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The algorithm is based on processing the phase variations exhibited by target reflections along the slow-time trajectories.
Due to the different travel times of the target reflections and the shape of the scan geometry, the target responses exhibit nonlinear signatures when viewed on the domain.
The subspace decomposition method, presented in [25, 26], utilizes not only the approximately identical wall scattering characteristics across the array elements but also the higher strength of the wall reflections compared to that of target reflections.
Since SR image formation methods use either the time of arrival of the recorded responses or the wavenumber of the radiated waveforms, the wave speed in the propagation medium is required to accurately map the target reflections to their original spatial locations.
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Note that the target reflection coefficient α p (k) may vary from shapshot to snapshot [31].
In[3], maximizing the mutual information (MI) between a Gaussian-distributed extended target reflection and the received signal was suggested.
In all methods, the phase shift patterns are optimized to match their Fourier transforms to a target reflection spectrum.
In order to better understand this kind of data, a target reflection curve was analytically described, then, the total internal reflection (TIR) phenomenon was explained and illustrated.
In [11], the transmit signals are designed by minimizing the mean-square error of the estimate of the target reflection coefficient.
The procedure can not only enhance the target reflection while suppressing the noise by improving the signal to noise ratio (SNR), but also separate the background and reflection signals.
where the subscript 's' denotes the surveillance channel, σ(n,k) is the target reflection coefficient corresponding to the k th target, and η s ( n ) ( t ) is the additive noise.
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