Near Optimal Robust Adaptive Beamforming Approach Based on Evolutionary Algorithm

Mallipeddi, Rammohan; Lie, Joni Polili; Suganthan, P. N.; Razul, Sirajudeen Gulam; See, and Chong Meng S.

doi:10.2528/pierb10110810

Cited by 18 publications

(20 citation statements)

References 31 publications

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“…Our study presents a new adaptive beamforming (ABF) technique suitable for antenna arrays [3][4][5][6][9][10][11][12][13][14][15][16][17]. The technique is based on neural networks (NNs) [5,[18][19][20][21][22][23][24][25][26], which use training sets produced by a novel binary variant of Particle Swarm Optimization (PSO) [27][28][29][30][31][32][33], called Mutated Boolean PSO (MBPSO) [10].…”

Section: Introductionmentioning

confidence: 99%

“…Taking into account that the geometry of the array is time-independent, a dynamically shaped pattern is achieved by applying on the array elements appropriate excitation weights that vary with time. These weights are calculated in real time by beamforming techniques [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Therefore, the beamforming algorithm must be completed as fast as possible.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Beamforming With Low Side Lobe Level Using Neural Networks Trained by Mutated Boolean Pso

Zaharis

Gotsis

Sahalos

2012

PIER

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Abstract-A new adaptive beamforming technique based on neural networks (NNs) is proposed. The NN training is accomplished by applying a novel optimization method called Mutated Boolean PSO (MBPSO). In the beginning of the procedure, the MBPSO is repeatedly applied to a set of random cases to estimate the excitation weights of an antenna array that steer the main lobe towards a desired signal, place nulls towards several interference signals and achieve the lowest possible value of side lobe level. The estimated weights are used to train efficiently a NN. Finally, the NN is applied to a new set of random cases and the extracted radiation patterns are compared to respective patterns extracted by the MBPSO and a well-known robust adaptive beamforming technique called Minimum Variance Distortionless Response (MVDR). The aforementioned comparison has been performed considering uniform linear antenna arrays receiving several interference signals and a desired one in the presence of additive Gaussian noise. The comparative results show the advantages of the proposed technique.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Beamforming With Low Side Lobe Level Using Neural Networks Trained by Mutated Boolean Pso

Zaharis

Gotsis

Sahalos

2012

PIER

View full text Add to dashboard Cite

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“…Through the use of proposed technique, smart antenna arrays for DOA estimation will have the benefits of smaller size and better resolution. The proposed technique in this study can also be applied to many other topics in smart antennas or wireless communication systems [20][21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: Resultsmentioning

confidence: 99%

Reducing the Array Size for Doa Estimation by an Antenna Mode Switch Technique

Cheng¹,

Lee²

2012

PIER

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Abstract-In this paper, an antenna-mode-switch technique is proposed to reduce the array size for the DOA (direction-of-arrival) estimation. Conventional DOA estimation requires many elements of antenna array to achieve high resolution, and then suffers from large array size. To improve such disadvantages, this paper proposes an antenna-mode-switch technique to reduce the required number of array elements. Our results show that the required number of array elements will be greatly reduced by using the proposed technique. Furthermore, a bow-tie antenna design for implementing the proposed antennamode-switch technique is also proposed. The results prove that highresolution and accurate properties of such a practical antenna design is very close to those of the ideal antenna mode.

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“…Hence, we often encounter the problem of mismatch between the real direction vector of the desired signal and the steering vector. The effectiveness of a steered-beam beamformer can be destroyed even if a small mismatch arises [2,[17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Robust Antenna Array Beamforming Under Cycle Frequency Mismatch

Lee

2011

PIER B

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Abstract-Many algorithms exploiting the signal cyclostationarity have been shown to be effective in performing antenna array beamforming. However, these algorithms can not provide a unique weight vector for simultaneously extracting multiple signals of interest (SOIs) with distinct cycle frequencies (DCFs). They also suffer from severe performance degradation in the presence of a cycle frequency error (CFE). To simultaneously accommodate multiple SOIs with DCFs and alleviate the effects of cycle leakage due to finite data samples, we propose a cyclic sample matrix inversion (C-SMI) beamformer. To make the C-SMI beamformer robust against CFE, we present a novel objective function which is optimized by using a steepest-descent based algorithm to find the appropriate estimates of the true DCFs. The simulation results show the effectiveness of the robust C-SMI beamformer.

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Near Optimal Robust Adaptive Beamforming Approach Based on Evolutionary Algorithm

Cited by 18 publications

References 31 publications

Adaptive Beamforming With Low Side Lobe Level Using Neural Networks Trained by Mutated Boolean Pso

Adaptive Beamforming With Low Side Lobe Level Using Neural Networks Trained by Mutated Boolean Pso

Reducing the Array Size for Doa Estimation by an Antenna Mode Switch Technique

Robust Antenna Array Beamforming Under Cycle Frequency Mismatch

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