Human detection by Neural Networks using a low-cost short-range Doppler radar sensor

In this paper, we propose the human detection technique using Neural Networks to effectively classify the Doppler signals caused by human walking along with the background noise sources. The frequency or phase feature vectors converted from the given input signal are directly used as the input of Neural Networks. In addition, Gaussian noise is added in the input nodes of Neural Network in order to prevent the overfitting problem. We developed the low-cost & short-range K-band Doppler radar for the experiment. The proposed technique was examined with human walking data accompanied with the background noises caused by the fan, rain, snow, and other outdoor environmental factors. The trained Neural Network detection technique can detect human walking with 95.2% of the true positive rate and it has 4.6% of the false positive rate.