Interpolation-Weighted TSVD for Sparse Array Microwave Tomography

In microwave imaging with finite antenna arrays, the limited number of array elements constrains spatial sampling and degrades reconstruction quality. To enlarge the aperture effectively, virtual antennas are usually adopted. However, it may lead virtual data to dominate the reconstruction process, thereby amplifying artifacts. This work proposes an interpolation-weighted truncated singular value decomposition (IW-TSVD) framework that expands multistatic scattering matrix by using an integer interpolation factor. The proposed method preserves all physically measured antenna data and applies explicit weighting to virtual channels to suppress their influence. Simulations and hardware experiments show that IW-TSVD improves structural similarity index (SSIM), reduces the mean squared error (MSE), and suppresses artifacts compared with conventional TSVD and zero-padding-based interpolated TSVD, without increasing hardware complexity.