Adaptive Radar Signal Detection in Autoregressive Interference using Kalman-based Filters

Document Type : Article

Authors

1 Department of Electrical Engineering, Yazd University, Yazd, P.O. Box 89195-741, Iran

2 - Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, P.O. Box 84156-83111, Iran - Department of Electrical Engineering, Yazd University, Yazd, P.O. Box 89195-741, Iran

Abstract

This paper deals with the adaptive detection of radar target signal with unknown amplitude embedded in Gaussian interference which has been modelled as an AR process. Considering such model for the interference decreases the number of parameters that must be estimated and therefore less or even no secondary data is needed to obtain a detector with desired performance. Herein the detection is based on only the primary data. The authors resorting to the modern Kalman filtering technique develop the conventional GLRT-based detection in the presence of AR interference and propose two new detectors; AREKF based on extended Kalman filter and ARUKF based on unscented Kalman filter. The performance assessment conducted by Monte Carlo simulation compares the proposed detectors with the existing detectors based on generalised likelihood ratio test and Kalman filter. The results show that the ARUKF detector significantly has better detection performance than that of other detectors for the low number of primary data and high signal to noise ratio (SNR).

Keywords


References
1. Kelly, E.J. An adaptive detection algorithm", IEEE Trans. Aerosp. Electron. Syst., 22(2), pp. 115{127 (1986).
2. Robey, F.C., Fuhrman, D.L., Kelly, E.J., et al. A CFAR adaptive matched  lter detector", IEEE Trans.
Aerosp. Electron. Syst., 29(1), pp. 208{216 (1992).
3. Ra e, A.H. and Taban, M.R. Two dimensional optimal linear detector for slowly uctuating radar signals in compound Gaussian clutter", Scientia Iranica, 21(6), pp. 2213{2223 (2014).
4. Conte, E., De Maio, A., and Ricci, G. GLRTbased
adaptive detection algorithms for range-spread
targets", IEEE Trans. Signal Process., 49(7), pp.
1336{1348 (2001).
5. Himed, B. and Melvin, W.L. Analyzing space-time
adaptive processors using measured data", Thirty- rst
Asilomar Conf. on Signals, Systems & Computers,
Paci c Grove, CA, USA, pp. 930{935 (1997).
6. Bandiera, F., Besson, O., Coluccia, A., et al. ABORTlike
detectors: A Bayesian approach", IEEE Trans.
Signal Process., 63(19), pp. 5274{5284 (2015).
7. Aubry, A., De Maio, A., Foglia, G., et al. Di use
multipath exploitation for adaptive radar detection",
IEEE Trans. Signal Process., 63(5), pp. 1268{1281
(2015).
8. Said, S., Hajri, H., Bombrun, L., et al. Gaussian distributions
on riemannian symmetric spaces: statistical
kerning with structured covariance matrices", IEEE
Trans. Information Theory, 64(2), pp. 752{772 (2018).
9. Kay, S.M. Asymptotically optimal detection in unknown
colored noise via autoregressive modeling",
IEEE Trans. on Acoustics, Speech, and Signal Processing,
31(4), pp. 927{940 (1983).
10. Qureshi, T.R., Rangaswamy, M., and Bell, K.L. Parametric
adaptive matched  lter for multistatic MIMO
radar", IEEE Trans. Aerosp. Electron. Syst., 54(5),
pp. 2202{2219 (2018).
11. Haykin, S. and Steinhardt, A., Adaptive Radar Detection
and Estimation, Wiley, New York (1992).
12. Sheikhi, A., Nayebi, M.M., and Aref, M.R. Adaptive
detection algorithm for radar signals in autoregressive
interference", IEE Proc., Radar Sonar Navig., 145(5),
pp. 309{314 (1998).
13. Sheikhi, A., Nayebi, M.M., and Aref, M.R. A powerful
practical coherent adaptive radar detector", 2001 CIE
Conf. on Radar, Beijing, China, pp. 405{409 (2001).
14. Moniri, A.R., Nayebi, M.M., and Sheikhi, A. A multichannel
auto-regressive GLR detector for airborne
phased array radar applications", 2003 IEEE Conf. on
Radar, Adelaide, SA, Australia, pp. 206{211 (2003).
15. Sheikhi, A., Zamani, A., and Norouzi, Y. Modelbased
adaptive target detection in clutter using MIMO
radar", 2006 CIE Conf. on Radar, Shanghai, China,
pp. 1{4 (2006).
16. Alfano, G., De Maio, A., and Farina, A. Model-based
adaptive detection of range-spread targets", IEE Proc.
Radar Sonar Navig., 151(1), pp. 2{10 (2004).
17. Sheikhi, A., Zamani, A., Hatam, M., et al. Model
based adaptive detection algorithm with low secondary
data support", 10th Int. Conf. on Information Science,
Signal Processing and their Applications, pp. 177{180
(2010).
18. Wang, P., Li, H., and Himed, B. A parametric
moving target detector for distributed MIMO radar in
non-homogeneous environment", IEEE Trans. Signal
Process., 61(9), pp. 2282{2294 (2013).
19. Li, H., Wang, Z., Liu, J., et al. Moving target detection
in distributed MIMO radar on moving platforms",
IEEE J. Select. Topics Signal Process., 9(8), pp. 1524{
1535 (2015).
20. Fan, Y.F., Luo, F., Li, M., et al. Fractal properties
of autoregressive spectrum and its application on weak
target detection in sea clutter background", IET Radar
Sonar Navigat., 9(8), Kuala Lumpur, Malaysia, pp.
1070{1077 (2015).
21. Sohn, K.J., Li, H., and Himed, B. Parametric GLRT
for multichannel adaptive signal detection", IEEE
Trans. Signal Process., 55(11), pp. 5351{5360 (2007).
3362 M. Dorostgan and M.R. Taban/Scientia Iranica, Transactions D: Computer Science & ... 28 (2021) 3352{3362
22. Wang, P., Li, H., and Himed, B. A new parametric
GLRT for multichannel adaptive signal detection",
IEEE Trans. Signal Process., 58(1), pp. 317{325
(2010).
23. Crouse, D.F. Basic tracking using nonlinear
continuous-time dynamic models", IEEE M. Aerosp.
Electron. Syst., 30(2), pp. 4{41 (2015).
24. Jahanbakhshi, S., Pishvaie, M.R., and Boozarjomehry,
R.B. Characterization of three-phase 
ow in porous
media using the ensemble Kalman  lter", Scientia
Iranica, 24(3), pp. 1281{1301 (2017).
25. Kiani, M. and Pourtakdoust, S.H. Spacecraft attitude
and system identi cation via marginal modi ed unscented
Kalman  lter utilizing the sun and calibrated
three-axis-magnetometer sensors", Scientia Iranica,
21(4), pp. 1451{1460 (2014).
26. Taban, M.R. and Sheikh Moza ari, A. Adaptive radar
signal detection in Gaussian clutter with autoregressive
model using Kalman  lter", Journal of Radar,
2(3), pp. 1{12 (2014).
27. Skolnik, M.I., Introduction to Radar Systems, 3rd Ed.,
McGraw-Hill, New York (2001).
28. Aubry, A., De Maio, A., Pallotta, L., et al. Radar
detection of distributed targets in homogeneous interference
whose inverse covariance structure is de ned
via unitary invariant functions", IEEE Trans. Signal
Process., 61(20), pp. 4949{4961 (2013).
29. Haykin, S., Kalman Filtering and Neural Networks,
Wiley, New York (2001).
30. Kay, S.M., Fundamentals of Statistical Signal Processing,
2, Prentice Hall, New Jersey (1993).