  A class of novel STAP algorithms based on sparse recovery technique were
presented. Intrinsic sparsity of distribution of clutter and target energy on
spatial-frequency plane was exploited from the viewpoint of compressed sensing.
The original sample data and distribution of target and clutter energy was
connected by a ill-posed linear algebraic equation and popular $L_1$
optimization method could be utilized to search for its solution with sparse
characteristic. Several new filtering algorithm acting on this solution were
designed to clean clutter component on spatial-frequency plane effectively for
detecting invisible targets buried in clutter. The method above is called
CS-STAP in general. CS-STAP showed their advantage compared with conventional
STAP technique, such as SMI, in two ways: Firstly, the resolution of CS-STAP on
estimation for distribution of clutter and target energy is ultra-high such
that clutter energy might be annihilated almost completely by carefully tuned
filter. Output SCR of CS-STAP algorithms is far superior to the requirement of
detection; Secondly, a much smaller size of training sample support compared
with SMI method is requested for CS-STAP method. Even with only one snapshot
(from target range cell) could CS-STAP method be able to reveal the existence
of target clearly. CS-STAP method display its great potential to be used in
heterogeneous situation. Experimental result on dataset from mountaintop
program has provided the evidence for our assertion on CS-STAP.
