An adaptive beam former is a device, which is able
to steer and modifies an array's beam pattern ... more An adaptive beam former is a device, which is able to steer and modifies an array's beam pattern in or der to enhance the reception of a desired signal, while simultaneously suppressing interfering signals thr ough complex weight selection. However, the weight selec tion is a critical task to get the low Side Lobe Le vel (SLL) and Low Beam Width. One needs to have a low S LL and low beam width to reduce the antenna's energy radiation/reception ability in unintended di rections. The weights can be chosen to minimize the SLL and to place nulls at certain angles. The conve rgence of the array output towards desired signal i s also very important for a good signal processing to ol of an adaptive beam former. A vast number of possible window functions are available to calculat e the weights for Smart Antennas. From the analysis of many of these algorithms, it is observed that th ere is a compromise between HPBW and SLL. But in case of smart antennas, both of these parameters mu st have low values to get good performance. In our earlier work it is proposed that Complex Least Mean Square (CLMS) and Augmented Complex Least Mean Square ( ACLMS) algorithms gives low beam widt h and side lobe level in noisy environment. Another neural algorithm Adaptive Amplitude Non Lin ear Gradient Decent algorithm (AANGD) has the advantage of more number of control parameters over CLMS and ACLMS algorithms. In this paper the hybrid of CLMS and AANGD is presented and this nove l hybrid algorithm has outperformed the hybrid algorithm of CLMS and ACLMS in the aspect of conver gence towards the desired signal.
An adaptive beam former is a device, which is able
to steer and modifies an array's beam pattern ... more An adaptive beam former is a device, which is able to steer and modifies an array's beam pattern in or der to enhance the reception of a desired signal, while simultaneously suppressing interfering signals thr ough complex weight selection. However, the weight selec tion is a critical task to get the low Side Lobe Le vel (SLL) and Low Beam Width. One needs to have a low S LL and low beam width to reduce the antenna's energy radiation/reception ability in unintended di rections. The weights can be chosen to minimize the SLL and to place nulls at certain angles. The conve rgence of the array output towards desired signal i s also very important for a good signal processing to ol of an adaptive beam former. A vast number of possible window functions are available to calculat e the weights for Smart Antennas. From the analysis of many of these algorithms, it is observed that th ere is a compromise between HPBW and SLL. But in case of smart antennas, both of these parameters mu st have low values to get good performance. In our earlier work it is proposed that Complex Least Mean Square (CLMS) and Augmented Complex Least Mean Square ( ACLMS) algorithms gives low beam widt h and side lobe level in noisy environment. Another neural algorithm Adaptive Amplitude Non Lin ear Gradient Decent algorithm (AANGD) has the advantage of more number of control parameters over CLMS and ACLMS algorithms. In this paper the hybrid of CLMS and AANGD is presented and this nove l hybrid algorithm has outperformed the hybrid algorithm of CLMS and ACLMS in the aspect of conver gence towards the desired signal.
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Papers by Yarlagadda Rama Krishna
to steer and modifies an array's beam pattern in or
der
to enhance the reception of a desired signal, while
simultaneously suppressing interfering signals thr
ough
complex weight selection. However, the weight selec
tion is a critical task to get the low Side Lobe Le
vel
(SLL) and Low Beam Width. One needs to have a low S
LL and low beam width to reduce the antenna's
energy radiation/reception ability in unintended di
rections. The weights can be chosen to minimize the
SLL and to place nulls at certain angles. The conve
rgence of the array output towards desired signal i
s
also very important for a good signal processing to
ol of an adaptive beam former. A vast number of
possible window functions are available to calculat
e the weights for Smart Antennas. From the analysis
of many of these algorithms, it is observed that th
ere is a compromise between HPBW and SLL. But in
case of smart antennas, both of these parameters mu
st have low values to get good performance. In our
earlier work it is proposed that Complex Least Mean
Square (CLMS) and Augmented Complex Least
Mean Square ( ACLMS) algorithms gives low beam widt
h and side lobe level in noisy environment.
Another neural algorithm Adaptive Amplitude Non Lin
ear Gradient Decent algorithm (AANGD) has the
advantage of more number of control parameters over
CLMS and ACLMS algorithms. In this paper the
hybrid of CLMS and AANGD is presented and this nove
l hybrid algorithm has outperformed the hybrid
algorithm of CLMS and ACLMS in the aspect of conver
gence towards the desired signal.
to steer and modifies an array's beam pattern in or
der
to enhance the reception of a desired signal, while
simultaneously suppressing interfering signals thr
ough
complex weight selection. However, the weight selec
tion is a critical task to get the low Side Lobe Le
vel
(SLL) and Low Beam Width. One needs to have a low S
LL and low beam width to reduce the antenna's
energy radiation/reception ability in unintended di
rections. The weights can be chosen to minimize the
SLL and to place nulls at certain angles. The conve
rgence of the array output towards desired signal i
s
also very important for a good signal processing to
ol of an adaptive beam former. A vast number of
possible window functions are available to calculat
e the weights for Smart Antennas. From the analysis
of many of these algorithms, it is observed that th
ere is a compromise between HPBW and SLL. But in
case of smart antennas, both of these parameters mu
st have low values to get good performance. In our
earlier work it is proposed that Complex Least Mean
Square (CLMS) and Augmented Complex Least
Mean Square ( ACLMS) algorithms gives low beam widt
h and side lobe level in noisy environment.
Another neural algorithm Adaptive Amplitude Non Lin
ear Gradient Decent algorithm (AANGD) has the
advantage of more number of control parameters over
CLMS and ACLMS algorithms. In this paper the
hybrid of CLMS and AANGD is presented and this nove
l hybrid algorithm has outperformed the hybrid
algorithm of CLMS and ACLMS in the aspect of conver
gence towards the desired signal.