Yong Gao

In non-cooperative wireless communications, carrier frequency offset (CFO) arises from oscillator mismatch, doppler effect and time-varying channels. Along with noise, they can degrade signal quality and hampers subsequent signal processing. In this letter, we propose a robust carrier frequency offset estimation (CFOE) method under low signal-to-noise ratio (SNR) by leveraging hybrid denoising scheme and dual-channel convolutional attention network (DCA). To enhance noise robustness, we develop a hybrid denoising scheme combining adaptive dual-tree complex wavelet transform with phase-space reconstruction. The designed DCA model can extract deep features from the multi-domain features of the denoised signal. It employs an attention-based parallel dual-pooling mechanism to preserve local-global feature correlation during downsampling. In addition, the composite loss function is used to improve generalization ability and reduce overfitting. Compared with existing blind estimation methods, the experimental results demonstrate the effectiveness and robustness of the proposed CFOE method.

In order to realize on-line real-time monitoring of vehicle running status in enterprises, improve vehicle running supervision ability and vehicle mileage calculation accuracy, and improve vehicle compliance travel level, vehicle position positioning and track data collection are realized based on GPS positioning technology by installing vehicle intelligent terminal, and the positioning data are transmitted to the management platform through data communication service, real-time viewing and track playback of vehicle position and track are carried out through Siji map service through data receiving, processing and optimization processes. After filtering abnormal trajectory points, completing missing trajectory points and correcting trajectory, the vehicle trajectory can be fitted effectively, and the quality of vehicle trajectory data can be enhanced. Based on this, the purpose of vehicle mileage calculation optimization can be realized [1].

Determining the number of sources under low signal-to-noise ratio (SNR) and signal interference with the same frequency and modulation presents a significant challenge. To address this challenge, we propose a novel method for detecting the number of signal sources from single-channel that leverages signal reconstruction and deep learning. The method employs subspace projection based on the Hankel matrix to reconstruct the measured single-channel signals, effectively suppressing noise. Furthermore, we incorporate the correlation of information and the integrity of feature in the signal, by fusing the in-phase component, quadrature component, and frequency spectrum feature of the reconstructed complex signal into a one-dimensional feature suitable for convolutional neural network (CNN). To address the source number detection task, we design a one-dimensional CNN based on convolutional block attention module, transforming it into a classification problem. Finally, experimental measurements demonstrate the effectiveness of our proposed method, with an detection accuracy of 94% even at an SNR of −10 dB.

For the blind estimation of short-code direct sequence spread spectrum (DSSS) signal pseudo-noise (PN) sequences, the eigenvalue decomposition (EVD) algorithm, the singular value decomposition (SVD) algorithm and the double-periodic projection approximation subspace tracking with deflation (DPASTd) algorithm are often used to estimate the PN sequence. However, when the asynchronous time delay is unknown, the largest eigenvalue and the second largest eigenvalue may be very close, resulting in the estimated largest eigenvector being any non-zero linear combination of the really required largest eigenvector and the really required second largest eigenvector. In other words, the estimated largest eigenvector exhibits unitary ambiguity. This degrades the performance of any algorithm estimating the PN sequence from the estimated largest eigenvector. To tackle this problem, this paper proposes a spreading sequence blind estimation algorithm based on the rotation matrix. First of all, the received signal is divided into two-information-period-length temporal vectors overlapped by one-information-period. The SVD or DPASTd algorithm can then be applied to obtain the largest eigenvector and the second largest eigenvector. The matrix composed of the largest eigenvector and the second largest eigenvector can be rotated by the rotation matrix to eliminate any unitary ambiguity. In this way, the best estimation of the PN sequence can be obtained. Simulation results show that the proposed algorithm not only solves the problem of estimating the PN sequence when the largest eigenvalue and the second largest eigenvalue are close, but also performs well at low signal-to-noise ratio (SNR) values.