Layover solution in multibaseline SAR interferometry

A Three-Dimensional Imaging Algorithm for Tomography Sar Based on Improved Interpolated Array Transform1

“…From [16] we can get that the height unambiguous range of 10-element full uniform array extends from −4.5 to 4.5 height resolution units.…”

“…τ m is the radar reflectivity of the mth sources. {x m (n)} N n=1 are the speckle complex vectors pertinent to the mth source, and v(n) is Gaussian thermal noise with zero mean and power σ 2 v [16]. ϕ m is the interferometric phase at the overall baseline, and a(ϕ m ) is the steering vector for the mth source, which can be written as…”

“…where A m = diag{a(ϕ m )}, and the autocorrelation function of the speckle complex vectors {x m (n)} N n=1 can be given by [16] …”

“…Suppose that all SAR images have been coregistered first, and then, the azimuth and slant range positions of each scatterer in all SAR images are the same. Because the interval between two pass is far less than the distance between the pass and the illuminated area, the K phase centers of tomography SAR can be aligned to form a linear array [16]. Therefore, the complex amplitudes of the pixels from the same azimuth-slant range resolution cell collected at the K phase centers of tomography SAR can be modeled as…”

Methods and Performances for Multi-Pass SAR Interferometry

“…The ensemble of target decorrelation and APS results in a non Gaussian distribution of the data, which complicates the a-priori assessment of the LDF estimator performance for any given scenario. In the existing literature the computation of InSAR lower bounds is commonly approached as the problem of estimating a set of deterministic parameters in presence of decorrelation noise (23), (24), whereas the role of the APS is in general neglected. Within this chapter it will be shown that, under the hypothesis that a single, distributed, target is present within the SAR resolution cell, the roles of target decorrelation and APSs may be jointly treated by exploiting the Hybrid Cramér-Rao Bound (HCRB), where the unknowns are both deterministic parameters and stochastic variables.…”