To solve the problem of large training samples requirement of space time adaptive processing (STAP), a jointly sparse matrices recovery based method is proposed for clutter plus noise covariance matrix estimation by exploiting the transmitting waveform orthogonality of MIMO radar. The clutter spatio-temporal spectrum estimation problem is modelled as a sparse matrix reconstruction problem. Multiple training samples are generated based on the received signal of MIMO radar from a single training range cell. In order to recover the spatio-temporal spectrum matrices corresponding to different training samples, a jointly 2D iterative adaptive approach is formulated to provide high accuracy and efficiency. Compared with the existing sparse recovery based STAP method with single training range cell, due to the application of matricization process and MIMO radar, the proposed method can not only improve the clutter covariance matrix estimation accuracy, but also reduce the computational complexity. In the heterogeneous and non-stationary environment, the proposed method can achieve better clutter suppression performance than conventional sparse recovery based STAP methods using multiple training range cells. Simulation results are given to demonstrate the advantages of the proposed method over existing methods.

Most subspace-based algorithms need exact array manifold for direction of arrival (DOA) estimation, while, in practical applications, the gain-phases of different array elements are usually inconsistent, degrading their estimation performance. In this paper, a novel low-complexity 2D DOA and gain-phase error estimation algorithm is proposed by adding auxiliary array elements in a uniform rectangular array (URA). Firstly, the URA is modeled as the Kronecker product of two uniform linear arrays (ULAs) to decouple the 2D DOA estimation. Then, several well-calibrated auxiliary array elements are added in the two ULAs, based on which the rotation invariant factor of the URA destroyed by the gain-phase error is reconstructed by solving constrained optimization problems. Lastly, ESPRIT is used to estimate the 2-D DOA and the gain-phase error coefficients. The closed-form expressions of the estimation CRBs are also derived, providing insight into the impact of gain-phase error on DOA estimation. Simulation results are used to validate the effectiveness of the proposed algorithm and the correctness of the theoretical analysis.

To enhance the perception capability of radar in complex electromagnetic environments, this paper proposes an intelligent jamming recognition and parameter estimation method based on deep learning. The core idea of the method is to reformulate the jamming perception problem as an object detection task in computer vision, and we pioneer the application of oriented object detection to this problem, enabling simultaneous jamming classification and key parameter estimation. This method takes the time–frequency spectrogram of jamming signals as input. First, it employs the oriented object detection network YOLOv8-OBB (You Only Look Once Version 8–oriented bounding box) to identify three types of classic suppression jamming and five types of Interrupted Sampling Repeater Jamming (ISRJ) and outputs the positional information of the jamming in the time–frequency spectrogram. Second, for the five ISRJ types, a post-processing algorithm based on boxes fusion is designed to further extract features for secondary recognition. Finally, by integrating the detection box information and secondary recognition results, parameters of different ISRJ are estimated. In this paper, ablation experiments from the perspective of Non-Maximum Suppression (NMS) are conducted to simulate and compare the OBB method with the traditional horizontal bounding box-based detection approaches, highlighting OBB’s detection superiority in dense jamming scenarios. Experimental results show that, compared with existing jamming detection methods, the proposed method achieves higher detection probabilities under the jamming-to-noise ratio (JNR) ranging from 0 to 20 dB, with correct identification rates exceeding 98.5% for both primary and secondary recognition stages. Moreover, benefiting from the advanced YOLOv8 network, the method exhibits an absolute error of less than 1.85% in estimating six types of jamming parameters, outperforming existing methods in estimation accuracy across different JNR conditions.

The concept of a preserving source orthogonality and characteristic beam is defined according to an array data model. It is proven theoretically that the corresponding eigenvectors maintain the preserving orthogonality when the powers and the angle intervals between the signals are large. The theory is then applied to the direction of arrival (DOA) estimation for the array antenna in the presence of strong interference. A novel DOA estimation algorithm based on the elimination of the eigenvectors of interference is proposed. The simulation results and comparison of the jamming jam method (JJM) algorithm show that the proposed algorithm does not require the position of the interference source to be known. Moreover, its interference suppression ability and statistical estimation performance are superior to those of the JJM, and thus, it is more suitable for low SNR and small angle intervals.