Robust calibration method for distributed ISAR time‐varying frequency errors based on the contrast maximisation principle

Traditional sonar image target detection analysis has problems such as long detection time, low detection accuracy and slow detection speed. To solve these problems, this paper will use the multi-feature fusion sonar image target detection algorithm based on the particle swarm optimization algorithm to analyze the sonar image. This algorithm uses the particle swarm algorithm to optimize the combination of multiple feature vectors and realizes the adaptive selection and combination of features, thus improving the accuracy and efficiency of sonar image target detection. The results show that: when other conditions are the same, under the particle group optimization algorithm, the sonar image multiple feature detection algorithm for three sonar image detection time between 4s-9.9s, and the sonar image single feature detection algorithm of three sonar image detection time between 12s-20.9s, shows that the PSO in multiple feature fusion sonar image target detection with better performance and practicability, can be effectively applied to the sonar image target detection field.

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Robust calibration method for distributed ISAR time‐varying frequency errors based on the contrast maximisation principle

Cited by 4 publications

References 33 publications

Efficient Nonparametric ISAR Autofocus Algorithm Based on Contrast Maximization and Newton’s Method

Efficient Nonparametric ISAR Autofocus Algorithm Based on Contrast Maximization and Newton’s Method

Pseudo 3-D ISAR Imaging by Barker’s Phase Code Modulated Waveforms

Multi-feature fusion sonar image target detection evaluation based on particle swarm optimization algorithm

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