Motivated by the sparsity of filter coefficients in full-dimension space-Time adaptive processing (STAP) algorithms, this paper proposes a fast ℓ1-regularized STAP algorithm based on the alternating direction method of multipliers to accelerate the convergence and reduce the calculations. The proposed algorithm uses a splitting variable to obtain an equivalent optimization formulation, which is addressed with an augmented Lagrangian method. Using the alternating recursive algorithm, the method can rapidly result in a low minimum mean-square error without a large number of calculations. Through theoretical analysis and experimental verification, we demonstrate that the proposed algorithm provides a better output signal-To-clutter-noise ratio performance than other algorithms.@en

This paper puts forward a reverse backprojection algorithm for the moderately efficient generation of raw data of natural scenes acquired with general SAR geometries; in particular, we are interested in monostatic and bistatic geosynchronous (GSO) geometries. The backprojection algorithm has the advantage of being arbitrarily precise for all acquisition modes and systems, and allows an exact accommodation of space-variant effects introduced by topography and the propagation through the atmosphere. Its exactness allows both to simulate any real scenarios and understand further optimization potentials. The reverse backprojection algorithm is presented and analyzed in the following pages. The algorithm is also validated using both simulated clutter and data from the Sentinel-1 mission.

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