研究目的
To propose a new time reversal method for target imaging that achieves high resolution with limited array aperture and robustness in noise environments.
研究成果
The proposed SFF-DORT method effectively images targets with high resolution and robustness to noise, outperforming the traditional FF-DORT method, especially in cases with limited array aperture. It is applicable to moving targets and has potential uses in various imaging applications, though further research is needed for extensions to MUSIC methods and handling noise in more complex scenarios.
研究不足
The method assumes point-like and well-resolved scatterers, uses homogeneous medium Green's function approximations, and may have limitations in highly cluttered or non-ideal environments. The imaging quality depends on the accuracy of the SVD and the assumptions made in the signal processing.
1：Experimental Design and Method Selection:
The method involves forming a space–frequency–frequency multistatic data matrix (SFF-MDM) from received signals via a single measurement using an antenna array. Singular value decomposition (SVD) is applied to this matrix to extract left singular vectors, which are then divided into subvectors to exploit coarse frequency dependence and relative phase shifts for imaging.
2：Sample Selection and Data Sources:
Numerical simulations use point-like scatterers (e.g., perfect electric conductors) in a probed domain, with data generated using the finite-difference time-domain method. Experimental data is collected using a UWB radar system with a metal rocket model.
3：List of Experimental Equipment and Materials:
Antenna array (uniform linear array with 10 elements in simulations, 6 elements in experiments), UWB pulse source (sinusoidal modulated Gaussian pulse for simulations, PulsOn 440 transceiver for experiments), and computational tools for SVD and imaging.
4：Experimental Procedures and Operational Workflow:
Transmit UWB pulse from one antenna, record scattered signals with the array, transform to frequency domain to form SFF-MDM, apply SVD, divide vectors into subvectors, construct imaging function, and evaluate performance through simulations and experiments.
5：Data Analysis Methods:
Use SVD for signal subspace extraction, inner products for imaging function construction, and comparison with traditional FF-DORT method for resolution and noise robustness assessment.
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