The result of radar signal sorting directly affects the performance of electronic reconnaissance equipment. Sorting method based on intra-pulse features has become a research focus in recent years. However, as the number of extracted features increases, the dimension of the feature vector becomes higher and higher, which makes the complexity of the sorting algorithm increase geometrically. In this way, feature selection becomes more and more necessary. Combining the latest research on fuzzy rough sets, this paper proposes two feature selection methods, namely two-steps attribute reduction based on fuzzy dependency (TARFD) algorithm and fuzzy rough artificial bee colony (FRABC) algorithm. The TARFD method uses the candidate attribute set as starting point, according to the definition of the redundant attribute set. Then the less important attributes are successively eliminated. The FRABC method starts from the dependence degree of fuzzy rough set, and constructs a fitness function that reflects the importance of the attribute subset and the reduction rate. Based on this function, the artificial bee colony algorithm is used to reduce the attributes of the dataset. Using the TARFD and FRABC algorithms, the extracted feature sets, including entropy feature set, Zernike moment feature set, pseudo Zernike feature set, gray level co-occurrence matrix (GLCM) feature set, and Hu-invariant moment feature set are processed, then an optimal feature subset was obtained and a sorting test was performed. The results show the effectiveness of the extracted intra-pulse features and the efficiency of the feature selection algorithm.

In this letter, we consider the problem of secure communication in the downlink of a multi-cell multiple-input multiple-output (MIMO) system under imperfect channel state information (CSI) at the base stations (BSs). The passive eavesdroppers are considered, whose CSI are unavailable at the BSs. Coordinated Beamforming (CBf) and artificial noise (AN) are employed for secrecy enhancement. Based on regularized channel inversion (RCI) precoding, we derive a novel closed-form expression of secrecy sum-rate in the large-system regime. Based on the result, the regularization parameter and the power allocation ratio between the information signal and AN are optimized. Compared to the perfect CSI case, it shows that the channel estimation error can drastically reduce the secrecy sum-rate, and more power needs to be allocated for AN when the channel estimation error grows larger. The analytical results are validated with the finite-size system by simulation.

Sparse arrays are of deep concern due to their ability to identify more sources than the number of sensors, among which the hole-free difference co-array (DCA) with large degrees of freedom (DOFs) is a topic worth discussing. In this paper, we propose a novel hole-free nested array with three sub-uniform line arrays (NA-TS). The one-dimensional (1D) and two-dimensional (2D) representations demonstrate the detailed configuration of NA-TS, which indicates that both nested array (NA) and improved nested array (INA) are special cases of NA-TS. We subsequently derive the closed-form expressions for the optimal configuration and the available number of DOFs, concluding that the DOFs of NA-TS is a function of the number of sensors and the number of the third sub-ULA. The NA-TS possesses more DOFs than several previously proposed hole-free nested arrays. Finally, the superior direction of arrival (DOA) estimation performance based on the NA-TS is supported by numerical examples.

To address the weakness that the difference co-array (DCA) only enhances the degrees of freedom (DOFs) to a limited extent, a new configuration called the generalized nested array via difference–sum co-array (GNA-DSCA) is proposed for direction of arrival (DOA) estimation. We consider both the temporal and spatial information of the array output to construct the DSCA model, based on which the DCA and sum co-array (SCA) of the GNA are systematically analyzed. The closed-form expression of the DOFs for the GNA-DSCA is derived under the determined dilation factors. The optimal results show that the GNA-DSCA has a more flexible configuration and more DOFs than the GNA-DCA. Moreover, the larger dilation factors yield significantly wider virtual aperture, which indicates that it is more attractive than the reported DSCA-based sparse arrays. Finally, a hole-filling strategy based on atomic norm minimization (ANM) is utilized to overcome the degradation of the estimation performance due to the non-uniform virtual array, thus achieving accurate DOA estimation. The simulation results verify the superiority of the proposed configuration in terms of virtual array properties and estimation performance.

Aiming at the single stealth aircraft approaching support jamming, the support jamming suppression ability is insufficient, and if the jamming power is too large, it will be tracked and located. With the increase of hazard coefficient, a model of double stealth aircraft cooperative proximity support jamming is proposed. Based on solving the attitude angle of two aircraft, comparing with the static RCS database in the whole airspace and simulating the time-varying RCS of two aircraft, the influence degree of detection distance of monostatic radar is analyzed and verified by simulation using power co-suppression method. The results show that when the formation of two aircraft is 1 km, the suppression effect of the cooperative suppression and shielding task aircraft is better, which can effectively reduce its RCS, reduce the radar detection distance, improve the cooperative jamming effect and enhance the overall conduct operations level. Chinese Classification Number: V218; TN972+.1 Document Identification Code: A

Concerned with the problems that the extracted features are the absence of objectivity for radar emitter signal intrapulse data because of relying on priori knowledge, a novel method is proposed. First, this method gets the sparse autoencoder by adding certain restrain to the autoencoder. Second, by optimizing the sparse autoencoder and confirming the training scheme, intrapulse deep features are autoextracted with encoder layer parameters. The method extracts the eigenvectors of six typical radar emitter signals and uses them as inputs to a support vector machine classifier. The experimental results show that the method has higher accuracy in the case of large signal-to-noise ratio. The simulation verifies that the extracted features are feasible.