Ubiquitous radar has significant advantages over traditional radar in detecting and identifying low, slow, and small (LSS) targets in a strong clutter environment. It effectively addresses challenges faced in low-altitude target monitoring within the low-altitude economy (LAE). The working mode of ubiquitous radar, which tracks first and identifies later, provides high-resolution Doppler data to the target identification module. Utilizing high-resolution Doppler data allows for the effective identification of LSS targets. To meet the needs of real-time classification, this paper first designs a real-time classification process based on sliding window Doppler data. This process requires the classifier to classify targets based on multiple rows of high-resolution Doppler spectra within the sliding window. Secondly, a multi-channel parallel perception network based on a 1D ResNet-SE network is designed. This network captures features within the rows of sliding window data and integrates inter-row features. Experiments show that the designed real-time classification process and multi-channel parallel perception network meet real-time classification requirements. Compared to the 1D CNN-MLP multi-channel network, the proposed 1D ResNet-MLP multi-channel network improves the classification accuracy from 98.71% to 99.34%. Integrating the 1D Squeeze-and-Excitation (SE) module to form the 1D ResNet-SE-MLP network further enhances accuracy to 99.58%, with drone target accuracy, the primary focus of the LAE, increasing from 97.19% to 99.44%.

By simultaneously transmitting multiple different waveform signals, a multiple-input multiple-output (MIMO) radar possesses higher degrees of freedom and potential in many aspects compared to a traditional phased-array radar. The spatial–temporal characteristics of waveforms are the key to determining their performance. In this paper, a transmitting waveform design method based on spatial–temporal joint (STJ) optimization for a MIMO radar is proposed, where waveforms are designed not only for beam-pattern matching (BPM) but also for minimizing the autocorrelation sidelobes (ACSLs) of the spatial synthesis signals (SSSs) in the directions of interest. Firstly, the STJ model is established, where the two-step strategy and least squares method are utilized for BPM, and the L2p-Norm of the ACSL is constructed as the criterion for temporal characteristics optimization. Secondly, by transforming it into an unconstrained optimization problem about the waveform phase and using the gradient descent (GD) algorithm, the hard, non-convex, high-dimensional, nonlinear optimization problem is solved efficiently. Finally, the method’s effectiveness is verified through numerical simulation. The results show that our method is suitable for both orthogonal and partial-correlation MIMO waveform designs and efficiently achieves better spatial–temporal characteristic performances simultaneously in comparison with existing methods.

The false‐target echo generation to Inverse Synthetic Aperture Radar (ISAR) is significant in jamming the enemy ISAR and promoting ISAR development. Generally, it requires false‐target echo coherent with the radar, real time and fine. However, conventional methods, such as digital image synthesizer (DIS), cannot meet those requirements. Moreover, existing methods do not consider the target's radial moving. To meet those demands, we propose an improved method in this study. We equivalently model echo formation as the synthesizer of two independent parts: (1) echo of remote target with radial moving and (2) echo of nearby extended target. In part one, accuracy is improved by utilising the Inner Pulse Motion (IPM) model and complexity is simplified by deducing it as a frequency offset modulation. In part two, the fine extended target echo is constructed by using convolution filtering whose resources consumption can be greatly reduced by separating it into an offline stage and a real‐time stage. Our method is verified by algorithm simulations and actual experiments. The results indicate that it can build the fine false‐target echo in real‐time and can adapt to the target's radial velocity, different resolution and size. Compared with the conventional DIS method, our method reduces the computational complexity significantly and has more comprehensive functions.