2026-09-20T20:51:36Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/122078 2024-03-01 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Singer, Andrew C 2023-12 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms The student, Manan Mittal, accepted the attached license on 2023-12-08 at 11:29. The student, Manan Mittal, submitted this Thesis for approval on 2023-12-08 at 11:30. This Thesis was approved for publication on 2023-12-08 at 14:36. DSpace SAF Submission Ingestion Package generated from Vireo submission #20180 on 2024-03-01 at 13:15:32 Reduced complexity adaptive beamformers Mittal, Manan Compressed Beamformer Sensor Arrays Random Projections Beamforming Source Separation Distributed Arrays 2023-12-08 In many applications of interest, one may benefit from the use of large sensor arrays to capture the desired signals at multiple spatial locations. However, in complex acoustic environments, signal-dependent approaches like matched filtering have high computational requirements, which can be impractical for real-time applications. In many commercially available systems, the real-time processing requirement restricts the system designers to use signal-independent beamformers, such as a delay and sum beamformer. This work explores the design of hybrid beamforming systems that first use signal-independent beamformers to project the sensor outputs into a space of lower dimension and in turn uses those preprocessed signals to perform signal-dependent beamforming. These systems easily generalize to distributed arrays and can be constrained to meet bandwidth and latency requirements over a network, as well. This work also investigates the use of universal algorithms in order to find a mixture of projections to maximise the signal-to-noise-ratio of the desired signal. The performance of hybrid systems is evaluated in simulation and with real world data. This thesis proposes a system for source separation in reverberant acoustic environments and hypothesizes configurations for beamspace preprocessors. We then extend the system to environments involving a moving listener by designing a hybrid generalized sidelobe canceler and proposing an energy-based beam mixing strategy to combine the outputs of multiple beamformers, which can be identified as an instantaneous Wiener combination. Further, we propose the use of a universal algorithm, in combining the outputs of several hybrid beamformers that can perform nearly as well as the full-complexity beamformer. We then propose using a performance-weighted blend of random preprocessors that is shown to approach the performance of the sensorspace variant over Monte-Carlo trials in a simulated environment. Next, main lobe protection is incorporated into the random projections to derive a universal compressed beamformer that is able to consistently achieve a lower output power than the full-complexity variant in line with Capon's criterion. Text eng https://hdl.handle.net/2142/122078 Copyright 2023 Manan Sanjeev Mittal M.S. Thesis Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng