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## LEAST MEAN SQUARE ALGORITHMPosted by: projectsofme Created at: Wednesday 24th of November 2010 05:13:27 AM Last Edited Or Replied at :Monday 18th of April 2011 01:46:46 AM | lms algorithm in mathematics ,
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nces)(tiiθrespectively. a(0θ) and a(i) represents the steering vectors for the desired signal and
interfering signals respectively. Therefore it is required to construct the desired signal from the
received signal amid the interfering signal and additional noise n(t). As shown above the outputs of
the individual sensors are linearly combined after being scaled using corresponding weights such
that the antenna array pattern is optimized to have maximum possible gain in the direction of the
desired signal and nulls in the direction of the interferers. The weights here will be computed
using LM..................[:=> Show Contents <=:] | |||

## LEAST MEAN SQUARE ALGORITHMPosted by: projectsofme Created at: Wednesday 24th of November 2010 05:13:27 AM Last Edited Or Replied at :Monday 18th of April 2011 01:46:46 AM | lms algorithm in mathematics ,
least mean squares algorithm,
linear minimum mean square error algorithms doc ,
mathematics,
mathmatics ,
least mean square method problems,
least mean square algorithm doc ,
least square algorithm,
seminar least mean square algorithm ,
estimate the mean vector and the covariance matrix,
least mean squares lms algorithms ,
mean square error algorithm,
maths seminar topics square ,
last mean square algoritmen,
| ||

uch that the antenna array pattern is optimized to have maximum possible gain in the direction of
the desired signal and nulls in the direction of the interferers. The weights here will be computed
using LMS algorithm based on Minimum Squared Error (MSE) criterion. Therefore the spatial filtering
problem involves estimation of signalfrom the received signal (i.e. the array output) by minimizing
the error between the reference signal , which closely matches or has some extent of correlation
with the desired signal estimate and the beamformer output y(t) (equal to wx(t)). This is a
classical Wei..................[:=> Show Contents <=:] |

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