Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-22T08:06:01.023Z Has data issue: false hasContentIssue false

Cognitive null steering in frequency diverse array radars

Published online by Cambridge University Press:  29 July 2015

Sarah Saeed*
Affiliation:
Department of Electrical Engineering, Air University, Islamabad, Pakistan. Phone: +92519262557
Ijaz Mansoor Qureshi
Affiliation:
Department of Electrical Engineering, Air University, Islamabad, Pakistan. Phone: +92519262557
Abdul Basit
Affiliation:
Faculty of Electrical Engineering, International Islamic University, Islamabad, Pakistan
Ayesha Salman
Affiliation:
Department of Electrical Engineering, Air University, Islamabad, Pakistan. Phone: +92519262557
Waseem Khan
Affiliation:
Department of Electrical Engineering, Air University, Islamabad, Pakistan. Phone: +92519262557
*
Corresponding author: S. Saeed Email: [email protected]

Abstract

Null steering has been a challenge in radar communications for the past few decades. In this paper, a novel cognitive null steering technique in frequency diverse array radars using frequency offset selection is presented. The proposed system is a complete implementable framework that provides precise and deep null placement in the range and angle locations of the interference source. The proposed system is cognitive such that the transmitter and receiver are connected via a feedback loop. System extracts interference source location parameters from the radar scene using Multiple Signal Classification, a super resolution direction of arrival estimation technique. Neural networks known for minimum computation time, and good non-linear and non-parametric approximation have been utilized for prediction of next location of the interference source. Simulation results validate the proposed frequency offset selection by demonstrating precise and deep nulls at the desired locations.

Type
Research Papers
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1] Irteza, S., Schäfer, E., Stephan, R., Hornbostel, A.: Compact antenna array receiver for robust satellite navigation systems. Int. J. Microw. Wirel. Technol., (2014), 111. doi:10.1017/S1759078714000907.Google Scholar
[2] Mehmood, S., Khan, Z., Zaman, F.; Shoaib, B.: Performance analysis of the different null steering techniques in the field of adaptive beamforming. Res. J. Appl. Sci. Eng. Technol., 5 (2013), 40064012.CrossRefGoogle Scholar
[3] Baird, D.; Rassweiler, G.: Adaptive side lobe nulling using digitally controlled phase shifters. IEEE Trans. Antennas Propag., 24 (1976), 638649.CrossRefGoogle Scholar
[4] Steyskal, H.: Synthesis of antenna pattern with prescribed nulls. IEEE Trans. Antennas Propag., 30 (1982), 273279.CrossRefGoogle Scholar
[5] Steyskal, H.: Simple method for pattern nulling by phase perturbation. IEEE Trans. Antennas Propag., 31 (1983), 163166.CrossRefGoogle Scholar
[6] Vescovo, R.: Null synthesis by phase control for antenna array. IET Electron. Lett., 36 (2000), 198199.CrossRefGoogle Scholar
[7] Karaboga, N.; Güney, K.; Akdagli, A.: Null steering of linear antenna arrays with use of modified touring Ant Colony Optimization Algorithm. Int. J. RF Microw. Technol., 12 (2013), 40064012.Google Scholar
[8] Wang, W.Q.; Shao, H.; Cai, J.: Range-angle-dependent beamforming by frequency diverse array antenna. Int. J. Antennas Propag., 2012 (2012), 110.Google Scholar
[9] Wang, W.Q.: Range-angle dependent transmit beampattern synthesis for linear frequency diverse arrays. IEEE Trans. Antennas Propag., 61 (2013), 40734081.CrossRefGoogle Scholar
[10] Wang, W.Q.; Shao, H.Z.: Range-angle localization of targets by a double-pulse frequency diverse array radar. IEEE J. Sel. Top. Signal Process., 8 (2014), 106114.Google Scholar
[11] Wang, W.Q.: Transmit subaperturing for range and angle estimation in frequency diverse array radar. IEEE Trans. Signal Process., 62 (2014), 20002011.Google Scholar
[12] Wang, W.Q.: Nonuniform frequency diverse array for range–angle imaging of targets. IEEE Sens. J., 14 (2014), 18.Google Scholar
[13] Haykin, S.: Cognitive radar, a way of the future. IEEE Signal Process. Mag., 23 (1) (2006), 3040.Google Scholar
[14] Zhang, X.; Cui, C.: Range-spread target detecting for cognitive radar based on track-before-detect. Int. J. Electron., 101 (2014), 7487.Google Scholar
[15] Ince, N.A.; Ercan Topuz, E.; Erdal Panayirci, E.; Cevdet Isik, C.: Principles of Integrated Maritime Surveillance Systems, Kluwer Academic Publishers, New York, 2000.Google Scholar
[16] Kouemou, G.: Radar Technology, InTech Publishers, Croatia, 2010.Google Scholar
[17] Secmen, M.; Demir, S.; Hizal, A.; Eker, T.: Frequency diverse array antenna with periodic time modulated pattern in range and angle, in IEEE Radar Conf., USA, 2007, 427–430.CrossRefGoogle Scholar
[18] Osman, L.; Sfar, I.; Gharsallah, A.: The application of high-resolution methods for DOA estimation using a linear antenna array. Int. J. Microw. Wireless Technol., 7 (2014), 18.Google Scholar
[19] Islam, B.: Comparison of conventional and modern load forecasting techniques based on artificial intelligence and expert systems. Int. J. Comput. Sci. Issues, 8 (2011), 504513.Google Scholar
[20] Raji, M.A.; Athappilly, K.: A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models. Expert Syst. Appl., 29 (2005), 6574.Google Scholar
[21] Bashi, A.S.; Kaminsky, E.J.: Comparison of neural network and extended Kalman filter determination of kinematics from impact acceleration tests, in IEEE Int. Conf. Computational Cybernetics and Simulation, 1997, 35013506.Google Scholar
[22] Awadz, F.; Yassin, I.M.; Rahiman, M.H.F.; Taib, M.N.; Zabidi, A.; Abu Hassan, H.: System identification of essential oil extraction system using non-linear autoregressive model with exogenous inputs (NARX). IEEE Control Syst. Grad. Res. Colloq., 2010.Google Scholar
[23] Xie, H.; Tang, H.; Liao, Y.H.: Time series prediction based on NARX neural networks: An advanced approach, in IEEE Eighth Int. Conf. on Machine Learning and Cybernetics, Baoding, 2009.Google Scholar