAECON 2000. Proceedings of the IEEE
2000, Dayton, OH (2000)
5. Wood, J.C., Barry, D.T.: Linear signal synthesis using the Radon-Wigner transform. IEEE
Transactions on Signal Processing 42, 2105-2111 (1994)
6. Wang, M., Chan, A.K., Chui, C.K.: Linear frequency-modulated signal detection using
Radon-ambiguity transform. IEEE Transactions on Signal Processing 46, 571-586 (1998)
7. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung,
C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear
and non-stationary time series analysis. Proceedings of the Royal Society of London. Series
A: Mathematical, Physical and Engineering Sciences 454, 903-995 (1998)
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