Performance Analysis of Centroid and SVD Features for Personnel Recognition Using Multistatic Micro-Doppler

In this letter, we investigate the use of micro-Doppler signatures experimentally recorded by a multistatic radar system to perform recognition of people walking. Three different sets of features are tested, taking into account the impact on the overall classification performance of parameters, such as aspect angle, types of classifier, different values of signal-to-noise ratio, and different ways of exploiting multistatic information. High classification accuracy of above 98% is reported for the most favorable aspect angle, and the benefit of using multistatic data at less favorable angles is discussed.

[1]  Francesco Fioranelli,et al.  Multistatic human micro-Doppler classification of armed/unarmed personnel , 2015 .

[2]  Youngwook Kim,et al.  Human Detection Using Doppler Radar Based on Physical Characteristics of Targets , 2015, IEEE Geoscience and Remote Sensing Letters.

[3]  Irena Orovic,et al.  A new approach for classification of human gait based on time-frequency feature representations , 2011, Signal Process..

[4]  J. J. M. de Wit,et al.  Radar micro-Doppler feature extraction using the Singular Value Decomposition , 2014, 2014 International Radar Conference.

[5]  Alessio Balleri,et al.  Recognition of humans based on radar micro-Doppler shape spectrum features , 2015 .

[6]  C. Karabacak,et al.  Multi-aspect angle classification of human radar signatures , 2013, Defense, Security, and Sensing.

[7]  Dave Tahmoush,et al.  Review of micro-Doppler signatures , 2015 .

[8]  Francesco Fioranelli,et al.  Personnel recognition based on multistatic micro-Doppler and singular value decomposition features , 2015 .

[9]  Sevgi Zubeyde Gurbuz,et al.  Operational assessment and adaptive selection of micro-Doppler features , 2015 .

[10]  V. Chen,et al.  Radar Micro-Doppler signatures : processing and applications , 2014 .

[11]  Dave Tahmoush,et al.  Radar micro-doppler for long range front-view gait recognition , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[12]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[13]  Youngwook Kim,et al.  Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Francesco Fioranelli,et al.  Classification of Unarmed/Armed Personnel Using the NetRAD Multistatic Radar for Micro-Doppler and Singular Value Decomposition Features , 2015, IEEE Geoscience and Remote Sensing Letters.

[15]  Francesco Fioranelli,et al.  Aspect angle dependence and multistatic data fusion for micro-Doppler classification of armed/unarmed personnel , 2015 .

[16]  Ram M. Narayanan,et al.  Determining human target facing orientation using bistatic radar micro-Doppler signals , 2014, Defense + Security Symposium.