Electromagnetic vector sensors (EMVS) arrays have been employed extensively in the field of array signal processing for their advantage in terms of polarization diversity. However, with the introduction of EMVS, the cost of the hardware equipment and the complexity of the corresponding parameter estimation algorithms increase considerably, as the number of received signal channels and the dimension of the received signal are much larger than the traditional scalar array. In order to effectively reduce hardware cost and algorithm complexity, we propose a scheme that combines an electromagnetic vector sensor array with a compression network. We construct the corresponding signal model and based on this we derive a Compressed Reduced Dimensional MUltiple SIgnal Classification (Compressed To avoid the multi-dimensional search, Reduced Dimension MUSIC) algorithm which can effectively reduce the computational complexity. While selecting the coefficient matrix of the compressed network, random selection can cause information loss, which leads to the performance degradation of the estimation algorithm. To address this problem, we propose an optimization method for coefficient matrix selection based on the maximum signal-to-noise ratio (SNR) criterion. Numerical simulations are conducted in different scenarios to verify the effectiveness of the parameter estimation algorithm and the optimization algorithm.

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Chaff is widely used in electronic countermeasures as an effective passive jamming method. This study proposes a chaff identification method that integrates the distribution of distance, Doppler frequency, and power in Range-Doppler imaging. The traditional radar signal processing method estimates the distance and Doppler frequency, and the mean-shift algorithm is combined to complete the clustering after the constant false alarm detection. Then, the target and chaff cloud models are modelled in three dimensions: radar cross-section, range, and Doppler frequency. Using the Neyman-Pearson criterion, three chaff likelihood ratio test detectors are designed based on these assumptions. The three detectors can effectively identify the chaff, and then the theoretical detection probability and influencing factors are analysed. At the same time, the Kullback-Leibler divergence is used to describe the distribution difference between the data to be detected and the theoretical target and chaff. Besides, the different classifiers are used to identify the features of the differences in Kullback-Leibler divergence in three dimensions. Finally, the real-life chaff datasets verified the excellent recognition rate of the authors' method for the chaff.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Received signal Direction of Arrival (DOA) estimation represents a significant problem with multiple applications, ranging from wireless communications to radars. This problem presents significant challenges, mainly given by a large number of closely located transmitters being difficultly separable. Currently available state of the art approaches fail in providing sufficient resolution to separate and recognize the DOA of closely located transmitters, unless using a large number of antennas and hence increasing the deployment and operation costs.In this paper, we present a deep learning framework for DOA estimation under Line-of-Sight scenarios, which able to distinguish a number of closely located sources higher than the number of receivers' antennas. We first propose a formulation that maps the received signal to a higher dimensional space that allows for better identification of signal sources. Secondly, we introduce a Deep Neural Network that learns the mapping from the receiver antenna space to the extended space to avoid relying on specific receiver antenna array structures. Thanks to our approach, we reduce the hardware complexity compared to state of the art solutions and allow reconfigurability of the receiver channels. Via extensive numerical simulations, we demonstrate the superiority of our proposed method compared to state-of-theart deep learning-based DOA estimation methods, especially in demanding scenarios with low Signal-to-Noise Ratio and limited number of snapshots.