THROUGH WALL SURVEILLANCE USING ULTRAWIDEBAND RANDOM NOISE RADAR
Open Access
- Author:
- Lai, Chieh-Ping
- Graduate Program:
- Electrical Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- June 27, 2007
- Committee Members:
- Ram Mohan Narayanan, Committee Chair/Co-Chair
James Kenneth Breakall, Committee Member
John F Doherty, Committee Member
Shizhuo Yin, Committee Member
John Yen, Committee Member - Keywords:
- UWB
Through Wall Surveillance
Noise Radar
RF
Software Defined Radar - Abstract:
- Recent terrorist activities and law-enforcement situations involving hostage situations underscore the need for effective through-wall detection. Current building interior imaging systems are based on short-pulse waveforms, which require specially designed antennas to subdue unwanted ringing. In addition, periodically transmitted pulses of energy are easily recognizable by the intelligent adversary who may employ appropriate countermeasures to confound detection. A coherent polarimetric random noise radar architecture has been developed based on ultrawideband (UWB) technology and software defined radio, which has great promise in its ability to covertly detect obscured targets. The main advantages of the random noise radar lie in two aspects: first, random noise waveform has an ideal ¡§thumbtack¡¨ ambiguity function, i.e., its down range and cross range resolution can be separately controlled, thus providing unambiguous high resolution imaging at any distance; second, random noise waveform is inherently low probability of intercept (LPI) and low probability of detection (LPD), i.e., it is immune from detection, jamming, and interference. Thus, it is an ideal candidate sensor for covert imaging of obscured regions in hostile environments. For human activity characterization, different parts of the human body have different movements when a person is performing different physical activities. Also, there is great interest to remotely detect human heartbeat and breathing for applications involving anti-terrorism and search-and-rescue. Ultrawideband noise radar systems are attractive because they are covert and immune from interference. The conventional timefrequency analyses of human activity (usually including the short time Fourier transform iv (STFT), Wigner-Ville distribution (WVD), and wavelet analysis) are not generally adaptive to nonlinear and nonstationary signals. If one can decompose the noisy baseband signal containing human Doppler information and extract only the human-induced Doppler from it, the identification of various human activities becomes easier. We therefore propose to use a recently developed method, the Hilbert-Huang transform (HHT), since it is adaptive to nonlinear and nonstationary signals. When used with noise-like radar data, it is useful for covert detection of human movement. The HHT based signal processing can effectively improve pattern recognition and reject unwanted uncorrelated noise. In addition, backscattering information about the strong clutter and interference environment is lacking. Most of the previous through wall studies of radar imaging are based on the assumption of a light cluttered environment. However, in realistic scenarios, harsh clutter and multipath environments exist due to furniture, walls, etc. This study presents the EM modeling, system design, statistical analysis, signal processing, and experimental results of a realistic indoor environment consisting of real target.