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This paper presents a linear synthetic focusing method (SFM) in order to mitigate the drawbacks of the classical multiple signal classification (MUSIC) algorithm for microwave imaging (MI). The determination of noise subspace dimension for MUSIC algorithm is a challenge when imaging an extended target. That is because an extended target comprises many point scatterers and a practical MI system provides an incomplete multistatic response (IMSR) matrix. The SFM obtains a focused multistatic response matrix for each location in the imaging region, which contains, mostly, the scattered field of a point scatterer at that location. In this way, the signal subspace is reduced to the one for a point scatterer, whose singular values are well-separated from those of the noise subspace. The proposed SFM is applied to the breast cancer detection using synthetic data. It is also applied to the experimental data given by Institute Fresnel to test the SFM for scenarios other than biomedical uses. It is shown that the SFM provides superior results over the classical MUSIC algorithm, especially for extended targets and IMSR matrices. [ABSTRACT FROM AUTHOR]

Multiple signal classification (MUSIC) method uses the multi-static response matrix (MSR) to locate small inclusions in a microwave imaging (MI) system. The MSR cannot accommodate all available data in an actual MI system because practical restrictions make some receivers be inactive for every active transmitter. The current work presents a method for completing the MSR acquired in practical systems. Using the least squares method, the complete MSR matrix is formed by the estimation of its unknown elements, associated to the signal at inactive receivers. The estimation algorithm employs the signal of other active receivers to obtain inactive receiver signals. In this way, all available data contributes in imaging, which improves the localization performance. The merits of our proposal are demonstrated using the Institut Fresnel experimental data sets as well as various realistic synthetic data. [ABSTRACT FROM PUBLISHER]

Multiple signal classification (MUSIC) is a high-resolution method in microwave imaging which is severely based on the number of singular values of a multistatic response matrix. However, the role of noise and multiple scattering is crucial to determine the true dimension of the signal subspace. In this article, we show that in the presence of multiple scattering between the targets, some singular values of the signal subspace will greatly be decreased which is a critical point when the noise is added to the system. In this case, these signal singular values will mix with the noise ones and the most popular MUSIC methods degrade and even fail to estimate closely spaced target locations. Here, we introduce a method to solve the ambiguity in determining the number of targets employing a pre-processing method, discrete stationary wavelet transform (DSWT). The DSWT is developed on the basis of features of the reflection coefficient between targets. The application of this algorithm to measurement data shows its superiority over another thresholding-based algorithm, empirical mode decomposition. The results indicate that DSWT+MUSIC yields accurate estimate of the target locations, even in the presence of considerable noise and multiple scattering in the received signal. [ABSTRACT FROM PUBLISHER]