Time-Frequency Analysis of a Moving Human Doppler Signature

Abstract

Sensors and Electron Devices Directorate, ARLApproved for public release; distribution unlimited.ii REPORT DOCUMENTATION PAGE Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) February 20092. REPORT TYPE AUTHOR(S)Traian Dogaru, Calvin Le, and Getachew Kirose 5f. WORK UNIT NUMBER PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) U.S. Army Research Laboratory ATTN: AMSRD-ARL-SE-RU 2800 Powder Mill Road Adelphi, MD 20783-1197 PERFORMING ORGANIZATION REPORT NUMBER ARL-TR-4728 SPONSOR/MONITOR'S ACRONYM(S) 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution unlimited. SUPPLEMENTARY NOTES ABSTRACTIn this report we analyze the Doppler signature of a moving human with the purpose of finding temporal change patterns that would allow detection and classification of movers. The analysis is based on computer models that simulate the operation of a pulse-Doppler radar. We start with an animated human mesh in walking motion and compute the frame-by-frame radar return using electromagnetic solvers such as the Finite Difference Time Domain (FDTD). The results are processed in the timefrequency domain via Short-Time Fourier Transforms (STFT) and displayed as spectrograms. In the end, we try to distinguish patterns in these spectrograms that are characteristic to a certain human motion type. The analysis emphasizes the phenomenological aspects of the Doppler signature, by looking at the relative contribution of various body parts and the effect of frequency, aspect angle, polarization and other radar parameters.

Gait Variations in Human Micro-Doppler

“…We also do not simulate noise in the models. Highly accurate meshmodeled simulations of the human micro-Doppler signature have been done [15] but not with studies of the variability.…”

Analysis of Moving Human Micro-Doppler Signature in Forest Environments

“…A full wave technique, such as the one employed in this paper, provides better accuracy for more complex structures but requires a large number of computational resources. In [35], a realistic human mesh-based model is proposed with a radar signature modeling based on Finite Difference Time Domain (FDTD) technique. However, FDTD requires a fine mesh in the order of λ/50 for accurate modeling of curved objects, which becomes prohibitive for electrically large objects such as the human [36].…”

An UGS radar with micro-Doppler capabilities for wide area persistent surveillance

“…The motion of a human can be detected from the side as well as the front [10,24,29], though they are more difficult to detect, as can be seen in Figure 14. In order to develop features that are relatively insensitive to both azimuthal angle and depression angle, a series of features will have to be built upon human and animal micro-Doppler models [8,14,30,31,32]. Radar data itself is rich, allowing the use of micro-Doppler as a weak but long-range biometric [33], but can be limited when using an UGS version [34,35].…”