Abstract:
Adaptive beamformers rely on accurate estimation of the auto-covariance matrix for
proper mitigation of interference and preservation of signals. Most practical beamform-
ers use time averaging for covariance estimation, but it is difficult to acquire enough time
snapshots in dynamic environments. Frequency averaging enables adaptation in rapidly
changing scenarios, but the technique assumes homogeneity throughout the fixed averaging
bandwidth. Cluttered environments with narrowband sources can break this assumption of
consistency across frequency. This leads to many artifacts and causes the adaptive beam-
former to under perform compared to a conventional filter. Separation of signal and noise
frequency bins for covariance estimation mitigates the artifacts caused by fixed frequency
averaging bands. Automatically segmenting data into signals and noise requires the use
of an unsupervised clustering algorithm and creates a data-adaptive frequency averaging
covariance estimator. This thesis proposes a data-adaptive covariance estimation approach
for beamspace MVDR beamformers in one time snapshot environments.
Using fixed frequency averaging bands increases the noise
floor and consequently com-
promises the detection of low SNR signals. The mismatch between the covariance estimate
and the applied data causes the beam pattern of the adaptive beamformer to become ill-
behaved. Clustering the data to provide separate covariance estimates for signal and noise
clusters mitigates these artifacts and preserves the detection of low SNR signals. This
thesis also shows that the SNR of a signal is correlated to the increase in noise
floor for
fixed frequency averaging beamformers. The higher the SNR of a narrowband signal be-
comes, the higher the noise
floor on all other frequency bins. The proposed data-adaptive
covariance estimate beamformer mitigates these artifacts and preserves the noise
floor. The
main drawback to the proposed approach is the large computational burden. Applying an
unsupervised clustering approach and creating additional weight solutions needed for every
cluster is expensive. If the resources are available, the proposed approach shows that fre-
quency averaging beamformers can operate in single time snapshot environments and retain
the benefits of an adaptive beamformer without any artifacts in the beamformer output.
