Multistatic micro-Doppler radar signatures of personnel targets

This study extends the theory of the micro-Doppler effect into the multistatic domain, and considers how the multistatic micro-Doppler signature (μ-DS) will affect radar automatic target recognition (ATR). Real multistatic μ-DS of personnel targets are examined and their nature compared with theory and simulated results. It is demonstrated that the use of multistatic μ-DSs would increase the robustness of radar ATR systems to the problem of self-occlusion, where the target obscures itself. Redundancy in the multistatic μ-DS is identified, and how this may reduce the data fusion demands of multi-aspect classifiers is discussed. Also, information theory is used to demonstrate that the multistatic μ-DS contains more target information than the monostatic case. This result leads to the prediction that multi-aspect μ-DS-based radar ATR would have improved performance compared to single-aspect solutions.

[1]  H. Wechsler,et al.  Micro-Doppler effect in radar: phenomenon, model, and simulation study , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Lawrence Carin,et al.  Identification of ground targets from sequential HRR radar signatures , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

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

[4]  C.J. Baker,et al.  Realisation and Evaluation of a Low Cost Netted Radar System , 2006, 2006 CIE International Conference on Radar.

[5]  Daniel Thalmann,et al.  A global human walking model with real-time kinematic personification , 1990, The Visual Computer.

[6]  M. Skolnik,et al.  Introduction to Radar Systems , 2021, Advances in Adaptive Radar Detection and Range Estimation.

[7]  Mengdao Xing,et al.  On the aspect sensitivity of high resolution range profiles and its reduction methods , 2000, Record of the IEEE 2000 International Radar Conference [Cat. No. 00CH37037].

[8]  F. Groen,et al.  Human walking estimation with radar , 2003 .

[9]  C.J. Baker,et al.  Template Based Micro-Doppler Signature Classification , 2006, 2006 European Radar Conference.

[10]  A. G. Stove,et al.  A Doppler-based target classifier using linear discriminants and principal components , 2003, 2003 Proceedings of the International Conference on Radar (IEEE Cat. No.03EX695).

[11]  C.J. Baker,et al.  Multistatic Micro-Doppler Signature of personnel , 2008, 2008 IEEE Radar Conference.

[12]  C.J. Baker,et al.  Naïve Bayesian radar micro-doppler recognition , 2008, 2008 International Conference on Radar.

[13]  Shaun Raymond Doughty.  Development and performance evaluation of a multistatic radar system , 2008 .

[14]  Chris Baker,et al.  Design and evaluation of a low-cost multistatic netted radar system , 2007 .

[15]  A. Ghaleb,et al.  Micro-Doppler analysis of pedestrians in ISAR imaging , 2008, 2008 IEEE Radar Conference.

[16]  Feng Zhu,et al.  Nonstationary Hidden Markov Models for Multiaspect Discriminative Feature Extraction From Radar Targets , 2007, IEEE Transactions on Signal Processing.

[17]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[18]  A. Cohen,et al.  GMM-based target classification for ground surveillance Doppler radar , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[19]  Hugh Griffiths,et al.  Radar target classification using multiple perspectives , 2007 .

[20]  Mark R. Bell,et al.  JEM modeling and measurement for radar target identification , 1993 .