Direction of Arrival Estimation of Humans With a Small Sensor Array Using an Artificial Neural Network

Kim, Youngwook

doi:10.2528/pierb10100510

Cited by 21 publications

(13 citation statements)

References 20 publications

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“…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]. In the MBPSO, the update of particle velocities and positions is performed using exclusively Boolean expressions, while former binary PSO variants update the particle velocities using real number expressions [34].…”

Section: Introductionmentioning

confidence: 99%

“…It starts by selecting a set of random cases where a ULA receives several interference signals and a SOI at respective directions of arrival (DOA) in the presence of additive zero-mean Gaussian noise. The above directions are usually calculated by DOA estimation algorithms [1,19,22,24,[36][37][38][39][40]. The DOA of the SOI and the interference signals represent the input parameters for each case.…”

Section: Introductionmentioning

confidence: 99%

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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

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“…Compared to conventional signal processing algorithms that are mainly based on linear models, neural networks consider DOA estimation as approximation of highly nonlinear multidimensional function, or in other words, as a mapping between spatial covariance matrix of received signals from antenna elements and DOAs. There are many publications on ANNs in DOA estimation of both narrowband and wideband signals [22][23][24][25][26][27][28][29][30][31]. Most of them report results on Radial Basis Function Neural Network (RBF-NN) modeling to estimate DOAs in azimuth plane only, but there are also papers addressing two-dimensional DOA estimation [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Efficient Neural Network Approach for 2d Doa Estimation Based on Antenna Array Measurements

Agatonovic¹,

Stankovic²,

Milovanović³

et al. 2013

PIER

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Abstract-In this paper, we present an efficient Artificial Neural Network (ANN)-based model to estimate both azimuth and elevation arrival angles of a signal source. To achieve this goal, the ANN model is constructed using measurement data obtained by a rectangular antenna array in the space-frequency domain. Unlike classical superresolution algorithms such as 2D MUSIC, the proposed model is capable to account for imperfections of measurement equipment as well as mutual couplings between array elements. The neural model has been verified for several angular positions and frequencies. It is shown that the use of ANN model to estimate angular positions of a signal source yields more accurate results when compared to 2D MUSIC. Moreover, the neural model significantly outperforms 2D MUSIC in terms of speed of computation.

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“…Each case concerns a SOI and several interference signals received by a ULA at random directions of arrival (DOA) different from each other in the presence of additive Gaussian noise. The above directions are usually calculated by DOA algorithms [16,17,[20][21][22][23][24][25][26][27][28][29][30]. The DOA of all the incoming signals and the power level of the noise signals represent the input parameters for each case.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Neural Network Training Applied to Adaptive Beamforming of Antenna Arrays

Zaharis¹,

Gotsis²,

Sahalos³

2012

PIER

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Abstract-This paper presents a comparative study of neural network (NN) training. The trained NNs are used as adaptive beamformers of antenna arrays. The training is performed either by a recently developed method called Mutated Boolean PSO (MBPSO) or by a well known beamforming method called Minimum Variance Distortionless Response (MVDR). The training procedure starts by applying the MBPSO and the MVDR to a set of random cases where a linear antenna array receives a signal of interest (SOI) and several interference signals at random directions of arrival (DOA) different from each other in the presence of additive Gaussian noise. For each case, the MBPSO and the MVDR are independently applied to estimate respective excitation weights that make the array steer the main lobe towards the DOA of the SOI and form nulls towards the DOA of the interference signals. The lowest possible value of side lobe level (SLL) is additionally required. The weights extracted by the MBPSO and the weights extracted by the MVDR are used to train respectively two different NNs. Then, the two trained NNs are independently applied to a new set of cases, where random DOA are chosen for the SOI and the interference signals. Finally, the radiation patterns extracted by the two NNs are compared to each other regarding the steering ability of the main lobe and the nulls as well as the side lobe level. The comparison exhibits the superiority of the NN trained by the MBPSO.

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Direction of Arrival Estimation of Humans With a Small Sensor Array Using an Artificial Neural Network

Cited by 21 publications

References 20 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

Efficient Neural Network Approach for 2d Doa Estimation Based on Antenna Array Measurements

Comparative Study of Neural Network Training Applied to Adaptive Beamforming of Antenna Arrays

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