Multi-radar fusion technique for high-resolution ISAR imaging in sea-cluttered environment

Abstract

Radar imaging of the ship is one of the important means for surveillance and reconnaissance of targets. Here, the K-distribution is used for the statistical characterisation of echo model in sea-clutter environment. FEKO software is used to simulate the ship model. The multi-radar data fusion technique and Sparse Bayesian learning algorithm are used to generate ISAR target images in the different levels of sea condition. The simulation results show that the multi-radar data fusion algorithm not only has a significant inhibitory effect on sea-clutter but also improves the radar image resolution.

Keywords

• radar imaging
• marine radar
• synthetic aperture radar
• ships
• radar clutter
• learning (artificial intelligence)
• image resolution
• image fusion
• surveillance
• bayes methods
• radar resolution
• multiradar data fusion technique
• isar target images
• sea condition
• multiradar data fusion algorithm
• radar image resolution
• high-resolution isar imaging
• radar imaging
• statistical characterisation
• echo model
• sea-clutter environment
• feko software
• ship model
• sparse bayesian learning algorithm
• target surveillance
• reconnaissance
• k-distribution