For large-scale high-dimensional positioning scenes, the massive number of grid points brings challenges to the multi-target positioning algorithms based on compressed sensing. To cope with the challenges, a fast multi-target localization method based on direction of arrival is proposed. A compressed sensing model is constructed for multi-target localization based on the DOA sequence measured by positioning nodes. Then, a two-stage fast matching pursuit algorithm is presented for sparse reconstruction, which consists of preliminary estimation and supports rectification. A process similar to orthogonal matching pursuit algorithm is adopted to get preliminary estimate result, but no nonlinear operations is employed for complexity reduction. Then another iterative process is carried out to rectify the chosen supports in preliminary result sequentially. Simulation results verify the effectiveness and accuracy of the proposed method for multi-target localization.

The modulated wideband converter (MWC) is a recently proposed compressive sampling system for acquiring sparse multiband signals. For the MWC with digital sub‐channel separation block, channel gain mismatch and time delay will lead to a potential performance loss in reconstruction. These gains and delays are represented as an unknown multiplicative diagonal matrix here. The authors formulate the estimation problem as a convex optimisation problem, which can be efficiently solved by utilising least squares estimation. Then the calibrated system model is obtained and the estimates of the gains and time delays of physical channels from the estimate of this matrix are calculated. Numerical simulations verify the effectiveness of the proposed approach.

Last years, most sub-Nyquist sampling and parameters estimation methods for linear frequency modulated (LFM) signals are based on compressed sensing (CS) theory. However, nearly all CS reconstruction...

Multi‐coset sampling is a compressed sampling strategy for the acquisition of spectrally sparse signal. In previous discrete blind recovery methods, either the sensing matrix is really huge, or the sampled sequences are interpolated to the Nyquist rate at the first step, both leading to a high computational complexity. A new discrete blind reconstruction method is proposed in this study to reconstruct a multiband signal from its multi‐coset samples. In the proposed method, the fractional delay is implemented at sub‐Nyquist rate, so that the sampled sequences do not need to be interpolated to the Nyquist rate until the final step of the reconstruction process. Hence, the computational complexity of the reconstruction method is reduced. Moreover, a windowing procedure is employed in this method to avoid the spectral spreading effect. Numerical experiments are presented to demonstrate that the authors’ method outperforms previous methods in terms of computational complexity and accuracy.

In this paper, we conduct a comprehensive numerical study on the spiking dynamics characteristics and communication between unidirectionally coupled photonic neurons based on semiconductor ring lasers (SRLs). Firstly, we investigate the impact of external perturbation time, perturbation intensity, and frequency detuning on the spiking output of the first photonic neuron, the transmitting semiconductor ring laser (T-SRL). The results indicate that by adjusting the perturbation time of the external signal injected into the T-SRL, the generation of spiking signals can be precisely controlled, achieving controllability and repeatability. It is noteworthy that the bistable nature of SRLs results in similar characteristics in the outputs of the clockwise (CW) and counter clockwise (CCW) modes. Additionally, within the set parameter range, the number of spiking pulses increases as frequency detuning decreases. Subsequently, these spiking responses generated by the T-SRL can be successfully transmitted to the second photonic neuron, the receiving semiconductor ring laser (R-SRL), which responds by firing identical spiking pulses. These behaviors are analogous to the response mechanisms of neurons in the brain but occur at a sub-nanosecond scale. More importantly, we successfully demonstrate stable spiking transmission between the two SRLs photonic neurons. This discovery reveals significant potential for the application of SRLs-based photonic neuron networks in neuromorphic computing platforms.

Array optimization has recently received significant attention owing to its several advantages, such as larger array aperture and greater degrees of freedom (DOFs). However, current works focus on far-field sources, while array optimization for near-field sources has not been adequately investigated. Therefore, this work develops a new symmetry sparse array model for near-field sources based on the improved maximum inter-element spacing constraint (IMISC). The proposed symmetry IMISC (S-IMISC) array model has all the advantages of traditional sparse array models. Compared with traditional sparse array models, the S-IMISC array model affords more uniform DOFs and is less affected by mutual coupling. Additionally, in order to improve the real-time performance of near-field sources localization, the characteristic equation-based method (CEM) is used to obtain the azimuth information of near-field sources which can avoid eigenvalue decomposition (EVD), and a spectrum peak search and compression scheme is used to obtain the distance information by searching the partial area instead of the whole Fresnel area, thereby significantly reducing computation complexity. Extensive simulations verify the advantages of the proposed algorithm and the S-IMISC array model.