Study of Target Penetration Feature Extraction and Signal Antialiasing Performance Based on Magnetoelectric Sensor

Hypervelocity penetrators play a crucial role in breaching fortified structures designed to safeguard high-value targets. However, the signals captured by conventional accelerometers during the hypervelocity penetration of projectiles are often compromised by signal aliasing, which poses challenges in extracting features from the penetration signals. This article proposes a high-g magnetoelectric velocity sensor with antialiasing capabilities. The sensor is sensitive to velocity loss produced when the projectile penetrates the target, rather than deceleration. The design concept and theoretical feasibility of the magnetoelectric sensor are detailed. Based on the sensor’s joint simulation model, the relationship between the attenuation of the sensor’s response to the oscillation signal and the signal frequency is analyzed. To quantitatively assess the magnetoelectric sensor’s antialiasing performance, the single-layer simulation overload and the penetration overload of a projectile penetrating a six-layer target plate are inputted to calculate the adhesion coefficients of sensor signals. The results indicate that the sensor effectively attenuates the high-frequency oscillation signal, thereby reducing the degree of adhesion of the feature signal. Compared with the adhesion coefficients of the acceleration signal, the magnetoelectric output signal shows reductions of more than 80% in both its average layer coefficient and average interlayer coefficient before filtering, followed by corresponding attenuations exceeding 60% after filtering. These results demonstrate the antialiasing performance of the proposed sensor. It is able to extract penetration features that cannot be distinguished by traditional accelerometers.

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Publication Details

Journal
IEEE Sensors Journal
Published
2024-09-30
DOI
https://doi.org/10.1109/jsen.2024.3466558
Citations
3
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
1.04
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article

Study of Target Penetration Feature Extraction and Signal Antialiasing Performance Based on Magnetoelectric Sensor

Meiyun Zhang, Li Sui, Yao He, Zheyu Yang
3 citations
IEEE Sensors Journal
Neural Networks and Applications
1.04
article

Study of Target Penetration Feature Extraction and Signal Antialiasing Performance Based on Magnetoelectric Sensor

Meiyun Zhang, Li Sui, Yao He, Zheyu Yang
article en
3 citations

Abstract

Hypervelocity penetrators play a crucial role in breaching fortified structures designed to safeguard high-value targets. However, the signals captured by conventional accelerometers during the hypervelocity penetration of projectiles are often compromised by signal aliasing, which poses challenges in extracting features from the penetration signals. This article proposes a high-g magnetoelectric velocity sensor with antialiasing capabilities. The sensor is sensitive to velocity loss produced when the projectile penetrates the target, rather than deceleration. The design concept and theoretical feasibility of the magnetoelectric sensor are detailed. Based on the sensor’s joint simulation model, the relationship between the attenuation of the sensor’s response to the oscillation signal and the signal frequency is analyzed. To quantitatively assess the magnetoelectric sensor’s antialiasing performance, the single-layer simulation overload and the penetration overload of a projectile penetrating a six-layer target plate are inputted to calculate the adhesion coefficients of sensor signals. The results indicate that the sensor effectively attenuates the high-frequency oscillation signal, thereby reducing the degree of adhesion of the feature signal. Compared with the adhesion coefficients of the acceleration signal, the magnetoelectric output signal shows reductions of more than 80% in both its average layer coefficient and average interlayer coefficient before filtering, followed by corresponding attenuations exceeding 60% after filtering. These results demonstrate the antialiasing performance of the proposed sensor. It is able to extract penetration features that cannot be distinguished by traditional accelerometers.

IEEE Sensors JournalVol. 24(22)
Beijing Institute of Technology (CN), China North Industries Group Corporation (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 19%
Neural Networks and Applications
1.04
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