Abstract
The inspiration behind a basic cognitive radar is the analogy between human brain and radar signal processing techniques. In this paper, we propose a hybrid model of cognitive frequency diverse array radar with adaptive range–angle-dependent beamforming. Cognitive radar properties have been incorporated to enhance the signal to interference plus noise ratio and detection capability. The proposed receiver estimates the current and future target position and tunnels this information to the transmitter as feedback. Since frequency diverse array uses a small frequency increment across the antenna elements to generate a range–angle-dependent beam pattern, the proposed scheme provides an analytical formula to compute this frequency increment based on the feedback. This saves a lot of power and reduces computational complexity. In addition, the electromagnetic pollution of environment is decreased. Monte Carlo-based simulation results have been provided to validate the performance.













Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Haykin S, Zia A, Xue Y, Arasaratnam I. Control-theoretic approach to tracking radar: first step towards cognition. Digital Signal Process. 2011;21:576–85.
Haykin S. Neural networks and learning machines. 3rd ed. USA: Prentice-Hall Englewood Cliffs; 2009.
Haykin S, Fuster J.M. On cognitive dynamic systems: cognitive neuroscience and engineering learning from each other. Proceedings of the IEEE. April 2014; 608–628.
Dasgupta D, Michalewicz Z. Evolutionary algorithms in engineering applications. Berlin Heidelberg New York: Springer-Verlag; 1997.
Gavan J, Ishay JS. Hypothesis of natural radar tracking and communication direction finding systems affecting hornets flight. Prog Electromag Res. 2001;34:299–312.
Haykin S. Cognitive radar: a way of the future. IEEE Signal Process Mag. 2006;23:30–40.
Fiori S. Learning the Fréchet mean over the manifold of symmetric positive-definite matrices. Cognitive Computation. 2009;1:279–91.
Xue Y. Cognitive radar: theory and simulations: Phd.thesis. Canada: The school of graduate studies at McMaster University; 2010.
Kershaw DJ, Evans RJ. Optimal waveform selection for tracking systems. IEEE Trans Inf Theory. 1994;40:1536–50.
Müller VC. Autonomous cognitive systems in real-world environments: less Control, more flexibility and better interaction. Cognitive Computation. 2012;4(3):212–5.
Guerci JR. Cognitive radar: the knowledge-aided fully adaptive approach. MA: Artech House Reading; 2010.
Haykin, S, Amin Z; Arasaratnam I, Xue Y. Cognitive tracking radar. IEEE radar conference 10–14 May 2010; 1467–1470.
Haykin S, Xue Y, Setoodeh P. Cognitive radar: step toward bridging the gap between neuroscience and engineering. Proc IEEE. 2012;100:3102–30.
Karaboga D, Guney K. A simple formula obtained using tabu search algorithm for the radiation efficiency of a resonant rectangular microstrip antenna. Turkish J. Electr Eng Comput. 1999;7:19–28.
Mouhamadou M, Vaudon P, Rammal M. Smart antenna array patterns synthesis: null steering and multi-user beamforming by phase control. Prog Electromagn Res. 2006;60:95–106.
Antonik P, Wicks MC, Griffiths HD, Baker CJ. Frequency diverse array radars. IEEE conference on radar 2006.
Antonik P. An investigation of a frequency diverse array: London: University College London Bloomsbury; 2009.
Secmen M, Demir S, Hizal A, Eker T. Frequency diverse array antenna with periodic time modulated pattern in range and angle. IEEE conference on radar 2007; 427–430.
Huang J, Tong K, Baker CJ. Frequency diverse array with beam scanning feature. Antennas and propagation society international symposium 2008; 1–4.
Wang WQ, Shao H, Cai J. Range-angle-dependent beamforming by frequency diverse array antenna. Int J Antennas Propag. 2012;2012:760489.
Basit A, Qureshi IM, Khan W, Ulhaq I, Khan SU. Hybridization of cognitive radar and phased array radar having low probability of intercept transmit beamforming. Int J Antennas Propag. 2014;2014:129170.
Grewal MS, Andrews AP. Kalman Filtering: Theory and Practice, Englewood Cliffs. NJ: Prentice-Hall; 1993.
Herman SM. A particle filtering approach to joint passive radar tracking and target classification. Theses: University of Illinois at Urbana-Champaign; 2002.
Widrow B, Stearns SD. Adaptive Signal Processing. Englewood cliffs: Prentice Hall, Inc.; 1985.
Wang WQ, Shao H. Range-angle localization of targets by a double-pulse frequency diverse array radar. IEEE J Sel. Top Signal Process. 2014;8(1):106–14.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Basit, A., Qureshi, I.M., Khan, W. et al. Range–Angle-Dependent Beamforming for Cognitive Antenna Array Radar with Frequency Diversity. Cogn Comput 8, 204–216 (2016). https://doi.org/10.1007/s12559-015-9348-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12559-015-9348-6