Interrupted sampling repeater jamming (ISRJ) is an advanced coherent jamming with both suppression and deception effect. Effective countermeasures have been proposed, but most of these methods only consider ISRJ suppression in the case of single jamming or multiple jammings with the same parameters. In this paper, we propose a space-time complex modulation (STCM) technique based on colocated multiple-input multiple-output (MIMO) radar to cope with the threat of multiple ISRJs with different parameters. The STCM refers to encoding the carrier frequency of the signal transmitted by each array element, as well as randomly encoding the initial phase of each pulse emitted by each array element. This modulation method can separate the ISRJ mixed with the target in the transmitting angular frequency-range (TAFR) spectrum. Using this characteristic, we study the waveform parameter design criterion for the sufficient separation of targets and ISRJs. Moreover, the detailed STCM-MIMO waveform processing method is developed for ISRJ suppression. Simulation results are provided to prove the effectiveness of the proposed modulation method in ISRJ separation and the significant advantage of the proposed ISRJ suppression method over existing methods in the presence of multiple jammings.

MIMO ambiguity function integrates the effects of transmit waveforms and array geometry and characterizes the resolution properties of radar systems in the range, Doppler, and spatial domains. This paper considers the joint design of transmit waveforms and transmit array antenna positions based on optimization of the MIMO ambiguity function. Two design issues are considered here, one is to find orthogonal waveform set and transmit array geometry to obtain the highest resolution and low sidelobes, and the other is to match a desired transmit beampattern while reducing range sidelobes. Since sparse arrays provide larger array apertures and more degrees of optimization freedom than the uniform arrays with the same number of antennas, we consider the sparse array design by selecting a given number of antenna positions from a set of possible grid points with equal spacing. The joint optimization of transmitted waveforms and antenna positions has many advantages, but it also leads to Boolean-nonconvex problems that are difficult to solve. Therefore, we propose a multi-step method based on the nonlinear conjugate gradient (NCG) method and the limited-memory Broyden-Fletcher-Goldfarb-Shanno with bound constraints (L-BFGS-B) algorithm to solve the design problems. Simulation results demonstrate the proposed method can obtain the transmit waveforms and array configuration with the desired performance and is superior to the existing methods.

An adaptive beamforming technique for large-scale arrays has been proposed recently, where a subarray with much smaller number of antenna elements is selected from the original array to perform digital sampling, and the beamforming coefflcients for the original large array are calculated by using only the data samples from the selected subarray. In this paper, we consider various selections for the subarray that meet the requirements. We evaluate the performance of this adaptive beamforming method based on different subarray selections and get some useful conclusions on how to select the subarrays. Moreover, we also consider different weight calculation algorithms that are robust to the covariance matrix mismatch. Simulations are provided to compare the results obtained by different strategies.

As the electromagnetic environment becomes increasingly complex, radar encounters more intricate jamming patterns. Accurate and real‐time jamming recognition is crucial for effective radar anti‐jamming decisions. For scenarios with limited data, a rapid active jamming recognition method based on the Hybrid 3_CNN‐Transformer model is proposed. To thoroughly evaluate and validate this method, we simulated nine jamming types and used pulse compression for preprocessing. Combining the strengths of CNNs and Transformers, we constructed the Hybrid 3_CNN‐Transformer model, which employs three CNNs to extract local features from the complex domain, real part, and imaginary part of the jamming signals. After concatenating these features and performing position encoding, the model utilises Transformers to capture global features, enhancing recognition accuracy and reducing training time. To enhance computational efficiency and reduce storage, we applied an L1 norm‐based unstructured pruning algorithm for model compression, achieving an 82% pruning rate and cutting inference time to 33 ms. Experiments show that the Hybrid 3_CNN‐Transformer model significantly boosts recognition accuracy and speed over other models. On a small dataset with nine jamming types, it achieved 95.1% accuracy after 25 epochs and 100% after 90 epochs. This approach enhances training speed and accuracy, allowing rapid and reliable jamming recognition in resource‐limited environments.