Direction finding in phased arrays with a neural network beamformer

Southall, H.L.; Simmers, John; O'Donnell, T.H.

doi:10.1109/8.475924

Cited by 125 publications

(47 citation statements)

References 5 publications

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“…wherew 1 , w 2 , and w n are the complex weights and in proportional to the complex current phasorsI 1 , I 2 , and I n, respectively. To achieve the objective of forming a resultant single beam, the value of the complex weights w 1 , w 2 , and w n needs to be optimized such that the resultant field must matched to a desired single beam function    f .…”

Section: Adaptive Array Modelmentioning

confidence: 99%

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Single Perceptron Model for Smart Beam forming in Array Antennas

Senthilkumar

Pirapaharan

Hoole³

et al. 2016

IJECE

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In this paper, a single neuron neural network beamformer is proposed. A perceptron model is designed to optimize the complex weights of a dipole array antenna to steer the beam to desired directions. The objective is to reduce the complexity by using a single neuron neural network and utilize it for adaptive beamforming in array antennas. The selection of nonlinear activation function plays the pivotal role in optimization depends on whether the weights are real or complex. We have appropriately proposed two types of activation functions for respective real and complex weight values. The optimized radiation patterns obtained from the single neuron neural network are compared with the respective optimized radiation patterns from the traditional Least Mean Square (LMS) method. Matlab is used to optimize the weights in neural network and LMS method as well as display the radiation patterns. Keyword:Adaptive INTRODUCTIONDue to its broad range of applications, adaptive array antenna is most popular in the present world. Present world applications require much faster beam steering that cannot be achieved using a mechanical systems. Hence it is required to use more consistent and much faster electronic beam steering techniques such as adaptive arrays. However, the beamforming withal most identical elements result lack of flexibility. On the other hand, adaptive beamforming methods by means of weight optimization are capable of managing the complexities of distinct elements. The adaptive array can detect, track and allocate narrow beams in the direction of the desired users while nulling unwanted sources of interferences. There are well known traditional techniques for adaptive beamforming in array antenna.Soft computing techniques namely Artificial Neural Networks (ANN), fuzzy logics, Genetic Algorithms (GAs), provide low cost solutions and robustness to different complex real world problems. ANN is a powerful information processing paradigm that tries to simulate the structure and functionalities of the biological nervous systems. The ANN is used to deal with many applications, and they have proved their effectiveness in several research areas such as image recognition, speech recognition, signal analysis, process control, and robotics. The true power of neural networks lies in their ability to represent both linear and nonlinear relationships. ANN, like human learning, learns by example. Training a neural network is, in most cases, an exercise in numerical optimization of a usually nonlinear function. Basic building block of every artificial neural network is an artificial neuron or perceptron that is a simple mathematical model.

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Section: Adaptive Array Modelmentioning

confidence: 99%

“…Neural networks are used in adaptive antenna signal processing [1]- [2] because of their general purpose nature, fast convergent rate and large scale integration implementations. The goal of neural network training is to minimize the difference between output data and the target data.…”

mentioning

confidence: 99%

Single Perceptron Model for Smart Beam forming in Array Antennas

Senthilkumar

Pirapaharan

Hoole³

et al. 2016

IJECE

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“…Neural Network approaches have also been used in beamforming [33][34][35][36][37][38][39]. Zaman et al utilized a GA hybridized with a pattern search for DOA analysis [40].…”

Section: Beamformingmentioning

confidence: 99%

Radar Angle of Arrival System Design Optimization Using a Genetic Algorithm

2017

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An approach for using a Genetic Algorithm (GA) to select radar design parameters related to beamforming and angle of arrival estimation is presented in this article. This was accomplished by first developing a simulator that could evaluate the localization performance with a given set of design parameters. The simulator output was utilized as part of the GA objective function that searched the solution space for an optimal set of design parameters. Using this approach, the authors were able to more than halve the mean squared error in degrees of the localization algorithm versus a radar design using human-selected design parameters. The results of this study indicate that this kind of approach can be used to aid in the development of an actual radar design.

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“…Constant is a scalar bias that needs to be added since the nonlinear estimator may be biased. Parameter vector always lies in the subspace spanned by the training data and, according to the representer theorem [37], it can be constructed as a linear combination of the given data (8) where are the training data pairs. Then, estimator (7) can be rewritten as (9) In this context, 's are called primal parameters, and 's are the dual ones.…”

Section: Estimators In Reproducing Kernel Hilbert Subspacesmentioning

confidence: 99%

“…This feature decreases estimation errors or bit error rates. Neural networks [4] have been proposed for beamforming (e.g., [5]- [7]) and direction of arrival estimation (e.g., [8], [9]) among other array processing tasks. A com-A.…”

mentioning

confidence: 99%

Approximate Kernel Orthogonalization for Antenna Array Processing

Navia-Vázquez

Martínez‐Ramón

García-Muñoz

et al. 2010

IEEE Trans. Antennas Propagat.

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Abstract-We present a method for kernel antenna array processing using Gaussian kernels as basis functions. The method firs identifie the data clusters by using a modifie sparse greedy matrix approximation. Then, the algorithm performs model reduction in order to try to reduce the fina size of the beamformer. The method is tested with simulations that include two arrays made of two and seven printed half wavelength thick dipoles, in scenarios with 4 and 5 users coming from different angles of arrival. The antenna parameters are simulated for all DOAs, and include the dipole radiation pattern and the mutual coupling effects of the array. The method is compared with other state-of-the-art nonlinear processing methods, to show that the presented algorithm has near optimal capabilities together with a low computational burden.

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Direction finding in phased arrays with a neural network beamformer

Cited by 125 publications

References 5 publications

Single Perceptron Model for Smart Beam forming in Array Antennas

Single Perceptron Model for Smart Beam forming in Array Antennas

Radar Angle of Arrival System Design Optimization Using a Genetic Algorithm

Approximate Kernel Orthogonalization for Antenna Array Processing

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