| Summary: | 碩士 === 國立中正大學 === 資訊工程研究所 === 105 === The recognition of moving targets is very helpful for social security or smart living products. Most research achieve the goal of objects tracking and identification by using a camera to collect image data for feature extraction and analysis.
In this work, we use a continuous wave radar, and calculate the direction and speed of an moving object from its reflected radar wave according to the Doppler effect. Since different objects have their own motion behavioral patterns, we can perform classification or recognition tasks by analyzing its periodicity with time frequency analysis and extracting meaningful features. Our goal is to differentiate human and non-human objects like dogs, cars, and blank background.
After collecting the human and non-human gait data through a series of experiments, we proposed four combinations of features, and used K-nearest neighbor and Support vector machine classifiers to check if Fisher linear discriminant analysis is required as a preprocessing. The recognition rate under different feature combinations and classifiers were compared. We also establish a simple user interface to facilitate the real-time moving target recognition for future applications.
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