Antenna array signal processing with neural networks for direction of arrival estimation

Zooghby, A.H. El; Christodoulou, C.G.; Georgiopoulos, Michael

doi:10.1109/aps.1997.625423

Cited by 10 publications

(5 citation statements)

References 3 publications

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“…In [4], [5], [11], [12] and [13], RBFNN method is compared to MUSIC and its superior performance in terms of its feasibility to carry out real time processing is demonstrated.…”

Section: Simulation Resultsmentioning

confidence: 99%

A hybrid approach involving artificial neural network and ant colony optimization for direction of arrival estimation

Pour¹,

Atlasbaf²,

Mirzaee³

et al. 2008

2008 Canadian Conference on Electrical and Computer Engineering

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This paper discusses the application of a multi-layer perceptron network to estimate direction of arrival (DOA) using ant colony optimization (ACO) for training. ACO simulates the foraging behavior of ant colonies which manage to find the shortest path from nest to feeding source. This technique was originally developed for discrete optimization problems, but recent research efforts has led to some algorithm modifications to make it applicable to continuous optimization problems. In this work we utilize continuous ACO to train a neural network for direction of arrival estimation which encounters an interpolation of a complex nonlinear function. The performance of proposed hybrid approach is compared to radial basis function network that is a well known solution to DOA problem and some improvements in approximation are discussed.Index Terms-Direction of arrival, ant colony optimization, radial basis function neural network

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“…In [4], [5], [11], [12] and [13], RBFNN method is compared to MUSIC and its superior performance in terms of its feasibility to carry out real time processing is demonstrated.…”

Section: Simulation Resultsmentioning

confidence: 99%

A hybrid approach involving artificial neural network and ant colony optimization for direction of arrival estimation

Pour¹,

Atlasbaf²,

Mirzaee³

et al. 2008

2008 Canadian Conference on Electrical and Computer Engineering

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“…In the literature, RBFNN method is utilized for DoA problem and its performance in terms of its feasibility to handle real time computation is investigated Zooghby et al, 1997i;Kuwahara & Matsumoto, 2005). In this section, result of applying ACO R -based neural network to DoA problem is presented and estimation errors are compared with RBFNN method.…”

Section: Simulation Resultsmentioning

confidence: 99%

Application of Continuous ACOR to Neural Network Training: Direction of Arrival Problem

Movahedipour¹

2011

Ant Colony Optimization - Methods and Applications

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“…El Zooghby et al published detailed descriptions and results for adaptive beamforming algorithm to approximate Wiener beamforming weight evaluation algorithm using RBF neural networks for cellular [10,11,12,13] and satellite communications systems [14]. These are based on approximating the subspace multiple signal classification (MUSIC) DoA estimation algorithm and Wiener filter weight evaluation with respect to DoA estimates with the spatial correlation matrix estimated from input signals.…”

Section: Neuroadaptive Beamforming Using Rbfnnsmentioning

confidence: 99%

“…The detailed mathematical derivation of the training algorithm and (3) is shown in [10,11,12,13,15]. The neural network is expected to estimateŴ opt from the trained neural network by presenting unseen spatial correlation vectors to the network input during the performance stage.…”

Section: Neuroadaptive Beamforming Using Rbfnnsmentioning

confidence: 99%

Prototyping Neuroadaptive Smart Antenna for 3G Wireless Communications

Salčić

Nguang

2005

EURASIP J. Adv. Signal Process.

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This paper describes prototyping of a neuroadaptive smart antenna beamforming algorithm using hardware-software implemented RBF neural network and FPGA system-on-programmable-chip (SoPC) approach. The aim is to implement the adaptive beamforming unit in a combination of hardware and software by estimating its performance against the fixed real-time constraint based on IMT-2000 family of 3G cellular communication standards

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Antenna array signal processing with neural networks for direction of arrival estimation

Cited by 10 publications

References 3 publications

A hybrid approach involving artificial neural network and ant colony optimization for direction of arrival estimation

A hybrid approach involving artificial neural network and ant colony optimization for direction of arrival estimation

Application of Continuous ACOR to Neural Network Training: Direction of Arrival Problem

Prototyping Neuroadaptive Smart Antenna for 3G Wireless Communications

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