An adaptive beamformer suppresses interferers and provides spatial filtering gains by making use of the sample covariance matrix. Updates to the sample covariance matrix reflect changes in the environment to which the beamformer must adapt. In environments with intermittent interferers, it is beneficial to remember the “state” that represents a specific pattern of interferer activity. In such cases, an adaptive beamformer that simply averages all the snapshots may result in reduced performance (with respect to an omniscient, context-aware beamformer that is aware of the interferer state) as the beamformer wastes degrees of freedom suppressing interferers that are always not active. By using the directional cosine of the peak of the beamformer scanned response as an information-bearing sequence, we partition the space into angular sectors that represent beamformers averaging a different set of snapshots. To represent and efficiently mix the output of all beamformers represented by such partitions, we employ a context tree that has been previously used for data compression and piecewise linear prediction. We use the context tree to achieve the signal estimation error of the best piecewise adaptive beamformer that can choose the partition of the directional cosine space.
Mittal et al. (Tue,) studied this question.