In this work, the Transformer Network (TRN) is applied to the automatic modulation classification (AMC) problem for the first time. Different from the other deep networks, the TRN can incorporate the global information of each sample sequence and exploit the information that is semantically relevant for classification. In order to illustrate the performance of the proposed model, it is compared with four other deep models and two traditional methods. Simulation results show that the proposed one has a higher classification accuracy especially at low signal to noise ratios (SNRs), and the number of training parameters of the proposed model is less than those of the other deep models, which makes it more suitable for practical applications.

A novel OFDM MIMO Radar is proposed in this paper in a scenario of coexisting with a communication system. For the purpose of avoiding interference to communication while maintaining MIMO radar's capabilities, such as, measuring signal's Direction of Departure (DOD), a collocated antenna array is divided to several overlapped subarrays. Mutually orthogonal OFDM waveforms exploiting orthogonal space-time block code are transmitted through these subarrays to obtain waveform diversity, which can be separated at the radar receiver. A MUSIC-like DOD estimation algorithm and Cramer Rao bound of DOD is proposed for overlapped subarrays' configuration in order to evaluate performance of the beamforming strategy. The beamformer is designed using an optimization technique at cost of maximizing the input SNR of radar while keeping communication rate constant. Due to non-convexity of the optimization problem, a two-step optimization algorithm is proposed. Simulation experiments validate theoretical analysis of the proposed precoding radar. Experiments also show robustness of the precoding scheme, and the ability of balancing the input SNR of radar and the communication rate.

The existing research on deep learning for radar signal intra–pulse modulation classification is mainly based on supervised leaning techniques, which performance mainly relies on a large number of labeled samples. To overcome this limitation, a self–supervised leaning framework, contrastive learning (CL), combined with the convolutional neural network (CNN) and focal loss function is proposed, called CL––CNN. A two–stage training strategy is adopted by CL–CNN. In the first stage, the model is pretrained using abundant unlabeled time–frequency images, and data augmentation is used to introduce positive–pair and negative–pair samples for self–supervised learning. In the second stage, the pretrained model is fine–tuned for classification, which only uses a small number of labeled time–frequency images. The simulation results demonstrate that CL–CNN outperforms the other deep models and traditional methods in scenarios with Gaussian noise and impulsive noise–affected signals, respectively. In addition, the proposed CL–CNN also shows good generalization ability, i.e., the model pretrained with Gaussian noise–affected samples also performs well on impulsive noise–affected samples.

Radar intrapulse signal modulation classification is an important work for the electronic countermeasure and there are mainly two categories of algorithms. The deep learning-based algorithms usually outperform the traditional feature extraction-based ones, but they may rely on massive labeled samples for training, which limits their practical applications. To solve this problem, the SS-LWCNN model which combines the semisupervised learning (SI-SL) with virtual adversarial training (VAT) and the light weight technology is proposed. VAT provides the proposed model with robustness to the local perturbation of samples, which improves the classification accuracy with limited labeled samples provided. The light weight technology greatly reduces the complexity of the model, which increases the speed of classification. As demonstrated by the simulation results, in the condition of limited labeled samples are available, the SS-LWCNN model obtains greater classification accuracy compared to the other models. As tested by both the white Gaussian noise and the impulsive noise affected signals data sets, the SS-LWCNN model shows stronger robustness than the comparable models. Furthermore, the SS-LWCNN model contains much fewer training parameters and less floating point operations (FLOPs) than the other models.

The data volume and computation task of MIMO radar is huge; a very high-speed computation is necessary for its real-time processing. In this paper, we mainly study the time division MIMO radar signal processing flow, propose an improved MIMO radar signal processing algorithm, raising the MIMO radar algorithm processing speed combined with the previous algorithms, and, on this basis, a parallel simulation system for the MIMO radar based on the CPU/GPU architecture is proposed. The outer layer of the framework is coarse-grained with OpenMP for acceleration on the CPU, and the inner layer of fine-grained data processing is accelerated on the GPU. Its performance is significantly faster than the serial computing equipment, and satisfactory acceleration effects have been achieved in the CPU/GPU architecture simulation. The experimental results show that the MIMO radar parallel simulation system with CPU/GPU architecture greatly improves the computing power of the CPU-based method. Compared with the serial sequential CPU method, GPU simulation achieves a speedup of 130 times. In addition, the MIMO radar signal processing parallel simulation system based on the CPU/GPU architecture has a performance improvement of 13%, compared to the GPU-only method.

The optical approach to estimate the direction-of-arrival (DOA) estimation of microwave signals has attracted a lot of attention recently. Most of the existing methods are based on a one-sensor array system, which converts the DOA estimation problem into an optical power estimation problem. Their main disadvantage is that additional work is needed to represent the relationship between the phase shifts and the optical powers before estimation. The algorithm proposed in this paper is based on an N -sensor array ( N > 2 ), where the optical power vector is obtained first and then a compressive sensing-based approach is applied to directly estimate the phase shift. As demonstrated by simulation results, the proposed algorithm can achieve much better estimation accuracy and robustness than existing optical DOA estimation algorithms considered.