Pattern Matching for Building Feature Extraction

Abstract

Abstract-We address the problem of detecting building dominant scatterers using a reduced number of measurements with applications to through-the-wall radar (TWR) and urban sensing. We consider oblique illumination, which specially enhances the radar returns from the corners formed by the orthogonal intersection of two walls. This letter uses a novel type of image descriptor, named correlogram, which encodes information about spatial correlation of complex amplitudes of each TWR image pixel. The proposed technique compares the known correlogram of the scattering response of an isolated canonical corner reflector with the correlogram of the received radar signal. The feature-based nature of the proposed detector enables corner separation from other indoor scatterers, such as humans.Index Terms-Building dominant scatterers, pattern matching, through-the-wall radar imaging (TWRI).

Building feature extraction method based on double reflection imaging dictionary

“…This treatment will cause that the layout of the buildings is not intuitive enough, and the walls cannot match each other with lots of image thorn. A few TWRI systems use the electromagnetic characteristics of scatterers to extract the main scattering objects from the wall echo data, and get clear and complete layout images by graph theory, and overcome the influence of clutter and multipath [5][6][7]. This paper also focuses on this type TWRI system.…”

“…In recent years, some research institutions have applied the compressive sensing theory to the TWRI system, and proposed many practical and efficient imaging algorithms. They divided the building scene into multiple grid points, assuming that the corner can be located at any grid point of the scene, and achieved the better imaging effect [4][5][6][7].…”

“…In order to reduce the number of data collection and time, and increase the scattering signal in the corner of the building, oblique illumination is considered, which specially enhances the radar returns from the corners formed by the orthogonal intersection of two walls [5][6][7]. In [7], an oblique MIMO radar antenna array is used to reduce the sidelobe of the corner, and the layout of the building through the corner location information is indirectly obtained. However, the wall compensation step is employed to compensate for the delay of the wall parallel to the antenna array, which is not applicable to the oblique antenna array, resulting in the deviation of corner position.…”

Development of Scale and Rotation Invariant Neural Network based Technique for Detection of Dielectric Contrast Concealed Targets with Millimeter Wave System

“…The recognition rate can be improved with various signal processing steps. To improve the quality of images, several researchers have demonstrated various signal processing steps like frequency domain processing, spatial filtering, intensity transformation, image restoration and image segmentation at microwave frequency range for hidden target identification [16][17][18][19][20][21] . However, these techniques need to critically analyse for fully utilisation in MMW images for concealed target identification.…”

Extraction of Building Features from Stand-Off Measured Through-Wall Radar Data

“…In literature, there is consensus that building features can be more reliably extracted using an approach exploiting scatterer models, e.g., [4], [15]- [19]. In this approach, the building structural elements are represented by canonical scatterers such as spheres, cylinders, corners, and planar surfaces.…”