Classification and Modeling of Human Activities Using Empirical Mode Decomposition with S-Band and Millimeter-Wave Micro-Doppler Radars.
Open Access
- Author:
- Fairchild, Dustin Paul
- Graduate Program:
- Electrical Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- July 30, 2013
- Committee Members:
- Ram Mohan Narayanan, Dissertation Advisor/Co-Advisor
Ram Mohan Narayanan, Committee Chair/Co-Chair
Timothy Joseph Kane, Committee Member
Randy Young, Committee Member
Kamesh Madduri, Committee Member - Keywords:
- Micro-Doppler Radar
Empirical Mode Decomposition
Support Vector Machine
Human Motion Classification
Bistatic Doppler - Abstract:
- The ability to identify human movements can be an important tool in many different applications such as surveillance, military combat situations, search and rescue operations, and patient monitoring in hospitals. This information can provide soldiers, security personnel, and search and rescue workers with critical knowledge that can be used to potentially save lives and/or avoid a dangerous situation. Most research involving human activity recognition is focused on using the Short-Time Fourier Transform (STFT) as a method of analyzing the micro-Doppler signatures. Because of the time-frequency resolution limitations of the STFT and because Fourier transform-based methods are not well-suited for use with non-stationary and nonlinear signals, we have chosen a diff erent approach for classification. Empirical Mode Decomposition (EMD) has been shown to be a valuable time-frequency method for processing non-stationary and nonlinear data such as micro-Doppler signatures and EMD readily provides a feature vector that can be utilized for classification. For classification, the method of a Support Vector Machine (SVM) was chosen. SVMs have been widely used as a method of pattern recognition due to their ability to generalize well and also because of their moderately simple implementation. In this dissertation, we discuss the ability of these methods to accurately identify human movements based on their micro-Doppler signatures obtained from S-band and millimeter-wave radar systems. Comparisons will also be made based on experimental results from each of these radar systems. Furthermore, we will present simulations of micro-Doppler movements for stationary subjects that will enable us to compare our experimental Doppler data to what we would expect from an "ideal" movement. The Doppler radars that were developed for human activity classification consisted of a transmitter and a single receiver that are colocated in a quasi-monostatic con figuration. Thus, only the radial component of the target's velocity produces a Doppler signal. If the target is moving tangentially to the radar line of sight, Doppler signals cannot be detected. To remedy this, multiple bistatic radars can be utilized so that if one receiver does not detect Doppler, the other will. In addition to providing more information for classification purposes, multiple Doppler sensors can also be employed to determine a moving target's orientation by comparing the Doppler frequency shift at each sensor. The algorithm developed here uses the relationship between the Doppler frequencies measured at each sensor to determine the oscillation angle of the target. Experiments have been performed which show excellent agreement with simulations for both the mechanical motion of a swinging pendulum and also for simple human motions. These capabilities are discussed in detail and the experimental results are shown for a micro-Doppler radar system with a single transmitter and two receivers. Classification results using a 2-sensor micro-Doppler radar are be presented.