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| Citation: | REN Yexian and XU Feng. Comparative experiments on separation performance of overlapping scatterers with several tomography imaging methods[J]. Journal of Radars, 2022, 11(1): 71–82. doi: 10.12000/JR21139
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Comparative Experiments on Separation Performance of Overlapping Scatterers with Several Tomography Imaging Methods
doi: 10.12000/JR21139
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Abstract
The tomographic technique has attracted much attention because of its ability to separate overlapping scatterers in urban Synthetic Aperture Radar (SAR) images. The general method of SAR Tomography (TomSAR) imaging combines the following two aspects: estimating the distribution of the scatterers in the elevation direction and determining the number of strong scatterers in an overlapped pixel. This study applied several sophisticated spectrum estimations (e.g., Orthogonal Matching Pursuit, Sparse Learning via Iterative Minimization and Multiple Signal Classification) and model order selection approaches (e.g., Bayesian information criterion and generalized likelihood ratio test) with highly technical potential to recover the simulated overlapping scatterers. This simulation experiment is based on the parameters of the AIRCAS X-band TomoSAR data from Emei, Sichuan, China. The Cramér-Rao Lower Bound (CRLB) and recovery probability are used to evaluate the performances of different methods for the separation of overlapped scatterers. The experimental results revealed the following: (1) the standard deviation of estimation using second-order statistics is smaller than that of a single observation vector, especially when the number of acquisitions is very limited; (2) the amplitude ratio, phase difference, and elevation spacing between overlapping scatterers will have a significant impact on the different kinds of algorithms; and (3) the phase difference between overlapping scatterers will make the phase center estimation of greedy algorithm or spectrum estimation algorithm biased. -
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References
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- Figure 1. Imaging geometry of tomographic SAR
- Figure 2. The Cramér-Rao Lower Bound (CRLB) of the Emei data
- Figure 3. The probability density function of scattering position estimated by OMP and its corresponding Gaussian mixture distribution fitting curve
- Figure 4. Elevation estimation accuracy of different algorithm (M=11, SNR=5 dB, ρs=23.7805 m)
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Figure 5. Recovery probability as a function of amplitude ratio
${a_1}/{a_2}$ -
Figure 6. Recovery probability as a function of phase difference
$\Delta \phi $ -
Figure 7. Recovery probability as a function of elevation distance
$\Delta s/{\rho _s}$ -
Figure 8. The disturbance of height estimation caused by phase difference
$\Delta \phi $ - Figure 9. Elevation estimation accuracy of MUSIC algorithm (When the amplitude ratio is fixed at 0.5, the position estimation of the scatterer with small energy is obviously deviated)
- Figure 10. The scene of Emei data (The walls of tall buildings and the roofs of low buildings are overlapped)
- Figure 11. Range-Elevation section corresponding to the red dotted line in Fig. 10 (without geocoding)
- Figure 12. Tomography results (after geocoding)