Cognitive Nonlinear Radar

Abstract : In this report, a unique cognitive nonlinear radar (CNR) is introduced. Research and development efforts for the CNR are currently funded by the U.S. Army Research Laboratory (ARL). The CNR adapts to (1) an increasingly cluttered electromagnetic (EM) environment, a growing problem for ground-based and airborne radar systems; (2) multiple targets; and (3) other radar, communication, and electronic systems that must operate without interfering with each other. The CNR uses a narrowband, nonlinear radar target detection methodology. This methodology has the advantage, as compared with other nonlinear radar systems that do not implement a cognitive scheme, to adapt to the radio frequency (RF) environment by intelligently selecting waveform parameters using adaptive algorithms. The adaptive algorithms optimize the waveform parameters based on (1) the EM interference, (2) target likelihood, and (3) permissible transmit frequencies as specified by regulations and allowable by other systems operations within the environment.

[1]  S. Haykin,et al.  Cognitive radar: a way of the future , 2006, IEEE Signal Processing Magazine.

[2]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .

[3]  Robert Hstger,et al.  Harmonic Radar Systems for Near-Ground In-Foliage Nonlinear Scatterers , 1976, IEEE Transactions on Aerospace and Electronic Systems.

[4]  B. Widrow,et al.  Adaptive antenna systems , 1967 .

[5]  Amin Zia,et al.  Cognitive tracking radar , 2010, 2010 IEEE Radar Conference.

[6]  Tomi Mattila,et al.  Technical Solutions for Automotive Intermodulation Radar for Detecting Vulnerable Road Users , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.

[7]  Weifang Wang,et al.  Spectrum sensing in cognitive radio , 2016 .

[8]  Mark R. Bell.  Information theory and radar waveform design , 1993, IEEE Trans. Inf. Theory.

[9]  Leo C. Kempel,et al.  Tracking Insects with Harmonic Radar: a Case Study , 2004 .

[10]  David G. Stork,et al.  Pattern Classification , 1973 .

[11]  S. Applebaum,et al.  Adaptive arrays , 1976 .

[12]  E.J. Delp,et al.  Forensics of Things , 2006, 2006 IEEE Southwest Symposium on Image Analysis and Interpretation.

[13]  A. Farina,et al.  Knowledge-based radar signal and data processing: a tutorial review , 2006, IEEE Signal Processing Magazine.

[14]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[15]  Charles W. Bostian,et al.  Cognitive Radio Formulation and Implementation , 2006, 2006 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[16]  E.J. Rothwell,et al.  Harmonic radar tag measurement and characterization , 2003, IEEE Antennas and Propagation Society International Symposium. Digest. Held in conjunction with: USNC/CNC/URSI North American Radio Sci. Meeting (Cat. No.03CH37450).

[17]  C.W. Bostian,et al.  Cognitive radio testbed: further details and testing of a distributed genetic algorithm based cognitive engine for programmable radios , 2004, IEEE MILCOM 2004. Military Communications Conference, 2004..

[18]  L.E. Brennan,et al.  Theory of Adaptive Radar , 1973, IEEE Transactions on Aerospace and Electronic Systems.

[19]  Don R. Reynolds,et al.  A landscape‐scale study of bumble bee foraging range and constancy, using harmonic radar , 1999 .

[20]  R. J. Klensch,et al.  Harmonic radar helps autos avoid collisions , 1973, IEEE Spectrum.