3results about How to "Suppress background clutter" patented technology

The invention discloses an infrared polarization multi-dimensional fusion imaging method based on nonlinear mapping, and belongs to the technical field of photoelectric detection and computational imaging. Performing Stokes vector solution on the input detection data to obtain a total intensity component, a polarization degree component and a polarization angle component; performing logarithmic domain mapping and gradient calculation on the total intensity component, and performing nonlinear enhancement based on local statistical characteristics on the polarization degree component to obtain an enhanced polarization degree; in an HSV color space, constructing a brightness component according to a logarithm domain mapping result, constructing a saturation component by combining the enhanced polarization degree and gradient information, constructing a hue component according to polarization angle information, and performing threshold constraint on the hue component based on the enhanced polarization degree; and a multi-dimensional fusion image is generated through color space inverse transformation from HSV to RGB. According to the method, background clutters are effectively suppressed, the image information entropy and the average gradient are remarkably improved, and high-contrast clear imaging of a weak and small target in a complex scene is realized.

This invention discloses an infrared weak target detection method and system based on conflict filtering and feature focusing. The method includes: extracting features from an input image using a backbone network; performing multi-scale feature fusion and conflict filtering on the extracted feature maps; then performing multi-scale feature fusion and conflict filtering on the feature maps obtained from the backbone network to obtain a final feature map; performing feature focusing on the final feature map; obtaining a fused feature image based on the feature maps obtained from multiple feature focusing; performing multi-scale feature fusion on the feature maps obtained from multi-scale feature fusion and conflict filtering, the final feature map, and the fused feature image using a feature pyramid to obtain three feature maps of different scales; and inputting the three feature maps of different scales into a detection head. This invention can effectively suppress background feature interference, enhance target feature representation, and achieve high-precision infrared weak target detection.

This invention discloses a method for predicting attribute scattering center parameters based on automatic radar component segmentation and a phase-coordinated network. First, the method utilizes a PointNet++ network with multi-scale grouping and normal vector features to automatically segment complex target point clouds into basic geometric components, and achieves physical diversity of ray data based on the segmentation results. Second, a phase-coordinated network is constructed, endowing the network with electromagnetic interference sensing capabilities through explicit phase encoding. Distributed covariance pooling is used to accurately capture the spatial topological shape distribution of rays. A multi-task decoupled prediction head and a physically constrained sensing loss function are used to achieve high-precision prediction of the attribute scattering center position, field value, amplitude, frequency dependence factor, and length distribution parameters. This invention significantly improves the modeling accuracy and physical consistency of the electromagnetic scattering characteristics of complex targets, and its inference speed is faster than the traditional ESPRIT algorithm, making it suitable for real-time radar target recognition and RCS reconstruction.