Vol. 99
Latest Volume
All Volumes
PIERM 114 [2022] PIERM 113 [2022] PIERM 112 [2022] PIERM 111 [2022] PIERM 110 [2022] PIERM 109 [2022] PIERM 108 [2022] PIERM 107 [2022] PIERM 106 [2021] PIERM 105 [2021] PIERM 104 [2021] PIERM 103 [2021] PIERM 102 [2021] PIERM 101 [2021] PIERM 100 [2021] PIERM 99 [2021] PIERM 98 [2020] PIERM 97 [2020] PIERM 96 [2020] PIERM 95 [2020] PIERM 94 [2020] PIERM 93 [2020] PIERM 92 [2020] PIERM 91 [2020] PIERM 90 [2020] PIERM 89 [2020] PIERM 88 [2020] PIERM 87 [2019] PIERM 86 [2019] PIERM 85 [2019] PIERM 84 [2019] PIERM 83 [2019] PIERM 82 [2019] PIERM 81 [2019] PIERM 80 [2019] PIERM 79 [2019] PIERM 78 [2019] PIERM 77 [2019] PIERM 76 [2018] PIERM 75 [2018] PIERM 74 [2018] PIERM 73 [2018] PIERM 72 [2018] PIERM 71 [2018] PIERM 70 [2018] PIERM 69 [2018] PIERM 68 [2018] PIERM 67 [2018] PIERM 66 [2018] PIERM 65 [2018] PIERM 64 [2018] PIERM 63 [2018] PIERM 62 [2017] PIERM 61 [2017] PIERM 60 [2017] PIERM 59 [2017] PIERM 58 [2017] PIERM 57 [2017] PIERM 56 [2017] PIERM 55 [2017] PIERM 54 [2017] PIERM 53 [2017] PIERM 52 [2016] PIERM 51 [2016] PIERM 50 [2016] PIERM 49 [2016] PIERM 48 [2016] PIERM 47 [2016] PIERM 46 [2016] PIERM 45 [2016] PIERM 44 [2015] PIERM 43 [2015] PIERM 42 [2015] PIERM 41 [2015] PIERM 40 [2014] PIERM 39 [2014] PIERM 38 [2014] PIERM 37 [2014] PIERM 36 [2014] PIERM 35 [2014] PIERM 34 [2014] PIERM 33 [2013] PIERM 32 [2013] PIERM 31 [2013] PIERM 30 [2013] PIERM 29 [2013] PIERM 28 [2013] PIERM 27 [2012] PIERM 26 [2012] PIERM 25 [2012] PIERM 24 [2012] PIERM 23 [2012] PIERM 22 [2012] PIERM 21 [2011] PIERM 20 [2011] PIERM 19 [2011] PIERM 18 [2011] PIERM 17 [2011] PIERM 16 [2011] PIERM 14 [2010] PIERM 13 [2010] PIERM 12 [2010] PIERM 11 [2010] PIERM 10 [2009] PIERM 9 [2009] PIERM 8 [2009] PIERM 7 [2009] PIERM 6 [2009] PIERM 5 [2008] PIERM 4 [2008] PIERM 3 [2008] PIERM 2 [2008] PIERM 1 [2008]
2020-12-15
RRT-MWF-MVDR Algorithm for Space-Time Antijamming
By
Progress In Electromagnetics Research M, Vol. 99, 201-210, 2021
Abstract
Minimum variance distortionless response (MVDR) beamformer is an one of the well-known space-time antijamming techniques for global navigation satellite system (GNSS). It can jointly utilize spatial filter and temporal filter to suppress interference signals. However, the computational complexity is usually so high that it is difficult to apply in engineering problems. In order to solve this problem, a novel MVDR algorithm based on rank-reducing transformation (RRT) and multistage wiener filter (MWF) is proposed for reducing the computational complexity, named as RRT-MWF-MVDR algorithm. Via the characteristics of the oppressive jamming environment and the steering vector of satellite signal, a rank-reducing transformation is given. By the rank-reducing transformation, a rank reduction is realized for the high dimensional received data. Taking these received data with reduced rank as the input of the MWF, the forward decomposition and backward iteration are accomplished. Then the equivalent reduced rank matrix and equivalent weight vector of MWF can be given, respectively. Finally, the space-time two-dimensional antijamming weight vector is given by the mathematical relationship between the reduced-rank matrix and the weight vector.The proposed method can effectively avoid the inverse of high-dimensional matrix. The proposed method offers a number of advantages over the existing algorithms. For example, (1) it has less computational load and is easier to be executed in practical application. (2) It can maintain higher output signal-to-interference-noise ratio (SINR). Simulation results verify the effectiveness of proposed method.
Citation
Fulai Liu Ruiyan Du Hui Song , "RRT-MWF-MVDR Algorithm for Space-Time Antijamming," Progress In Electromagnetics Research M, Vol. 99, 201-210, 2021.
doi:10.2528/PIERM20081301
http://www.jpier.org/PIERM/pier.php?paper=20081301
References

1. Hu, H. and N. Wei, "A study of GPS jamming and anti-jamming," International Conference on Power Electronics & Intelligent Transportation System, 2010.

2. Chen, F. Q., J. W. Nie, B. Y. Li, and F. X. Wang, "Distortionless space-time adaptive processor for global navigation satellite system receiver," Electronics Letters, Vol. 51, No. 25, 2138-2139, 2015.
doi:10.1049/el.2015.2832

3. Lu, Z. K., J. W. Nie, F. Q. Chen, H. M. Chen, and G. Ou, "Adaptive time taps of STAP under channel mismatch for GNSS antenna arrays," IEEE Transactions on Instrumentation and Measurement, Vol. 66, No. 11, 2813-2824, 2017.
doi:10.1109/TIM.2017.2728420

4. Tufts, D. W., R. Kumaresan, and I. Kirsteins, "Data adaptive signal estimation by singular value decomposition of a data matrix," Proceedings of the IEEE, Vol. 70, No. 6, 684-685, 1982.
doi:10.1109/PROC.1982.12367

5. Goldstein, J. S. and I. S. Reed, "Reduced-rank adaptive filtering," IEEE Transactions on Signal Processing, Vol. 45, No. 2, 492-496, 1997.
doi:10.1109/78.554317

6. Goldstein, J. S., I. S. Reed, and L. L. Scharf, "A multistage representation of the Wiener filter based on orthogonal projections," IEEE Transactions on Information Theory, Vol. 44, No. 7, 2943-2959, 1998.
doi:10.1109/18.737524

7. Peckham, C. D., et al., "Reduced-rank STAP performance analysis," IEEE Transactions on Aerospace and Electronic Systems, Vol. 36, No. 2, 664-676, 2000.
doi:10.1109/7.845257

8. Huang, Q. D., L. R. Zhang, and G. Y. Lu, "Interference suppression method for space-time navigation receivers based on samples selection Householder multistage wiener filter," IEEE International Conference on Signal Processing, 2010.
doi:10.1109/TSP.2009.2031732

9. Qiu, S., et al., "A robust reduced-rank monopulse algorithm based on variable-loaded MWF with spatial blocking broadening and automatic rank selection," Digital Signal Processing, Vol. 78, 205-217, 2018.
doi:10.1016/j.dsp.2018.02.015

10. He, S., Z. W. Yang, and G. S. Liao, "Adaptive reduced-rank beamforming method based on knowledge-aided joint iterative optimization," IEEE Geoscience and Remote Sensing Letters, Vol. 13, No. 10, 1582-1586, 2016.
doi:10.1109/LGRS.2016.2600752

11. Song, N., et al., "Adaptive widely linear reduced-Rank beamforming based on joint iterative optimization," IEEE Signal Processing Letters, Vol. 21, No. 3, 265-269, 2014.
doi:10.1109/LSP.2013.2295943

12. Li, D. G., et al., "An improved space-time joint anti-jamming algorithm based on variable step LMS," Tsinghua Science and Technology, Vol. 22, No. 5, 76-84, 2017.
doi:10.23919/TST.2017.8030541

13. Zhao, Y., W. X. Li, X. J. Mao, and N. Zhang, "Null broadening beamforming against array calibration errors," Journal of Harbin Engineering University, Vol. 39, No. 1, 163-168, 2018.

14. Li, W. X., X. J. Mao, and Y. X. Sun, "A new algorithm for null broadening beamforming," Journal of Electronics and Information Technology, Vol. 36, No. 12, 2882-2888, 2014.

15. Mao, X. J., W. X. Li, and Y. S. Li, "Robust adaptive beamforming against signal steering vector mismatch and jammer motion," International Journal of Antennas and Propagations, Vol. 10, 1-12, 2015.

16. Souden, M., J. Benesty, and S. Affes, "A study of the LCMV and MVDR noise reduction filters," IEEE Transactions on Signal Processing, Vol. 58, No. 9, 4925-4935, 2010.
doi:10.1109/TSP.2010.2051803

17. Huang, Y. W., M. K. Zhou, and S. A. Vorobyov, "New designs on MVDR robust adaptive beamforming based on optimal steering vector estimation," IEEE Transactions on Signal Processing, Vol. 67, No. 14, 3624-3638, 2019.
doi:10.1109/TSP.2019.2918997

18. Zhang, Y. P., Y. J. Li, and M. G. Gao, "Robust adaptive beamforming based on the effectiveness of reconstruction," Signal Processing, Vol. 120, 572-579, 2016.
doi:10.1016/j.sigpro.2015.09.039

19. Huang, F., et al., "A fast adaptive reduced rank transformation for minimum variance beamforming," Signal Processing, Vol. 92, No. 12, 2881-2887, 2012.
doi:10.1016/j.sigpro.2012.05.017

20. Du, R., F. Liu, K. Tang, and H. Song, "Adaptive antijamming based on space-time 2-D sparse array for GNSS receivers," Progress In Electromagnetics Research M, Vol. 96, 89-97, 2020.
doi:10.2528/PIERM20070302