Due to the rapid progress of electronic countermeasures, the corresponding means of electronic counter-countermeasures are urgently needed. To address the mainlobe jamming problem, frequency diverse array (FDA) radar has received extensive attention for its range-dependent feature. However, shortcomings arise such as the invading of new jamming styles that may degrade the detection performance along with the applications of FDA. In this letter, with the construction of accurate deceptive jamming models based on FDA multiple-input multiple output (FDA-MIMO), the suppression procedures are presented, which provide a new perspective for mainlobe jamming suppression considering the special jamming and drawbacks come along with FDA systems. At first, range-angle two-dimensional adaptive beamforming method is presented to eliminate the jamming model 1. Then range dimensional monopulse approach is proposed to alleviate the influence of jamming model 2. Simulation examples demonstrate the effectiveness of the proposed approach.

Efficient jamming recognition capability is a prerequisite for radar anti-jamming and can enhance the survivability of radar in electronic warfare. Traditional recognition methods based on manually designed feature parameters have found it difficult to cope with the increasingly complex electromagnetic environment, and research combining deep learning to achieve jamming recognition is gradually increasing. However, existing research on radar jamming recognition based on deep learning can ignore the global representation in the jamming time–frequency domain data, while not paying enough attention to the problem of lightweighting the recognition network itself. Therefore, this paper proposes a lightweight jamming recognition network (JR-TFViT) that can fuse the global representation of jamming time–frequency domain data while combining the advantages of the Vision Transformer and a convolutional neural network (CNN). The global representation and local information in the jamming time–frequency data are fused with the assistance of the multi-head self-attention (MSA) mechanism in the transformer to improve the feature extraction capabilities of the recognition network. The model’s parameters are further decreased by modifying the standard convolutional operation mechanism and substituting the convolutional operation needed by the network with Ghost convolution, which has less parameters. The experimental results show that the JR-TFViT requires fewer model parameters while maintaining higher recognition performance than mainstream convolutional neural networks and lightweight CNNs. For 12 types of radar jamming, the JR-TFViT achieves 99.5% recognition accuracy at JNR = −6 dB with only 3.66 M model parameters. In addition, 98.9% recognition accuracy is maintained when the JR-TFViT parameter number is further compressed to 0.67 M.

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar has received a lot of attention due to the advantages of waveform diversity. Suppression of mainlobe deceptive jamming can be effectively achieved with the degree of freedom (DOF) in the range domain. However, the existing research mainly focuses on non-coherent signals. The echo signal of VHF-FDA-MIMO radar for low elevation has its own unique characteristics. False targets cannot be suppressed with conventional beamforming methods. Thus, a signal model for VHF-FDA-MIMO radar subjected to deceptive jamming is established. The reconstruction of the covariance matrix and the estimation of the steering vector are implemented with the generalized MUSIC algorithm. In addition, a matching Capon reconstruction method is proposed to finish the robust suppression of false targets for the problem of self-cancellation in the absence of a priori information. Finally, the beampattern and performance curves of different methods are compared. The simulation results show that the methods can be effectively applied to the suppression of deceptive jamming in VHF-FDA-MIMO radar under the multipath effect.

Frequency diverse array (FDA) has characteristics different from phased arrays due to the frequency offset introduced. To explore the performance of FDA, we analyze the generalized ambiguity function of coherent FDA based on multi-channel mixers and matched filters. We build an accurate and complete coherent FDA ambiguity function. Then, the peak shift of the ambiguity function is proposed because of the irregular weighting term in the model. We define sensitive array element to evaluate the influence of the irregular weighting on the FDA ambiguity function. Finally, the ambiguity function shaping method is proposed based on waveform and weight vector cascade optimization. Numerical results show that the complete and principal ambiguity functions have differences; the better the Doppler tolerance and longer waveform, the less effect of irregular weighting. Meanwhile, the proposed cascade optimization method can effectively generate a coherent FDA ambiguity function with good local performance.

A multiple‐input multiple‐output (MIMO) radar with frequency diverse array (FDA) produces a range‐angle‐dependent steering vector, which can suppress the deceptive jamming in joint range‐angle domain. This in turn complicates the task of radar jamming. With the emergence and development of digital radio frequency memory (DRFM), radar electronic jamming technique has entered the age of coherent jamming. Based on the analysis of the superiority of the FDA‐MIMO radar in cancelling the deceptive jammers, a novel range gate pull‐off method via DRFM against FDA‐MIMO radar is proposed. Compared with the traditional deceptive jamming methods, the proposed method cannot be suppressed due to the mismatch in range. In particular, even the anti‐jamming methods based on power domain cease to be effective. The effectiveness of the proposed scheme in jamming the tracking performance of the FDA‐MIMO radar is demonstrated via computer simulations.