The single-pixel imaging (SPI) technique enables the tracking of moving targets at a high frame rate. However, when extended to the problem of multi-target tracking, there is no effective solution using SPI yet. Thus, a multi-target tracking method using windowed Fourier single-pixel imaging (WFSI) is proposed in this paper. The WFSI technique uses a series of windowed Fourier basis patterns to illuminate the target. This method can estimate the displacements of K independently moving targets by implementing $6K$ measurements and calculating $2K$ windowed Fourier coefficients, which is a measurement method with low redundancy. To enhance the capability of the proposed method, we propose a joint estimation approach for multi-target displacement, which solves the problem where different targets in close proximity cannot be distinguished. Using the independent and joint estimation approaches, multi-target tracking can be implemented with WFSI. The accuracy of the proposed multi-target tracking method is verified by numerical simulation to be less than 2 pixels. The tracking effectiveness is analyzed by a video experiment. This method provides, for the first time, an effective idea of multi-target tracking using SPI.

Microwave computational imaging (MCI) employs uncorrelated illumination patterns via meta-materials to multiplex the information content of an entire scene into a few simple measurements, enabling image reconstruction through computational strategies. However, the reconstruction process often involves solving an ill-conditioned equation iteratively, which can result in a high computational burden and challenges in ensuring real-time performance. This paper presents a wavenumber spectrum reconstruction (WSR) method for fast imaging with reprogrammable metasurface (RMS). By constructing a series of code configurations, the electric field spectrum at the position of the RMS is measured. Then the wavenumber spectrum of the target is calculated by compensation in the wavenumber domain. Finally, the reconstruction of the target image is performed by inverse Fourier transformation (IFT) operation, which is less computationally burdensome and less time consuming. The proposed approach enables the construction of code configurations independent of the target's location and shape, thereby reducing the difficulty of optimizing measurement patterns. Simulation results show that the WSR method outperforms the traditional compressed sensing method in reconstructing target images with a low signal-to-noise ratio while significantly reducing computation time. The proposed method can satisfy the requirements for real-time performance and noise immunity in MCI systems, and can also be implemented on hardware platforms with limited computing resources.

Due to the introduction of frequency offsets, the pattern synthesis problem of sparse Frequency diverse array (FDA) becomes more complicated than that of the phased array. A typical way to solve this problem is to use a global optimization algorithm, but this is usually time-consuming. In this paper, we propose an efficient non-iterative beampattern synthesis approach for sparse FDA. For a given reference pattern, which can be generated by other synthesis methods, we first sample it uniformly and construct the Hankel matrix with the sampled data. By low-rank processing, a low-rank approximation version of the Hankel matrix can then be obtained. Finally, the matrix enhancement and matrix pencil (MEMP) and matrix pencil (MP) methods are applied to estimate the antenna positions, frequency offsets, and excitations of the obtained array from the approximated matrix. Besides this, two typical FDA frameworks including multi-carrier FDA (MCFDA) and standard FDA (SFDA) are considered. Numerical simulation results prove that the proposed method outperforms the existing methods in terms of synthesis error, average runtime, and percentage of saving elements.

Gestures serve as an intuitive and natural medium for conveying human intent and personal identity, offering a convenient, contactless, and privacy-preserving interaction modality for human–computer interaction (HCI) systems. This paper proposes a radar-based multimodal framework for joint gesture and identity recognition, aimed at enhancing performance in radar-based gesture-identity recognition tasks. First, a robust preprocessing and multimodal feature extraction method is introduced, which integrates gesture-range-based valid frame detection with clutter suppression, enabling the extraction of multidimensional gesture features including micro-Doppler maps (MDMs), elevation–time maps (ETMs), and azimuth–time maps (ATMs). Next, a novel Joint Recognition Framework with Cross-Modal Attention Fusion (JRF-CMAF) is proposed, which incorporates Adaptive Rectification Blocks (ARBs) to dynamically leverage the complementary and correlated information across modalities. Extensive experiments were conducted on a custom radar gesture dataset collected from 7 volunteers performing 7 distinct gestures. The proposed JRF-CMAF achieves accuracies of 99.76%, 97.57%, and 96.84% in gesture recognition, identity recognition, and joint recognition tasks, respectively. Compared with conventional gesture recognition approaches and existing radar-based identity recognition methods, it attains the highest overall recognition accuracy.

This letter presents a model based on the Cramér–Rao bound theory for calculating the theoretical lower limit of imaging resolution of microwave computational imaging aided by re‐configurable metasurface. The model takes into account two crucial parameters: the signal‐to‐noise ratio and the number of measurements in the imaging process. By simultaneously considering the signal‐to‐noise ratio and the number of measurements, as well as other parameters, the model allows for a comprehensive assessment of the imaging resolution of the imaging system. In addition, both position estimation and intensity estimation are considered, and a new composite statistical resolution concept is proposed, which is more suitable for microwave computational imaging.