Publication Date

2012

Document Type

Dissertation

Committee Members

Raj Bhatnagar (Committee Member), Ramana Grandhi (Other), Lang Hong (Committee Chair), Andrew Hsu (Other), Zhiqiang Wu (Committee Member), Kefu Xue (Committee Member), Xiaodong Zhang (Committee Member)

Degree Name

Doctor of Philosophy (PhD)

Abstract

Low cost un-modulated continuous wave (CW) radar (CW Doppler radar) can be used to measure the speed of a vehicle. Traditionally, a radar gun, a lidar gun or a speed camera is used to capture a speeding vehicle. A radar gun can either measure the fastest vehicle or the vehicle with the strongest reflection. If a radar gun is used, a police officer must determine which vehicle has the speed shown on the screen of the radar gun. A lidar gun can precisely detect a speeding vehicle, but it requires precise aiming. When a camera is used, a picture will be taken at a fixed location. For the first case, human error is unavoidable, for the second case, the aiming requirement makes it unsuitable for automated surveillance, and in the third case, the surveillance region is very limited. In order to solve these problems, we have invented an automatic traffic surveillance system (ATSS) using two CW Doppler radars (forward radar and side radar) and a video camera. An algorithm to balance in-phase and quadrature channel of directional CW Doppler radar based on spectrogram has been developed and tested on real highway data. A detailed architecture for Doppler speed tracking has been designed. Doppler speed tracks are initialized and extended by the side radar and further extended by the forward radar. Three algorithms have been developed for Doppler speed tracking. All algorithms have been tested on real highway data and simulated data. The results show that all three algorithms can successfully extract the Doppler speed tracks from CW radar signals.

Page Count

175

Department or Program

Ph.D. in Engineering

Year Degree Awarded

2012

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