Suppression of Clutter and Jammer Using Recursive Space-Time Adaptive Processing For Airborne Radars

Document Type : Original Article

Authors

Malek Ashtar university of Technology

Abstract

The main challenge for airborne radars in discovering ground targets is the removal of Clutter and Jammer. The adaptive space-time processing method is the newest method of processing against these signals. The space-time processor creates a two-dimensional filter, which sets zero (null) angular and Doppler frequencies where Jammer and clutter are signaled. In computing the optimal weight vector for this filter, the covariance matrix of the interference signals is required. In practice, the covariance matrix of the signal is not interfering and should be estimated. The estimation of covariance matrix is based on recursive signals and according to other target cells other than the target cell (secondary data). The main problem in estimating this matrix is the limitation of the number of secondary data that is homogeneous and reliable. Different processing methods have been devised to increase convergence and reduce computational volume. The use of de finite filters facilitates the convergence and faster updating of the weighted vector and eliminates the processes caused by the inversion of the covariance matrix. In this paper, the recursive method has several parallel processing branches associated with weight reduction and drop-down matrix updating approach which, in comparison with previous recursive methods, results in improved performance by having a limited number of secondary data.

Keywords


R. C. de Lamare, and R. Sampaio-Neto, “Adaptive reduced-rank processing based on joint and iterative interpolation, decimation, and filtering,” IEEE Transactions on Signal Processing, vol. 57, no. 7, pp. 2503-2514, 2009.##
 
[2]   R. Fa, R. C. de Lamare, and L. Wang, “Reduced-rank STAP schemes for airborne radar based on switched joint interpolation, decimation and filtering algorithm,” IEEE Transactions on Signal Processing, vol. 58, no. 8, pp. 4182-4194, 2010.##
 
[3]   P. S. Chang, and A. N. Willson, “Analysis of conjugate gradient algorithms for adaptive filtering,” IEEE Transactions on Signal Processing, vol. 48, no. 2, pp. 409-418, 2000##
 
[4]   S. S. Haykin, “Adaptive filter theory” , Pearson Education India, 2008.##
 
[5]   H. Wang, and L. Cai, “On adaptive spatial-temporal processing for airborne surveillance radar systems,” IEEE Transactions on aerospace and electronic systems, vol. 30, no. 3, pp. 660-670, 1994.##
 
[6]   D. A. Pados, and G. N. Karystinos, “An iterative algorithm for the computation of the MVDR filter,” IEEE Transactions On signal processing, vol. 49, no. 2, pp. 290-300, 2001.##
[7]   D. A. Pados, S. N. Batalama, G. N. Karystinos, and J. D. Matyjas, "Short-data-record adaptive detection." pp. 357-361.##
 
[8]   R. Fa, and R. C. De Lamare, “Reduced-rank STAP algorithms using joint iterative optimization of filters,” IEEE Transactions on Aerospace and Electronic Systems, vol. 47, no. 3, pp. 1668-1684, 2011##
 
[9]   J. S. Goldstein, 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, pp. 2943-2959, 1998.##
 
[10] R. C. de Lamare, and R. Sampaio-Neto, “Reduced-rank adaptive filtering based on joint iterative optimization of adaptive filters,” IEEE Signal Processing Letters, vol. 14, no. 12, pp. 980-983, 2007.##
 
[11] M. E. Weippert, J. Hiemstra, J. Goldstein, and M. Zoltowski, "Insights from the relationship between the multistage Wiener filter and the method of conjugate gradients." pp. 388-392##
 
[12] L. Wang, and R. De Lamare, “Constrained adaptive filtering algorithms based on conjugate gradient techniques for beamforming,” IET Signal Processing, vol. 4, no. 6, pp. 686-697, 2010##
 
[13]         L. L. Scharf, E. K. Chong, M. D. Zoltowski, J. S. Goldstein, and I. S. Reed, “Subspace expansion and the equivalence of conjugate direction and multistage wiener filters,” IEEE Transactions on Signal Processing, vol. 56, no. 10, pp. 5013-5019, 2008.##
Volume 8, Issue 1 - Serial Number 23
September 2020
Pages 45-54
  • Receive Date: 26 October 2019
  • Revise Date: 28 June 2020
  • Accept Date: 16 November 2020
  • Publish Date: 22 August 2020