Recently, synchrosqueenzing transform (SST)-based time-frequency analysis (TFA) methods have been developed for achieving the highly concentrated TF representation (TFR). However, SST-based methods suffer from two drawbacks. The first one is that the TFRs are unsatisfactory when dealing with the multicomponent signals, the instantaneous frequencies (IFs) of which are closely adjacent or intersected. Besides, the exhaustive adjustment of window length is required for SST-based methods to obtain the optimal TFR. To tackle these problems, in this article, we first analyze the concentration of TFRs for SST-based methods. A deep learning (DL)-based end-to-end replacement scheme for SST-based methods, named TFA-Net, is then proposed, which learns complete basis functions to obtain various TF characteristics of time series. The 2-D filter kernels are subsequently used for energy concentration. Different from the two-step SST-based methods where the TF transform and energy concentration are separated, the proposed end-to-end architecture makes the basis functions used for extracting TF features more beneficial to energy concentration. The comprehensive numerical experiments are conducted to demonstrate the effectiveness of the TFA-Net. The applications of the proposed method to real-world vital signs, undersea voices and micro-Doppler signatures show its great potential in analyzing nonstationary signals.

Frequency estimation of 2-D multicomponent sinusoidal signals is a fundamental issue in the statistical signal processing community that arises in various disciplines. In this article, we extend the DeepFreq model by modifying its network architecture and apply it to 2-D signals. We name the proposed framework 2-D ResFreq. Compared with the original DeepFreq framework, the 2-D convolutional implementation of the matched filtering module facilitates the transformation from time-domain signals to frequency-domain signals and reduces the number of network parameters. The additional upsampling layer and stacked residual blocks are designed to perform superresolution. Moreover, we introduce frequency amplitude information into the optimization function to improve the amplitude accuracy. After training, the signals in the test set are forward-mapped to 2-D accurate and high-resolution frequency representations. Frequency and amplitude estimation are achieved by measuring the locations and strengths of the spectral peaks. We conduct numerical experiments to demonstrate the superior performance of the proposed architecture in terms of its superresolution capability and estimation accuracy.

Traditional vital sign monitoring devices typically involve direct contact with the skin using electrodes, making them unsuitable for daily vital sign monitoring due to the discomfort and skin damage. Remote detection technology provides an effective solution to these issues. This article introduces an efficient and robust algorithm for estimating vital signs using frequency-modulated continuous-wave (FMCW) radar. The integration of this method with emerging technologies such as the Internet of Things (IoT), enables long-term and contactless vital sign monitoring, which facilitates a new model of self-management for chronic diseases and their prevention. While the breathing estimation accuracy is typically constrained by noise, heart rate estimation is primarily hindered by strong interference from the breathing signal and its higher-order harmonics. A maximum likelihood estimatior based on the Newton’s method is derived and proposed in this paper to accurately assess the breathing and heartbeat frequencies by enhancing the precision of vital sign parameter estimation. The proposed algorithm is validated utilizing a 77 GHz FMCW radar and compared with a reliable reference sensor. Experimental results from eight subjects demonstrate that the proposed method enhances the estimation accuracy, outperforming both conventional spectral estimation and other methods. Specifically, the root mean square error between the reference sensor measurements and the estimations is lower than 1 bpm for breathing rates and 1.5 bpm for heart rates. Additionally, the Bland-Altman plots demonstrate a high level of agreement between these estimations and the reference measurements.

In modern communication systems, automatic modulation recognition (AMR) has broad application prospects but a large amount of training data is usually required. Due to the high cost of adding annotations to signals, it is of great significance to study AMR under the condition of few labeled samples. In this paper, an end-to-end semi-supervised radio signal recognition algorithm is proposed. In view of the poor anti-noise performance of the original IQ data, short-time Fourier transform (STFT) is introduced to mine the stable modulation characteristics. In order to overcome the noise problem caused by pseudo-labels, a mixed sample data augmentation (MSDA) method PFMix is proposed, and the consistency constraint of the model disturbance based on mean teacher (MT) framework and data disturbance based on PFMix is designed. Meanwhile, in order to provide pseudo-labels with high confidence, entropy regularization is constructed by taking the prediction variance of MT framework as correction factor. The experimental results using the open source RML2016.10A dataset show the superior performance of the proposed method. The average recognition accuracy with 1% labeled data is 88.82% at 0 dB, which is a significant improvement compared with state of the art in semi-supervised AMR.

Radar signal recognition is of great significance for threat analysis and intelligence support in radar electronic warfare. However it remains a challenging task due to (1) the variety of radar signal modulation types, (2)the increasingly saturated electromagnetic environment and the more serious influence of noise interference, and (3)the intrinsic real-time requirement of this application. In this paper, a novel automatic modulation recognition algorithm based on the joint architecture of convolution neural network (CNN) and the support vector machine (SVM) is proposed to comprehensively address these challenges. In particular, this algorithm first employs time-frequency analysis to better show the pulse representation in the time-frequency domain. Then a well-designed DCNN equipped with a hybrid attention (HA) mechanism and skip feature aggregation (SFA) is used to automatically learn the feature representations of distinctiveness from time frequency images. HA is designed to quickly capture the important information of local parts in sight and enhance the ability of the backbone network to mine high-level features. SFA is applied to selectively aggregate features among channels for self-monitoring, which improves the expression and generalization ability. Sixteen types of radar signals are used to verify the feasibility and effectiveness of this algorithm. The experimental evaluation shows that the proposed method not only achieve competitive accuracy with a better timeliness than state-of-the-art methods, but also achieves effective identification of most of the signal modulation types.

This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning architectures. During the preprocessing phase, datasets suitable for synchronization are created by integrating Kalman filtering, multiscale bandpass filters, and a periodic extraction method in the time domain. These data comprise data on chest micro variations, encapsulating a complex array of physiological and biomedical information reflective of cardiac micromotions. The Radar-based Stacked Deformable convolution Network (RSD-Net) integrates channel and spatial self attention mechanisms within a deformable convolutional framework to enhance feature extraction from radar signals. The network architecture systematically employs deformable convolutions for initial deep feature extraction from individual signals. Subsequently, continuous blood pressure estimation is conducted using self attention mechanisms on feature map from single source coupled with multi-feature map channel attention. The performance of model is corroborated via the open-source dataset procured using a non-invasive 24GHz six-port continuous wave radar system. The dataset, encompassing readings from 30 healthy individuals subjected to diverse conditions including rest, the Valsalva maneuver, apnea, and tilt-table examinations. It serves to substantiate the validity and resilience of the proposed method in the non-contact assessment of continuous blood pressure. Evaluation metrics reveal Pearson correlation coefficients of 0.838 for systolic and 0.797 for diastolic blood pressure predictions. The Mean Error (ME) and Standard Deviation (SD) for systolic and diastolic blood pressure measurements are -0.32 $\pm$ 6.14mmHg and -0.20 $\pm$ 5.50mmHg, respectively. The ablation study assesses the contribution of different structural components of the RSD-Net, validating their significance in the overall of model performance.