Localization of Stochastic Electromagnetic Sources by using Correlation Matrix Trained MLP Neural Network
نویسندگان
چکیده
In this paper, MLP neural network-based approach is proposed for an efficient direction of arrival (DOA) estimation of multiple narrow-band electromagnetic sources of stochastic nature in far-field. Neural network is trained to perform the mapping from the space of signals described by correlation matrix, obtained by signal sampling in far-field scan area, to the space of DOA in angular positions. Accuracy and efficiency of the proposed approach is validated on two examples determining the location of one and three stochastic sources in far-field, respectively, placed at fixed angle distance.
منابع مشابه
An Artificial Neural Network Model for Efficient Estimation of the Number of Mobile Stochastic Em Sources in the Space Sector
Information on the total number of radiation sources that are currently observed in the physical sector may be of use in procedures dealing with efficient DoA (Direction of Arrival) estimation of stochastic radiation source. This paper introduces an artificial neural model based on MLP (Multi-Layer Perceptron) network, that is based on a value of the spatial correlation matrix signal sampled in...
متن کاملCalibration of an Inertial Accelerometer using Trained Neural Network by Levenberg-Marquardt Algorithm for Vehicle Navigation
The designing of advanced driver assistance systems and autonomous vehicles needs measurement of dynamical variations of vehicle, such as acceleration, velocity and yaw rate. Designed adaptive controllers to control lateral and longitudinal vehicle dynamics are based on the measured variables. Inertial MEMS-based sensors have some benefits including low price and low consumption that make them ...
متن کاملPredicting the Risk of Complications in Coronary Artery Bypass Operations using Neural Networks
Dr. David Shahian Lahey Clinic Burlington, MA 01805 Experiments demonstrated that sigmoid multilayer perceptron (MLP) networks provide slightly better risk prediction than conventional logistic regression when used to predict the risk of death, stroke, and renal failure on 1257 patients who underwent coronary artery bypass operations at the Lahey Clinic. MLP networks with no hidden layer and ne...
متن کاملA generalized ABFT technique using a fault tolerant neural network
In this paper we first show that standard BP algorithm cannot yeild to a uniform information distribution over the neural network architecture. A measure of sensitivity is defined to evaluate fault tolerance of neural network and then we show that the sensitivity of a link is closely related to the amount of information passes through it. Based on this assumption, we prove that the distribu...
متن کاملFast accurate MEG source localization using a multilayer perceptron trained with real brain noise.
Iterative gradient methods such as Levenberg-Marquardt (LM) are in widespread use for source localization from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Unfortunately, LM depends sensitively on the initial guess, necessitating repeated runs. This, combined with LM's high per-step cost, makes its computational burden quite high. To reduce this burden, we trained a ...
متن کامل