An efficient hybrid time reversal（TR） imaging method based on signal subspace and noise subspace is proposed for electromagnetic superresolution detecting and imaging. First, the locations of targets are estimated b...An efficient hybrid time reversal（TR） imaging method based on signal subspace and noise subspace is proposed for electromagnetic superresolution detecting and imaging. First, the locations of targets are estimated by the transmitting-mode decomposition of the TR operator（DORT） method employing the signal subspace. Then, the TR multiple signal classification（TR-MUSIC）method employing the noise subspace is used in the estimated target area to get the superresolution imaging of targets. Two examples with homogeneous and inhomogeneous background mediums are considered, respectively. The results show that the proposed hybrid method has advantages in CPU time and memory cost because of the combination of rough and fine imaging.展开更多
Cross-range scaling plays an important role in the inverse synthetic aperture radar(ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation(FFT). However, the F...Cross-range scaling plays an important role in the inverse synthetic aperture radar(ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation(FFT). However, the FFT technique is resolution limited, so that the FFT-based algorithms will fail in the rotation velocity(RV) estimation of the slow rotation target. In this paper,we propose an accurate cross-range scaling algorithm based on the multiple signal classification(MUSIC) method. We first select some range bins with the mono-component linear frequency modulated(LFM) signal model. Then, we dechirp the signal of each selected range bin into the form of sinusoidal signal, and utilize the super-resolution MUSIC technique to accurately estimate the frequency. After processing all the range bins, a linear relationship related to the RV can be obtained. Eventually, the ISAR image can be scaled. The proposal can precisely estimate the small RV of the slow rotation target with low computational complexity. Furthermore, the proposal can also be used in the case of cross-range scaling for the sparse aperture data. Experimental results with the simulated and raw data validate the superiority of the novel method.展开更多
基金supported by the National Natural Science Foundation of China(6130127161331007)+2 种基金the Specialized Research Fund for the Doctoral Program of Higher Education of China(2011018512000820120185130001)the Fundamental Research Funds for Central Universities(ZYGX2012J043)
文摘An efficient hybrid time reversal（TR） imaging method based on signal subspace and noise subspace is proposed for electromagnetic superresolution detecting and imaging. First, the locations of targets are estimated by the transmitting-mode decomposition of the TR operator（DORT） method employing the signal subspace. Then, the TR multiple signal classification（TR-MUSIC）method employing the noise subspace is used in the estimated target area to get the superresolution imaging of targets. Two examples with homogeneous and inhomogeneous background mediums are considered, respectively. The results show that the proposed hybrid method has advantages in CPU time and memory cost because of the combination of rough and fine imaging.
基金supported by the National Natural Science Foundation of China (61871146,61622107)the China Scholarship Council(201906120113)。
文摘Cross-range scaling plays an important role in the inverse synthetic aperture radar(ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation(FFT). However, the FFT technique is resolution limited, so that the FFT-based algorithms will fail in the rotation velocity(RV) estimation of the slow rotation target. In this paper,we propose an accurate cross-range scaling algorithm based on the multiple signal classification(MUSIC) method. We first select some range bins with the mono-component linear frequency modulated(LFM) signal model. Then, we dechirp the signal of each selected range bin into the form of sinusoidal signal, and utilize the super-resolution MUSIC technique to accurately estimate the frequency. After processing all the range bins, a linear relationship related to the RV can be obtained. Eventually, the ISAR image can be scaled. The proposal can precisely estimate the small RV of the slow rotation target with low computational complexity. Furthermore, the proposal can also be used in the case of cross-range scaling for the sparse aperture data. Experimental results with the simulated and raw data validate the superiority of the novel method.
