Implementation of the Constrained Least Mean Squares (LMS) algorithm for Beamforming
الموضوعات : Majlesi Journal of Telecommunication Devices
1 - گروه مهندسی برق،واحد فسا،دانشگاه آزاد اسلامی،فسا،ایران
الکلمات المفتاحية: Least Mean Squares (LMS), signal-to-noise ratio (SNR), Adaptive Frost Beamformer (AFB),
ملخص المقالة :
A Beamformer is an array of sensors which can do spatial filtering. The objective is to estimate the signal arriving from the desired direction in the presence of noise and other interfering signals. A beamformer does spatial filtering in the sense that it separates two signals with overlapping frequency content originating from different directions. The aim of the paper was to study the different beamforming techniques and use the Constrained Least Mean Squares (LMS) filter for spatial filtering. An array of microphones was simulated in MATLAB and a simple delay and sum beamformer was implemented. The results were compared with that of a single microphone and it was observed that beamforming definitely gives a significant SNR improvement. A Constrained least mean square algorithm (also known as Frost Beamformer) was derived which is capable of iteratively adapting the weights of the sensor array to minimize noise power at the array output while maintaining a chosen frequency response in the look direction. The adaptive version of the Frost beamformer was simulated in MATLAB and it was observed that there was a significant improvement in the SNR as compared to the simple delay and sum beamformer.
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