Study On Adaptive Digital Beamforming At Subarray Level

The study on ADBF (Adaptive Digital Beamforming) methods has been carried through for more than thirty years and a lot of effective methods have been proposed, but they are basically based on element level. ADBF techniques have important applications in phased array system, now the array usually comprises hundreds even to thousands of elements, so it usually adopts subarray configuration. Thus we need adopt ADBF methods at subarray level. Now the studies on ADBF at subarray level are carried through rarely, and mainly based on the linear array. Actually phased array system adopts planes, so the study on 2-D ADBF methods at subarray level also has great importance.In this paper we mainly study two aspects of contents: ADBF signal model at subarray level and ADBF methods at subarray level.Based on the structure of the subarray, we construct the signal model at subarray level. By generalizing it to 2-D, we construct 2-D signal model at subarray level, this model has no limitation to the array configuration and can be applied to any plane array. The above models are appropriate to overlapped or non-overlapped subarrays.Combining the above signal model and the conventional LCMV method at element level we present a LCMV method at subarray level. In all kinds of jammer scenarios, the method can suppress jammers well, but the disadvantage is that the SLL (SideLobe Level) of adaptive patterns is much higher. The unequal noise powers of respective subarrays result in several SLLs closer to the mainlobe in the adaptive pattern increasing. So we propose normalization method by normalizing the outputs of subarrays which can suppress the SLL well.Quiescent pattern control is important for radar systems equipped ADBF and spatial adaptivity. And we hope that the ADBF methods at subarray level should possess the quiescent pattern keep ability, so that there is no need to switch between adaptive and non-adaptive operation modes. So we present two methods: the MOD method by introducing mismatched control vector and the SSP algorithm by partitioning subspaces to reduce the dimensions of adaptive process.