Comparison of Notch Depth for Constrained Least Mean Squares and Dominant Mode Rejection Beamformers
Date
2015-08-19
Authors
Bojja, Mani Shanker Krishna
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Abstract
Detection of low power signals in the presence of high power interferers is a common problem in spatial signal processing. Notch depth (ND) is defined as the response of the beamformer in the interferer direction when the beamformer is steered towards a specified look direction. This thesis analyzes the ND of the constrained LeastMean Squares algorithm proposed by Frost [1]. Several variants of the LMS algorithm are considered, and the algorithm is analyzed for the case of single and multiple interferers. The thesis compares the ND of the LMS beamformer to the ND of the Dominant Mode Rejection beamformer proposed by Abraham and Owsley [2]. The performance comparison indicates that DMR attains a deeper notch faster than LMS. The white noise gain of the two beamformers is approximately the same. Analysis of the computational complexity of the LMS and DMR algorithms indicates that DMR requires on the order of N times more floating point operations than LMS, where N is the size of the receiving array. Thus, DMR is a better choice for applications requiring fast convergence as long as the processor can handle the increased computational load.
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Keywords
DMR, Notch Depth, Frost LMS, Normalized LMS, FLOPS