Dual-frequency continuous wave radar, as a promising Internet of Things, has been used for indoor human tracking, activity detection, and smart homes. Previous indoor tracking primarily used short-time Fourier transform (STFT) for instantaneous frequencies extraction. However, STFT has low resolution and suffers from Heisenberg’s uncertainty principle, which limits the positioning accuracy. Therefore, this article introduces an improved time-frequency analysis (TFA) algorithm called UTFA-Net, capable of significantly enhancing time-frequency resolution, potentially outperforming traditional principles, and thus boosting the precision of through-wall radar target tracking. The proposed framework is founded on an end-to-end self-supervised neural network architecture, integrating novel Rényi and consistency loss mechanisms. Meanwhile, we propose a multidimensional spectrogram generation module, short time fusion attention, and a nonlocal module to boost the model’s spectrogram generation capabilities. In order to validate proposed UTFA-Net, a comprehensive set of experiments is undertaken, including simulation tests, ablation studies, and indoor tracking experiments. The results indicate that UTFA-Net surpasses current advanced algorithms in four performance metrics, showcasing the highest tracking accuracy.

Doppler radar is a cost-effective Internet of Things (IoT) device widely utilized in smart homes, urban management, and health monitoring. Conventional Doppler radars, which detect targets from a single perspective, can only extract the radial information from the radar echoes and struggle to detect stationary targets or targets moving tangentially to the radar. Furthermore, the receivers commonly encounter the issue of ambiguous frequency (AF) simultaneously, making it difficult for conventional Doppler radar to track multiple targets accurately. To address these limitations, this article enhances the target detection capabilities of Doppler radars through the design of both radar hardware structure and Doppler frequency (DF) estimation algorithms. First, a multiperspective radar system is proposed to provide richer target information and substantially minimize the AF area. Second, a novel DF estimation algorithm, based on reservoir computing (RC) theory, is proposed to estimate the DFs of targets in these reduced ambiguous intervals. Lastly, an error compensation process, adapted to the characteristics of the echoes, is designed to reduce the accumulation of estimation errors. Compared to conventional Doppler radar systems, this approach reveals more precise target information and suppresses AF interference, a critical advantage in multitarget tracking environments.

Frequency-Modulated Continuous-Wave (FMCW) radar is extensively used in human activity recognition (HAR) due to its non-intrusive operation, all-weather performance, and anti-interference ability. However, FMCW signals predominantly relies on the Discrete Fourier Transform (DFT) method, which is limited by its fixed basis functions and restricted time-frequency resolution (TFR). Therefore, this paper proposes a cross-attention augmented end-to-end architecture for FMCW radar HAR called CASA-SWIN. The preprocessing module of CASA-SWIN learns the potential Range-Time (R-T), Range-Doppler (R-D), Dopper-Time(D-T) features of rawdata through multiple complex fully connected layers with preset parameters. Furthermore, it utilizes a three-branch cross-attention mechanism (TBCAM) module to extract intrinsic related information for R-T, D-T, R-D branches. For the D-T branch, proposed multi-window filtering module (MWFM) comprises multiple 1D complex convolutions with varying kernel sizes, emulating different window lengths in the Short-Time Fourier Transform (STFT). Finally, a three-branch swin-transformer is used as the HAR classifier. Moreover, proposed CASA-SWIN is evaluated on self-collected HAR, Gait recognition datasets, and open-source gesture recognition datasets. Compared to 12 advanced HAR algorithms, a series of experiments including comparative experiments, cross-environment experiments, open-source datasets experiments, and ablation experiments verify the effectiveness of the proposed module and CASA-SWIN.

Radar, with its privacy-preserving features and robust stability, shows immense promise in the field of wireless sensing. In previous research, the time-frequency analysis (TFA) methods are predominantly applied to preprocessed signals to produce spectrum for the activity recognition. Nevertheless, the limited resolution of spectrums, coupled with the constraints imposed by the Heisenberg uncertainty principle, results in frequency errors that can impact recognition accuracy. Therefore, we propose a network architecture for radar sensing called UHAR-Net, which consists of: Augmented Spectrum Network (AS-Net), Multi-Spectrum Fusion module, and Multispectral Behavior Identification Network (MBI-Net). Proposed AS-Net can enhance the time-frequency resolution (TFR) of the spectrum and suppresses the frequency errors. Multi-Spectrum Fusion module combines enhanced spectrums of different resolutions as input for the MBI-Net, which uses a complex-valued neural network (CVNN) for feature extraction and obtaining the final activity results. Moreover, proposed Frequency-Axis Phase Modulation module is used to improve the global feature extraction capability of the convolutional neural network (CNN). Subsequently, a series of experiments including human activity recognition (HAR) tasks, gait recognition tasks, and simulation signal experiments, ablation experiments, and parameter comparison experiments are performed. These experiments result suggest that the proposed UHAR-Net can be applied to a wider range of radar sensing fields.

In recent years, Doppler through-wall radar (TWR) have shown its potential in Internet of Things (IoT) applications like target positioning, smart security, and health monitoring. However, traditional time-frequency analysis (TFA) struggles with close-range or crossed instantaneous frequency (IF) from multiple targets, reducing positioning accuracy. To address this, we propose a novel algorithm, local maximum reassignment chirplet basis transform (LMRCBT). Firstly, the time-frequency characteristics of the target signal are analyzed using Short-Time Fourier Transform (STFT) and the time-frequency distribution (TFD) is chunked to ensure local consistency. Next, we introduce the chirplet basis function combined with a kurtosis-based local optimal Chirp domain theory to capture the optimal TFD and enhance signal energy concentration. Finally, we design a timefrequency reassignment operator (RO) to optimize the redistribution of time frequency coefficients, improving resolution and boosting noise suppression. The main contribution of LMRCBT is its effective handling of non-stationary signals, especially those with close-range, non-proportional, or crossed IF, improving target localization accuracy and suppressing timefrequency aliasing, as shown in experiments.

The range-Doppler (RD) spectrum of frequency-modulated continuous-wave (FMCW) radar is widely used in various fields, making high-resolution RD spectrum estimation a critical research focus. This letter proposes HRRD-Net, an end-to-end high-resolution RD spectrum estimation algorithm for FMCW radar. HRRD-Net uses 2-D complex signals as input and achieves strong anti-noise performance with a matched filtering module and denoising loss. A coarse RD spectrum is generated using a discrete Fourier transform (DFT)-initialized module, and its resolution is enhanced by a super-resolution module. Four experiments—perception, frequency accuracy, frequency resolution, and real super-resolution RD—demonstrate HRRD-Net’s ability to generate high-resolution RD spectra, even under noisy conditions, outperforming advanced RD estimation algorithms, and its frequency accuracy has been improved by approximately 37.78% compared to the Periodogram.

Doppler through-wall radar (TWR) is a promising device for the Internet of Things (IoT), effective for indoor tracking, health monitoring, and smart homes. However, employing it to estimate the trajectories of multiple targets presents challenges associated with time-frequency analysis (TFA). In this article, a multimodal network called MWVD is proposed, which eliminates the crossterm problem of Wigner-Ville distribution (WVD) and improves the accuracy of instantaneous frequency (IF) extraction to obtain accurate localization. In the MWVD, both the WVD spectrogram and the 1-D complex signals are used as inputs to the network. The complex signals are passed through the proposed multiwindow short-time filtering (MWSTF) module followed by an adaptive wavelet attention fusion (AWAF) module to simulate the wavelet transform. Subsequently, the enhanced WVD spectrogram is obtained by the energy compression module. As a result, comprehensive experiments, including simulated signal tests, module ablation studies, fusion mode ablation analyses, and real TWR target tracking, are conducted to demonstrate the proposed algorithm’s excellence, which will be combined with more IoT applications in the future.

Through-wall radar-based target localization algorithm has great promise in the Internet of Things (IoT), such as indoor positioning, health monitoring, and surveillance. However, when locating multiple targets, time-frequency aliasing occurs in the echo’s time-frequency distribution (TFD), which makes it challenging to accurately extract the target instantaneous frequency (IF) curve from the TFD, ultimately hindering the achievement of high-precision positioning. In this article, we propose a joint algorithm based on the improved linear chirplet transform (ILCT) and singular value decomposition (SVD) for target tracking, aiming to improve the localization precision of TWR. We design an ILCT algorithm to increase the time-frequency energy concentration of the target component of interest. Then, we use the SVD algorithm, based on the ILCT results, to accurately separate the target signal of interest from the echo signal. Finally, the motion path of the tracked target is synthesized based on the estimated target IF curves. Experimental results of target tracking demonstrated that the proposed method not only improves the target localization precision but also effectively suppresses the time-frequency aliasing phenomenon.

In target tracking applications by Doppler through-wall radar (TWR), the frequency ambiguity issue is a notable drawback, which can severely degrade the target localization performance. To overcome this drawback, a convolutional neural networks (CNNs) based target tracking algorithm is proposed in this letter. First, the short-time Fourier transform (STFT) is used to acquire an echo spectrogram. Then, the spectrogram resolution is enhanced by introducing a modified CNN module. Compared with the traditional CNN module, an additional convolutional weight block with a residual structure is designed to help extract deeper time–frequency features. Finally, the target instantaneous frequency (IF) curves are extracted from the enhanced spectrogram and the target positions are obtained. Experimental results prove that the proposed method can effectively enhance the spectrogram resolution and suppress the frequency ambiguity issue, which would lead to higher target localization accuracy and better robustness than traditional target tracking approaches.