Radar jamming recognition aims to accurately recognize the type of jamming to provide guidance for radar countermeasures. Although previous deep learning-based methods have made promising performance, they mainly rely on convolutional neural network (CNN) based on local processing, which ignore the global information in the time-frequency domain data of the jamming signal and also require much inference time to obtain the final recognition results. In this paper, a novel few-shot jamming recognition network via time-frequency self-attention and global knowledge distillation (JR-TFSAD) is proposed by jointly considering the global information in the time-frequency spectrum of the jamming signal and the real-time performance of the recognition network. A time-frequency self-attention model (TFSA) is proposed to extract the global deep features of radar jamming signals by learning the correlation between two arbitrary points in the time-frequency spectrum of the jamming signal, thus improving the recognition accuracy. Moreover, to effectively reduce the inference time of the method while preserving the recognition accuracy, a global knowledge distillation model (GKD) is further constructed to perform jamming recognition by distilling the global knowledge from the TFSA model. The experimental results on the simulated and measured mixed dataset verify that the proposed method has higher recognition accuracy and shorter inference time compared to the state-of-the-art methods.

Orthogonal frequency division multiplexing (OFDM) is often used in integrated radar and communication (IRC) systems to improve the sharing of scarce spectrum resources and develop efficient solutions for intelligent vehicles. However, the communication symbols and pilot information carried by the integrated signal and the cyclic prefix of OFDM greatly limit the performance of the radar and communication. In this paper, we propose a joint sequence optimization-based OFDM waveform design method for IRC. First, the ambiguity function of the OFDM signal block and guard interval (GI) sequence are deduced in detail, and a new composite sequence with the cascading of Walsh code and m sequence is used to optimize the correlation of communication information, resulting in better ambiguity function characteristics. Then, a multivariate optimization model is explored, and the constrained particle swarm optimization algorithm based on weight allocation (WCPSO) is proposed to obtain the optimal GI sequence. It has lower sidelobe levels and higher information transmission efficiency. In addition, an improved received signal processing method is introduced to achieve stable communication synchronization and high-resolution distance and velocity estimation of multiple targets radar detection. Finally, numerical simulation results are presented to validate the effectiveness of the proposed method.

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is widely used in integrated radar and communication systems (IRCSs). Moreover, index modulation (IM) is a reliable OFDM transmission scheme in the field of communication, which transmits information by arranging several distinguishable constellations. In this paper, we propose a sparse reconstruction-based joint signal processing scheme for integrated MIMO-OFDM-IM systems. Combining the advantages of MIMO and OFDM-IM technologies, the integrated MIMO-OFDM-IM signal design is realized through the reasonable allocation of bits and subcarriers, resulting in better intercarrier interference (ICI) resistance and a higher transmission efficiency. Taking advantage of the sparseness of OFDM-IM, an improved target parameter estimation method based on sparse signal reconstruction is explored to eliminate the influence of empty subcarriers on the matched filtering at the receiver side. In addition, an improved sequential Monte Carlo signal detection method is introduced to realize the efficient detection of communication signals. The simulation results show that the proposed integrated system is 5 dB lower in the peak sidelobe ratio (PSLR) and 1.5 ×${10}^5$ lower in the number of complex multiplications than the latest MIMO-OFDM system and can achieve almost the same parameter estimation performance. With the same spectral efficiency, it has a lower bit error rate (BER) than existing methods.

Compared to traditional single orthogonal frequency division multiplexing (OFDM) radar and OFDM communication systems, integrated OFDM radar and communication systems have the advantages of improved sharing of scarce spectrum resources, a simple hardware structure and a reduced interference between signals. In this paper, a constraint relaxation-based robust OFDM multiplexing waveform (ROW-CR) design method for integrated radar and communication systems is proposed. Considering the influence of correlated clutter and jamming signals, transmission and reception models of different system platforms are established by allocating subcarrier complex weights of different antenna signals so as to determine radar conditional mutual information and communication channel capacity parameters. Meanwhile, a restricted closed model is introduced through the limit range of the target frequency response. Then, a robust OFDM waveform optimization problem for integrated radar and communication systems is constructed by the minimax criterion, and the closed-form solution is obtained by adopting an improved method based on trace function properties and constraint relaxation, resulting in a better radar and communication performance trade-off. In addition, a parameter hierarchical optimization-based robust OFDM waveform (ROW-PHO) design method is further explored to reduce the computational complexity, and this method can also ensure a low system performance loss. Finally, the numerical simulation results verify the effectiveness of the proposed methods.

Unmanned Aerial Vehicle (UAV) swarms have the characteristics of small size, high density, and agile maneuverability. These attributes have given rise to substantial difficulties for radar in achieving precise detection and resolution of UAV swarms. Meanwhile, their potential malicious use poses a significant threat to national security, making accurate identification of UAV swarms of utmost importance. This paper presents a super-resolution method for UAV swarms that integrates the coherent long-time integration (CLTI) technique with the gridless sparse recovery method based on iterative weighted atomic norm minimization (IW-ANM). In the method, a framework for UAV swarm detection and super-resolution processing is first established. Then, based on the framework, the IW-ANM algorithm is proposed. This algorithm encodes prior information into the Toeplitz constraint matrix, adopts a well-designed weight function, and finally super-resolves UAV swarms in the spatial dimension through iterative weighting. Numerical simulations demonstrate that, compared with the reweighted atomic norm minimization (RAM), atomic norm minimization (ANM), multiple signal classification (MUSIC), and so on, the proposed IWANM algorithm is more practical and robust, and has a better super-resolution performance in conditions of low SNR, highdensity swarms, and small angle intervals. Furthermore, a real experiment using Ku-band radar is conducted to validate the effectiveness of the proposed IW-ANM.

In the tracking of unresolvable group object (URGO), the common extended Kalman filter based on the multiplicative error model (MEM-EKF*) provides an effective solution to accurately estimate the extended shape. But in multiple URGOs tracking applications, the existing methods based on MEM-EKF* and joint probabilistic data association (JPDA) is not adequate for tracking the complex behavior of URGOs such as overlapping. An ideal is presented in this paper which use fuzzy clustering technique (FCT) to complete probabilistic data association between multiple URGOs and simultaneously estimate the shape of the URGOs through MEM-EKF*. A multiple URGOs tracking method called GMMEMMEM-EKF* is proposed: it utilizes the Gaussian mixture modelexpectation maximization (GMM-EM) clustering to achieve data association and estimate the states of multiple URGOs within the MEM-EKF*, which avoided the imprecision of traditional FCT such as fuzzy C-means clustering (FCM) in data association. In addition, this papers gives the solution of multiple URGOs tracking under dense clutter. The better performance and accuracy of our method in dealing with complex behaviors of URGOs is demonstrated by Monte Carlo simulations in comparison to state-of-the-art methods in literature