Radar cross-sections of pedestrians at automotive radar frequencies using ray tracing and point scatterer modelling

Simulation of radar cross-sections of pedestrians at automotive radar frequencies forms a key tool for software verification test beds for advanced driver assistance systems. Two commonly used simulation methods are the computationally simple scattering centre model of dynamic humans and the shooting and bouncing ray technique based on geometric optics. The latter technique is more accurate but computationally complex. Hence, it is usually used only for modelling scattered returns of still human poses. In this work, the authors combine the two methods in a linear regression framework to accurately estimate the scattering coefficients or reflectivities of point scatterers in a realistic automotive radar signal model which they subsequently use to simulate range-time, Doppler-time and range-Doppler radar signatures. The simulated signatures show a normalised mean square error 81% with respect to measurement results generated with an automotive radar at 77 GHz.

[1]  Tong-Yee Lee,et al.  Real-Time Physics-Based 3D Biped Character Animation Using an Inverted Pendulum Model , 2010, IEEE Transactions on Visualization and Computer Graphics.

[2]  J Grajal,et al.  Facet Model of Moving Targets for ISAR Imaging and Radar Back-Scattering Simulation , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[3]  R. Rajesh,et al.  Pedestrian Detection in Automotive Safety: Understanding State-of-the-Art , 2019, IEEE Access.

[4]  Ram M. Narayanan,et al.  Classification of human motions using empirical mode decomposition of human micro-Doppler signatures , 2014 .

[5]  Joel T. Johnson,et al.  Simulation and analysis of polarimetric radar signatures of human gaits , 2014, IEEE Transactions on Aerospace and Electronic Systems.

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

[7]  Kang Li,et al.  Human Motion Capture Data Compression by Model-Based Indexing: A Power Aware Approach , 2007, IEEE Transactions on Visualization and Computer Graphics.

[8]  Sevgi Zubeyde Gurbuz,et al.  A kinect-based human micro-doppler simulator , 2015, IEEE Aerospace and Electronic Systems Magazine.

[9]  Francesco Fioranelli,et al.  Practical classification of different moving targets using automotive radar and deep neural networks , 2018, IET Radar, Sonar & Navigation.

[10]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[11]  Yangyu Fan,et al.  Pedestrian and Bicyclist Identification Through Micro Doppler Signature With Different Approaching Aspect Angles , 2018, IEEE Sensors Journal.

[12]  Gareth Wiliams Overdetermined systems of linear equations , 1990 .

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

[14]  S. Lee,et al.  Shooting and bouncing rays: calculating the RCS of an arbitrarily shaped cavity , 1989 .

[15]  Igal Bilik,et al.  Pedestrian motion direction estimation using simulated automotive MIMO radar , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Rini Sherony,et al.  Artificial Skin for 76–77 GHz Radar Mannequins , 2014, IEEE Transactions on Antennas and Propagation.

[17]  Hao Ling,et al.  Simulation and Analysis of Human Micro-Dopplers in Through-Wall Environments , 2010, IEEE Transactions on Geoscience and Remote Sensing.