<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Addressing the problem of being easily interfered by false targets such as chaff and corner reflector when selecting missile targets, a multi-feature multi-model target selection method based on the technique for order preference by similarity to ideal solution (TOPSIS) is proposed if only single-target characteristics such as target position or radar cross section (RCS) size is used in the selection of missile targets. The target selection problem under the comprehensive utilisation of multiple features is regarded as a problem in decision-making based on multiple attributes. The BP network based on the target RCS frequency domain statistical features, the radial basis network based on the target polarisation feature and the radial size feature based on high-resolution range profile (HRRP) are realised. The comprehensive utilisation of C-support vector machine (SVM) and other target recognition methods and the comprehensive sorting of target selection, to a certain extent, enable anti-ship missiles to make the correct target selection more accurately. The simulation results display that the effective selection of ship targets can be achieved in the case of passive interference by surface ships.</p>
</abstract>

Addressing the problem of being easily interfered by false targets such as chaff and corner reflector when selecting missile targets, a multi-feature multi-model target selection method based on the technique for order preference by similarity to ideal solution (TOPSIS) is proposed if only single-target characteristics such as target position or radar cross section (RCS) size is used in the selection of missile targets. The target selection problem under the comprehensive utilisation of multiple features is regarded as a problem in decision-making based on multiple attributes. The BP network based on the target RCS frequency domain statistical features, the radial basis network based on the target polarisation feature and the radial size feature based on high-resolution range profile (HRRP) are realised. The comprehensive utilisation of C-support vector machine (SVM) and other target recognition methods and the comprehensive sorting of target selection, to a certain extent, enable anti-ship missiles to make the correct target selection more accurately. The simulation results display that the effective selection of ship targets can be achieved in the case of passive interference by surface ships.

TOPSIS missile target selection method supported by the posterior probability of target recognition

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Addressing the problem of being easily interfered by false targets such as chaff and corner reflector when selecting missile targets, a multi-feature...

TOPSIS missile target selection method supported by the posterior probability of target recognition

Publicado en línea: 31 mar 2022

Páginas: 713 - 720

Recibido: 23 may 2021

Aceptado: 06 dic 2021

DOI: https://doi.org/10.2478/amns.2021.2.00160

Palabras clave
vector machines, neural networks, TOPSIS, target recognition

© 2021 Zhang Jianqiang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

TOPSIS missile target selection method supported by the posterior probability of target recognition

Publicado en línea: 31 mar 2022

Páginas: 713 - 720

Recibido: 23 may 2021

Aceptado: 06 dic 2021

DOI: https://doi.org/10.2478/amns.2021.2.00160

Palabras clavevector machines, neural networks, TOPSIS, target recognition

© 2021 Zhang Jianqiang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
vector machines, neural networks, TOPSIS, target recognition