Building feature extraction method based on double reflection imaging dictionary
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In the technology of building feature extraction, multi-path virtual image appears in the imaging result because the imaging scene is composed of complex wall model and the observation distance of the through-wall radar is short. In order to solve this problem, this paper proposes a double reflection imaging dictionary algorithm to extract building scattering center. In this algorithm, the propagation models of direct path and double reflection path are established with the direct path imaging dictionary and the double reflection imaging dictionary, the corresponding optimization problems are constructed. Finally, the multi-perspective fusion imaging steps are taken to suppress the false image according to the non-correlation of false image from the different reflection path. Experimental results verify the effectiveness of the proposed algorithm.
KeywordsThrough-the-wall radar imaging (TWRI) The direct path imaging dictionary The double reflection imaging dictionary Building dominant scatterers
Entropy of the image
Orthogonal matching pursuit
Through-the-wall radar imaging
UWB through-wall-radar imaging (TWRI) is a new technology in recent years, which can obtain high resolution images of buildings all day . TWRI technology can determine the layout of buildings and identify indoor location information, which has wide practical value in disaster relief, counter-terrorism operations and fire protection . At present, most TWRI systems observe the building’s wall imaging by multi view [3, 4], and the layout of the building is achieved by the radar image fusion method. 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.
Most of the energy in the building scene is only provided by a few strong scattering centers, which are corners, indicating that the parameter space of radar echo in the corner scattering center is of high sparsity [8, 9]. Therefore, building corners can be extracted by compressive sensing theory. 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 , a ghosting suppression method based on corner azimuth features is proposed. This method fuses multiple sub-aperture images with full-aperture images to eliminate multiple aperture ghosting. However, a large number of antenna arrays should be set around buildings, and the performance of this method is greatly influenced by the number and orientation of the antenna array. In practical application, it is difficult to select the proper azimuth of the sub-aperture and the number of electromagnetic wave data acquisition is huge, so the method is not practical.
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 , 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. In [6, 7], the influence of azimuth of antennas on building scatterers is considered, and an over complete dictionary based on antenna azimuth variables is proposed, which can completely restore location information of main scatters. However, the method in  considers the corner of a building as a simple point target, while the corner should be regarded as an extended target when detecting the building in the near field. This method fails to take into account the dihedral angle attribute of the corner and ignores the double reflection phenomenon of the corner echo, resulting in defocus, background noise, and more wall clutter in the corner imaging.
When using through-wall radar test building, the phenomenon of multipath effect exists in the actual environment. Antenna array receives not only the corner direct reflection echo signal, but also multipath transmission signals from the wall reflection and the second reflection signals from the corner. If the information contained in the double reflection wave of the corner is used as the supplement of the corner information, the false image can be effectively suppressed while the signal strength of the corner is strengthened.
From this point of view, in this paper, a combined algorithm based on direct wave dictionary and secondary wave dictionary is proposed to extract the scattering center of buildings and remove the multi-path ghost. Firstly, the corner is regarded as the extended target, and the direct path model and the double reflection model are constructed based on corner characteristics. Each path is regarded as an observation channel and concentrated on the same compressed sensing model. Then the direct path imaging dictionary and the double reflection imaging dictionary are constructed, and the single view imaging is obtained by using the compressed sensing method. Finally, the multi-perspective fusion imaging is used to suppress the false image. The method can not only make full use of the double reflection information of the corner to accurately extract the scattering center of the building, but also use the non-correlation of false image from the different reflection path to suppress a large number of false images and noises. The simulation and real data are used to prove the validity of the proposed approach.
This method can not only make full use of the information of corner echo to accurately extract the scattering center of buildings, but also use the false image non-correlation of different observation models to suppress a large number of false images and noises. Simulation and experimental results verify the effectiveness and feasibility of the proposed method.
The rest of the paper is organized as follows. In Section II, the direct path model and the double reflection model are introduced. In Section III and Section IV, the detailed processing steps of the proposed imaging dictionary and image fusion algorithm are described. In Section V, the GprMax simulation data and the real experimental data are provided to evaluate the performance of the proposed algorithm. The conclusions are drawn in Section VI.
In this section, several computer simulation and real data collection experiment results are provided to validate the proposed algorithm.
2.1 Simulation results
In order to more directly analyze the performance of the proposed algorithm, three evaluation criteria of target-to-clutter ratio (TCR), signal-to-clutter ratio (SCR), and entropy of the image (ENT) are provided in Table 2. The TCR value of an image is defined as the ratio between the highest pixel intensity value of the true corner location area to the maximum pixel intensity value of the clutter area. The higher the TCR value, the clearer the target is relative to the background clutter . The ENT value of an image determines the complexity of imaging, and the ENT value is smaller when the whole graph is less clutter. The SCR value of an image is the ratio of the power in the target regions to the power summation of the rest regions. When the background noise is more fully suppressed and the quality of the imaging results is better, the SCR is larger.
Comparison of effect of simulation experiment
The algorithm in 
The proposed algorithm
2.2 Experimental results
Comparison of the effect of measured algorithm
The algorithm in 
The proposed algorithm
This paper considers the problem of multipath ghosts elimination for the through-wall radar imaging. In the experiment of building feature extraction, while the observation distance between radar and building is relatively closer, the corner of a building is regarded as a complex scatter. So if the corner is regarded as a simple point target to be extracted, the image will appear defocus and more false images. In order to solve this problem, the double reflection imaging dictionary is proposed to extract the scattering center of the building by building a direct wave dictionary and the second reflection dictionary. Multi-perspective image fusion method is used to accurately identify the corner position of the building area, enhance the scattering amplitude of the corner, suppress a lot of wall clutter and background noise. The processing results of GprMax simulation data and experimental data show that the whole image is relatively clear.
4.1 TWRI signal model
4.2 The direct propagation path signal model
In the case of complex imaging scenes, the defocus and virtual images will appear in the imaging results if the corner of the building is regarded as a simple point target; therefore, the double reflection echo model of the corner is introduced in this paper.
4.3 The double reflection propagation paths signal model
4.4 Sparse representation of the image
In view of the corner model in the indoor environment, the direct path imaging dictionary and the double reflection imaging dictionary are constructed based on the sparse description [10, 11], and the virtual images are suppressed based on the non-correlation of noise in different dictionaries.
4.5 Image fusion of multiple dictionaries
Based on the above analysis, both the estimated image derived from the direct path imaging dictionary and the estimated images produced by the double reflection imaging dictionaries contain the target image of interest. Moreover, the elements will form a highlight focused region located at the position of the true target, and the noise in these images will not overlap .
The authors would like to thank Tingfa Xu for the support.
HD and TX conceived of the building feature extraction method based on double reflection imaging dictionary. All authors read and approved the final manuscript.
This work was supported by the Major Science Instrument Program of the National Natural Science Foundation of China under grant 61527802.
This work was supported by the Major Science and Technology Foundation of Guangxi Province under grant AA17204093.
The authors declare that they have no competing interests.
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