Eigensubspace method for space–time adaptive processing in the presence of non-i.i.d. clutter and array errors

This study examines space–time adaptive processing in the presence of non-independent and identically distributed (i.i.d.) clutter and array errors. The authors propose a clutter rank estimation method by exploring the spatial–temporal steering vectors of clutter. The proposed method is independent of clutter statistics and direction-independent array errors. They prove that when the proposed clutter rank estimation is used, the estimate of the clutter subspace is asymptotically independent of clutter statistics. This enables an eigensubspace method to acquire the asymptotic independence on clutter statistics. In addition, they prove that the eigensubspace method can suppress the clutter regardless of direction-independent array errors. They also suggest a geometrical non-homogeneity detector for the eigensubspace method. Simulation and experimental results with multi-channel airborne radar measurement (MCARM) data confirm that the eigensubspace method can suppress non-i.i.d. clutter such as discrete clutter as well as correlated clutter regardless of array gain-phase errors. The ability to suppress clutter regardless of clutter statistics and direction-independent array errors makes the eigensubspace method unique and feasible to the practical scenario when clutter is non-i.i.d. and the direction-independent array errors are present.