Dictionary Learning and Waveform Design for Dense False Target Jamming Suppression
Keywords:
Anti-jamming, dense false target jamming, dictionary learning, jamming detection, waveform designAbstract
For linear frequency modulation (LFM) pulse radars, dense false targets generated by new system jamming seriously damage the performance of such radar systems. In order to avoid the influence of dense false target jamming, an anti-jamming strategy combining waveform design and sparse decomposition are proposed. Specifically, the radar system transmits a random pulse initial phase (RPIP) signal, and uses peak detection method to detect the deception jamming. The phase distribution of the RPIP signal is partially randomly perturbed for a jamming, and we use optimization algorithm to design a phase perturbed LFM (PPLFM) signal with good autocorrelation characteristics. Using the correlation function of the designed signal, the target sample set and the jamming sample set are constructed, and the target echo and the jamming signal are separated using designed dictionary learning method to achieve suppression of dense false target jamming and range side-lobes. The effectiveness of the proposed method is verified by numerical simulation, and the results proved that this proposed method maintains good anti-jamming performance under low signal-tonoise ratio (SNR).
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Q. Shi, N. Tai, C. Wang, and N. Yuan, “On deception jamming for countering LFM radar based on periodic 0-phase modulation,” AEU-International Journal of Electronics and Communications, vol. 83, pp. 245-252, Jan. 2018.
K. Olivier, J. E. Cilliers, and M. D. Plessis, “Design and performance of wideband DRFM for radar test and evaluation,” Electronics Letters, vol. 47, no. 14, pp. 824-825, July 2011.
P. Lei, J. Wang, P. Guo, and D. Cai, “Automatic classification of radar targets with micro-motions using entropy segmentation and time-frequency features,” AEU-International Journal of Electronics and Communications, vol. 65, no. 10, pp. 806- 813, Oct. 2011.
N. Tai, H. Han, C. Wang, X. Xu, R. Wu, and Y. Zeng, “An improved multiplication modulation deception jamming method for countering ISAR,” AEU-International Journal of Electronics and Communications, vol. 110, article number: 152853, Oct. 2019.
G. Lu, S. Liao, S. Luo, and B. Tang, “Cancellation of complicated DRFM range false targets via temporal pulse diversity,” Progress in Electromagnetics Research C, vol. 16, pp. 69-84, Sept. 2010.
S. Lu, G. Cui, X. Yu, L. Kong, and X. Yang, “Cognitive radar waveform design against signaldependent modulated jamming,” Progress in Electromagnetics Research B, vol. 80, pp. 59-77, Mar. 2018.
Z. Liu, J. Sui, Z. Wei, and X. Li, “A sparse-driven anti-velocity deception jamming strategy based on pulse-doppler radar with random pulse initial phases,” Sensors (Basel), vol. 18, no. 4, Apr. 2018.
B. Zhou, R. Li, W. Liu, Y. Wang, L. Dai, and Y. Shao, “A BSS-based space-time multi-channel algorithm for complex-jamming suppression,” Digital Signal Processing, vol. 87, pp. 86-103, Apr. 2019.
Y. Lu, M. Li, R. Cao, Z. Wang, and H. Chen, “Jointing time-frequency distribution and compressed sensing for countering smeared spectrum jamming,” Journal of Electronics and Information Technology, vol. 38, no. 12, pp. 3275-3281, Dec. 2016.
G. Cui and L. Kong, “Main lobe jamming suppression for distributed radar via joint blind source separation,” IET Radar, Sonar & Navigation, vol. 13, no. 7, pp. 1189-1199, July 2019.
Y. Li, X. Ying, and B. Tang, “SMSP jamming identification based on Matched Signal transform,” 2011 International Conference on Computational Problem-Solving (ICCP), Chengdu, China, pp. 182-185, Oct. 21-23, 2011.
A. Aubry, A. D. Maio, M. Piezzo, M. M. Naghsh, M. Soltanalian, and P. Stoica, “Cognitive radar waveform design for spectral coexistence in signal-dependent jamming,” 2014 IEEE Radar Conference, Cincinnati, OH, USA, pp. 0474-0478, May 19-23, 2014.
Y. Li, Z. Jiang, W. Shi, X. Han, and B. Chen, “Blocked maximum correntropy criterion algorithm for cluster-sparse system identifications,” IEEE Trans. Circuits Syst. II, Express Briefs, vol. 66, no. 11, pp. 1915-1919, Nov. 2019.
W. Shi, Y. Li, and Y. Wang, “Noise-free maximum correntropy criterion algorithm in non-Gaussian environment,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 67, no. 10, pp. 2224-2228, Oct. 2020.
W. Shi, Y. Li, and B. Chen, “A separable maximum correntropy adaptive algorithm,” IEEE Trans. Circuits Syst. II, Express Briefs, vol. 67, no. 11, pp. 2797-2801, Nov. 2020.
Y. Li, Y. Wang, and T. Jiang, “Norm-adaption penalized least mean square/fourth algorithm for sparse channel estimation,” Signal Processing, vol. 128, pp. 243-251, 2016.
W. Shi, Y. Li, L. Zhao, and X. Liu, “Controllable sparse antenna array for adaptive beamforming,” IEEE Access, vol. 7, pp. 6412-6423, 2019.
X. Huang, Y. Li, Y. V. Zakharow, Y. Li, and B. Chen, “Affine-projection Lorentzian algorithm for vehicle hands-free echo cancellation,” vol. 70, no. 3, pp. 2561-2575, 2021.
T. Liang, Y. Li, W. Xue, Y. Li, and T. Jiang, “Performance and analysis of recursive constrained least Lncosh algorithm under impulsive noises,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 7, pp. 2217-2221, 2021.
T. Liang, Y. Li, Y. V. Zakhrow, W. Xue, and J. Qi, “Constrained least Lncosh adaptive filtering algorithm,” Signal Processing, vol. 183, 2021.
Y. Li and Y. Wang, “Sparse SM-NLMS algorithm based on correntropy criterion,” vol. 52, no. 17, pp. 1461-1463, 2016.
Y. Li, C. Zhang, and S. Wang, “Low-complexity non-uniform penalized affine projection algorithm for sparse system identification,” Circuits, Systems, and Signal Processing, vol. 35, no. 5, pp. 1611- 1624, 2016.
