paper 150204110829

Published: 2015-07-09

The Application of Non-linear Dynamics Methods for Radar Target Identification

Thomas L. Carroll,

Thomas L. Carroll

Naval Research Laboratory, USA

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Frederic J. Rachford

Naval Research Laboratory, USA

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Proceedings of 2015 Photonics & Electromagnetics Research Symposium, Prague, July 6 - 9，Page(s)1533-1537

Abstract

Currently radar target identification is effected by finding the downrange distribu- tion of scatterers on a platform using high definition radar and referencing this distribution to a look-up table of measured radar returns. This method is difficult due to the rapid scintillation of the radar signal of a flying target at short wavelengths. We investigate a method of target identification in the low definition, relatively long wavelength, limit using techniques derived from the field of non-linear dynamics. In this limit the radar reflection is much less sensitive to the orientation of the target to the radar and scintillation is greatly reduced. Modern detection advances allow the digitization and processing of raw real time non-pulse compressed waveforms. We simulate the raw radar returns from several very similar small (4 to 5 wavelength) objects (i.e., cylinder, cone-cylinder, cone-ogive and double ogive) illuminated with chaotic or random modulated waveforms or a linear chirp using a commercial finite difference time domain code. In this aggressive limit we embed the target return in two dimensions using the method of delays to form an attractor. The geometric properties of the attractors are analyzed to distinguish between these similar target objects in the low definition limit. Our method selects random points on a reference attractor (or strands of points) with many Euclidean nearest neighbors and compares these with the nearest neighbor density of points (or strands) with the same time index on an attractor from an unknown target. Comparing the density of nearest neighbors at these points on two attractors allows us to calculate probabilities of correct identification. (Is the unknown signal from the reference target or from one of the other targets?) Simulation data and analyses derived from three different transmit waveforms are compared. The robustness of our target ID method is probed by the addition of white noise to the signals.

Citation

Thomas L. Carroll, and Frederic J. Rachford, "The Application of Non-linear Dynamics Methods for Radar Target Identification," Proceedings of 2015 Photonics & Electromagnetics Research Symposium, Prague, July 6 - 9，Page(s)1533-1537

References

1. Knott, E. F., J. F. Shaeffer, and M. T. Tulley, Radar Cross Section, Artech House, Norwood, MA, 1993. Google Scholar

2. Rihaczek, A. W. and S. J. Hershkowitz, Theory and Practice of Radar Target Identification, Artech House, Norwood, MA, 2000. Google Scholar

3. Hu, R. and Z. Zhu, "Researches on radar target classification based on high resolution range profiles," Proc. IEEE, Vol. 2, 951-955, 1997.
doi:10.1109/naecon.1997.622757 Google Scholar

4. Berni, A. J., "Target identification by natural resonance estimation," IEEE Transactions on Aerospace and Electronic Systems, Vol. 11, 147-154, 1975.
doi:10.1109/taes.1975.308051 Google Scholar

5. Baum, C. E., E. J. Rothwell, K. M. Chen, and D. P. Nyquist, "The singularity expansion method and its application to target identification," Proc. IEEE, Vol. 79, 1481-1491, 1991.
doi:10.1109/5.104223 Google Scholar

6. Kennaugh, E., "The K-pulse concept," IEEE Transactions on Antennas and Propagation, Vol. 29, 327-331, 1981.
doi:10.1109/tap.1981.1142561 Google Scholar

7. Pillai, S. U., H. S. Oh, D. C. Youla, and J. R. Guerci, "Optimum transmit & receiver design in the presence of signal-dependent interference and channel noise," IEEE Transactions on Information Theory, Vol. 46, No. 2, 577-584, 2000. Google Scholar

8. Bergin, J. S., P. M. Techau, J. E. D. Carlos, and J. R. Guerci, "Radar waveform optimization for colored noise mitigation," 2005 IEEE Intrnational Radar Conference, 149-154, 2005.
doi:10.1109/radar.2005.1435810 Google Scholar

9. Vespe, M., C. J. Baker, and H. D. Griffiths, "Automatic target recognition using multi-diversity radar," IET Radar, Sonar & Navigation, Vol. 1, No. 6, 470-478, 2007.
doi:10.1049/iet-rsn:20070004 Google Scholar

10. Carroll, T. L., "Optimizing chaos-based signals for complex radar targets," Chaos, Vol. 17, 033103, 2007.
doi:10.1063/1.2751392 Google Scholar

11. Rachford, F. J. and T. L. Carroll, "A chaos based waveform approach to radar target identification," PIERS Online, Vol. 6, No. 5, 476-479, 2010.
doi:10.2529/piers091216170610 Google Scholar

12. Carroll, T. L. and F. J. Rachford, "Nonlinear dynamics method for target identification," Proceedings of SPIE, Vol. 8021, 8021L1-1-8021L1-13, 2011.
doi:10.1117/12.884473 Google Scholar

13. Carroll, T. L. and F. J. Rachford, "Using filtering effects to identify objects," Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 22, 23107-23109, 2012.
doi:10.1063/1.3702566 Google Scholar

14. Carroll, T. L. and F. J. Rachford, "Object identification based on filtering," IET-RSN, Vol. 2013, 1-6, 2013.
doi:10.1049/iet-rsn.2012.0194 Google Scholar

15. Carroll, T. L. and F. J. Rachford, "Target identification based on state space analysis," IEEE RADAR, Vol. 2014, 32-37, 2014.
doi:10.1109/radar.2014.6875550 Google Scholar

16. Taflove, A. and S. C. Hagness, Computational Electrodynamics: The Finite Difference Time Domain Method, Artech House, Norwood, MA, 2005. Google Scholar

17. Pecora, L. M., T. L. Carroll, and J. F. Heagy, "Statistics for mathematical properties between time series embeddings," Physical Review E, Vol. 52, 3420-3439, 1995.
doi:10.1103/physreve.52.3420 Google Scholar

18. Takens, F., Dynamical Systems and Turbulence, Springer, Berlin, 1981. Google Scholar

19. Sauer, T., J. A. Yorke, and M. Casdagli, "Embedology," Journal of Statistical Physics, Vol. 65, 579-616, 1991.
doi:10.1007/bf01053745 Google Scholar

20. Abarbanel, H. D. I., R. Brown, J. J. Sidorowich, and L. S. Tsmring, "The analysis of observed chaotic data in physical systems," Reviews of Modern Physics, Vol. 65, 1331-1392, 1993.
doi:10.1103/revmodphys.65.1331 Google Scholar

21. Pecora, L. M., L. Moniz, J. Nichols, and T. L. Carroll, "A unified approach to attractor reconstruction," Chaos, Vol. 17, 13110-13119, 2007.
doi:10.1063/1.2430294 Google Scholar