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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