A Matching Pursuit-Based Vehicle Wheel Parameter Extraction Method from Micro-Doppler Radar Signal

Micro-Doppler effects of moving vehicles in a radar system are mainly induced by the rotation of wheels, whose features are closely related to the numbers, positions and radiuses of wheels. These parameters of wheels are critical for the vehicle classification and recog¬nition. However, most micro-Doppler features extraction works of vehicles are unable to explicitly extract parame¬ters of wheels. In this paper, a parameter extraction method of vehicle wheels using micro-Doppler features based on the matching pursuit (MP) is proposed. The mi¬cro-Doppler signals of wheels are generally weak compar¬ing to redundant echo signals induced by other irrelevant parts of the vehicle, which makes the micro-Doppler fea¬tures difficult to extract. In this case, several signal atom sets are created according to the motion states of irrele¬vant parts of vehicle and MP is performed to suppress the redundant signals. After the suppression, micro-Doppler signals induced by wheels have become the major part of the echo signal. Another atom set is generated according to the rotational motion of wheels to perform MP again. Then the wheel parameters, such as the estimated numbers, positions and radiuses, are extracted. Simulation results demonstrate that the proposed method is feasible in feature extraction of moving vehicle. Besides, the accuracy can be guaranteed when the signal-to-noise ratio is greater than –5 dB

RF-Based Low-SNR Classification of UAVs Using Convolutional Neural Networks

This paper investigates the problem of classification of unmanned aerial vehicles (UAVs) from radio frequency (RF) fingerprints at the low signal-to-noise ratio (SNR) regime. We use convolutional neural networks (CNNs) trained with both RF time-series images and the spectrograms of 15 different off-the-shelf drone controller RF signals. When using time-series signal images, the CNN extracts features from the signal transient and envelope. As the SNR decreases, this approach fails dramatically because the information in the transient is lost in the noise, and the envelope is distorted heavily. In contrast to time-series representation of the RF signals, with spectrograms, it is possible to focus only on the desired frequency interval, i.e., 2.4 GHz ISM band, and filter out any other signal component outside of this band. These advantages provide a notable performance improvement over the time-series signals-based methods. To further increase the classification accuracy of the spectrogram-based CNN, we denoise the spectrogram images by truncating them to a limited spectral density interval. Creating a single model using spectrogram images of noisy signals and tuning the CNN model parameters, we achieve a classification accuracy varying from 92% to 100% for an SNR range from -10 dB to 30 dB, which significantly outperforms the existing approaches to our best knowledge.Comment: 18 pages, 9 figures, 4 tables, journa