Adaptive Filtering and Beamforming Design Using MATLAB
Earners have completed a project on design and implementation of adaptive filtering algorithms in space and time. They demonstrated command of basic MATLAB functionality and its use in designing filters that learn the properties of undesirable interference and eliminate it from sampled signals. They understand the impact and trade-offs with various adaptive filter parameters, can tailor the design for specific requirements of a given application, and understand the limitations of the algorithms.
- Adaptive Filtering And Beamforming Design Using MATLAB
Create a function in MATLAB that implements the Recursive Least Squares (RLS) Adaptive Noise Canceller
Create a function in MATLAB that implements the Least Mean Squares (LMS) Adaptive Noise Canceller
Create a function in MATLAB that implements both a time- and data-adaptive spatial filter (beamformer)
Demonstrate the ability of all the designed functions to adaptively eliminate temporally and spatially varying interference using simulated audio data
Assess the impact on performance of filter parameters such as the training length, filter length, filter step size, filter memory, and initialization using real audio data