Abstract
We propose a stacking method for ensemble learning to distinguish micro-Doppler signals generated by human walking from background noises using radar sensors. We collected micro-Doppler signals caused by four types of background noise (line of sight (LoS), fan, snow and rain) and additionally considered micro-Doppler signals caused by human walking combined with these four types of background noise. We firstly verified the effectiveness of a fully connected deep neural network (DNN) to classify 8 types of signals. The average accuracy was 88.79% for the test set. Then, we propose a stacking method to combine two base classifiers of different structures. The average accuracy of the stacking method on the test set was 91.43%. Lastly, we designed a modified stacking method to reuse feature information stored at the previous stage and the average test accuracy increased to 95.62%. This result shows that the proposed stacking methods can be an effective approach to improve classifier’s accuracy in recognizing human walking using micro-Doppler signals with background noise.
Highlights
Radar sensors are relatively robust to weather conditions and have the advantage of being able to detect large areas
We designed a modified stacking method to reuse feature information stored at the previous stage and the average test accuracy increased to 95.62%
This result shows that the proposed stacking methods can be an effective approach to improve classifier’s accuracy in recognizing human walking using micro-Doppler signals with background noise
Summary
Radar sensors are relatively robust to weather conditions and have the advantage of being able to detect large areas. Other studies tried to find meaningful features through statistical analysis [5,6,7,8] These approaches need a relatively long pre-processing time (window time) to extract a feature vector from the micro-Doppler spectrogram. In order to reduce the window time, References [14,15] proposed methods to directly extract features from raw data without any pre-processing and using them as the input to the designed classifier and they showed the possibility of classifying micro-Doppler signals without generating image data. It should be seriously considered how to improve the classification accuracy of the corresponding classifier To tackle this problem, in this paper, we propose a stacking method as an ensemble method of deep networks to distinguish micro-Doppler signals caused by humans walking from the background noise using radar sensors.
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