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.

This paper proposes an energy-efficient and high-accuracy gesture recognition framework to address the challenges of high computational complexity and interference susceptibility in conventional radar-based gesture recognition methods. Firstly, an enhanced Gaussian Mixture Model (GMM) with an optimized learning rate is introduced to improve anti-interference performance by exploiting spatial and velocity differences between gesture signals and target-like interference. Furthermore, a novel Spiking Neural Network (SNN) architecture is, proposed combining a 2D Convolutional Neural-Network (2D-CNN) for spatial feature extraction with a Long Short-Term Memory (LSTM) network for capturing, longterm temporal dependencies. This hybrid architecture effectively integrates short-term and long-term temporal dynamics to enhance recognition accuracy. Additionally, Spike-Timing-Dependent Plasticity (STDP) is incorporated to address the non-differentiability of spike-based data, thereby improving the network’s feature learning capabilities. To evaluate the proposed approach, a radar-based gesture dataset comprising seven gesture categories was constructed using a 60 GHz Frequency-Modulated Continuous Wave (FMCW) radar system. Experimental results demonstrate a recognition accuracy of 99.28%, alongside computational complexity and power consumption have better performance than the existing competitive methods, suiting power and resource-constrained environments.

Due to the range-angle two-dimensional beam control ability of frequency diverse array multiple-input multipleoutput (FDA-MIMO) radar, it has a wide range of application prospects in mainlobe interference suppression. Several robust beamforming methods for FDA-MIMO radar have been proposed in recent years. However, most of these methods require sufficient independent and identically distributed snapshots, which is difficult to satisfy for FDA-MIMO radar, especially in dynamic environments. Therefore, the problem of FDA-MIMO robust beamforming in sample-starved scenarios is considered in this paper, especially for an extreme case where only a single snapshot can be used for beamforming. This work proposes an improved single-snapshot robust beamforming method for FDA-MIMO radar. We eliminate the desired target component by forward-backward spatial smoothing, expand the number of samples, and construct a robust beamforming problem based on the worst-case performance optimal (WCPO) criterion. To reduce the computational complexity, the original problem can be divided into two convex optimization subproblems using the Kronecker product (KP) structure of the steering vector. The optimal weight vector can be obtained by iteratively solving the two convex optimization subproblems. In addition, the Shrinkage method is introduced to alleviate the mismatch of the covariance matrix caused by the lack of snapshots. Numerical examples show that the proposed method has better performance than the existing competitive algorithms in terms of output signal-to-interference-plus-noise ratio (SINR), robustness, and computational complexity.

Frequency diverse array (FDA) exhibits a phenomenon of distance-angle coupling, and existing decoupling methods suffer from drawbacks such as high side-lobe levels and computational inefficiency. However, traditional joint optimization methods face challenges of computational inefficiency and convergence issues by jointly optimizing frequency deviation and element positions. This letter proposes a FDA decoupling method based on sequential convex optimization (SCO) technology, which employs Taylor expansion techniques to approximate this high-dimensional non-convex optimization problem as a series of convex optimization sub-problems, enabling efficient solution. Simulation results demonstrate that the proposed method not only further improves the performance of the FDA antenna but also exhibits higher computational efficiency compared to heuristic optimization algorithms

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.

The range‐angle coupling characteristics of traditional frequency diverse array (FDA) will deteriorate target localization performance. To address this problem, several decoupling methods for FDA have been developed in the past decade, mainly including deterministic methods and global optimization methods. However, most of these methods are based on frequency offset design and do not fully utilize other design degrees of freedom, such as excitation and element spacing, which limits obtained pattern performance. In this letter, a low‐sidelobe FDA pattern synthesis method is proposed by jointly optimizing frequency offset, array spacing and excitation. The entire optimization process is treated as two sub‐optimization problems, that is, a non‐convex sub‐problem involves frequency offset and array spacing, and a convex sub‐problem involves excitation. By integrating the mayfly algorithm and convex programming techniques, this synthesis problem can be effectively solved. Compared with the current state‐of‐the‐art counterparts, the proposed method takes full advantage of the FDA's design freedom and generates a doted beampattern with lower sidelobes.