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| Citation: | YU Longlong, FENG Dong, WANG Jian, et al. Estimation of the minimum number of acquisitions for coprime tomographic synthetic aperture radar[J]. Journal of Radars, 2022, 11(4): 618–636. doi: 10.12000/JR22047
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Estimation of the Minimum Number of Acquisitions for Coprime Tomographic Synthetic Aperture Radar
DOI: 10.12000/JR22047
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Abstract
In the practical application of Tomographic Synthetic Aperture Radar (TomoSAR), the number of acquisitions is usually restricted due to their expensive cost. A coprime TomoSAR technique was proposed to reduce the required number of acquisitions by sparsely distributing acquisitions and elongating baseline aperture. To guarantee a reliable tomogram, this study aims to determine the minimum number of acquisitions for the coprime TomoSAR when adopting a subspace method for performing the tomographic reconstruction. However, the performance of the subspace method depends on multiple parameters. In light of this, the selection of acquisition times has to comprehensively weigh the effects of all these parameters on the reconstruction performance. To this end, a prerequisite for reliable reconstruction is established by quantifying the relationship between the sample eigenvalues and all related parameters. Compared with conventional estimation approaches for the minimum number of acquisitions, the proposed approach has twofold advantages of containing all related parameters and of having a closed-form expression. Finally, the simulation experiments verify that the number of acquisitions estimated by our approach is close to the minimum and can guarantee reconstruction reliability. -
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References
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- Figure 1. The acquisition geometry of the coprime TomoSAR
- Figure 2. The flow chart for the propose approach to estimate the minimum number of acquisitions
- Figure 3. Successful detection rate varies with the number of acquisitions (K=2; SNR1=0 dB, SNR2=10 dB; Δh=1 m)
- Figure 4. The RMSE of the height estimation varies with the number of acquisitions (K=2; SNR1=0 dB, SNR2=10 dB; Δh=1 m)
- Figure 5. Successful detection rate varies with the height separation (K=2; SNR1=0 dB, SNR2=10 dB; ρh=1 m)
- Figure 6. The RMSE of the height estimation varies with the height separation (K=2; SNR1=0 dB, SNR2=10 dB; ρh=1 m)
- Figure 7. Successful detection rate varies with the number of acquisitions (Δh=1 m; L=20; all the SNRs=0 dB)
- Figure 8. The RMSE of the height estimation varies with the number of acquisitions (Δh=1 m; L=20; all the SNRs=0 dB)
- Figure 9. Successful detection rate varies with the height separation (L=20; all the SNRs=0 dB; ρh=1 m)
- Figure 10. The RMSE of the height estimation varies with the height separation (L=20; all the SNRs=0 dB; ρh=1 m)
- Figure 11. The constraint relationship between the minimum number of acquisitions and the SNR (K=2; Δh=1 m; L=10)
- Figure 12. |<a1, a2>|/M varies with the number of acquisitions (d=7.5 m; K=2; Δh=1 m)
- Figure 13. The phase transition of the RMSE as a function of the number of acquisitions and the SNR (K=2; Δh=1 m; L=10)
- Figure 14. The reconstruction results from the uniform TomoSAR and the coprime TomoSAR which are designed by Ref. [9] vary with the SNR [the uniform TomoSAR (M=16; d=7.5 m; B=112 m); the coprime TomoSAR (M1=9, M2=8; d=1.8 m; B=112 m)]
- Figure 15. Successful detection rate calculated from the deviated tracks and the nominal tracks (K=2; all the SNRs=0 dB; L=20; ρh=1 m)
- Figure 16. RMSE of the height estimation calculated from the deviated tracks and the nominal tracks (the simulation parmeters are seted as the same as in Fig. 15)