A Direct Position-Determination Approach for Multiple Sources Based on Neural Network Computation

Abstract

The most widely used localization technology is the two-step method that localizes transmitters by measuring one or more specified positioning parameters. Direct position determination (DPD) is a promising technique that directly localizes transmitters from sensor outputs and can offer superior localization performance. However, existing DPD algorithms such as maximum likelihood (ML)-based and multiple signal classification (MUSIC)-based estimations are computationally expensive, making it difficult to satisfy real-time demands. To solve this problem, we propose the use of a modular neural network for multiple-source DPD. In this method, the area of interest is divided into multiple sub-areas. Multilayer perceptron (MLP) neural networks are employed to detect the presence of a source in a sub-area and filter sources in other sub-areas, and radial basis function (RBF) neural networks are utilized for position estimation. Simulation results show that a number of appropriately trained neural networks can be successfully used for DPD. The performance of the proposed MLP-MLP-RBF method is comparable to the performance of the conventional MUSIC-based DPD algorithm for various signal-to-noise ratios and signal power ratios. Furthermore, the MLP-MLP-RBF network is less computationally intensive than the classical DPD algorithm and is therefore an attractive choice for real-time applications.

Rejecting Chaotic Disturbances Using a Super-Exponential-Zeroing Neurodynamic Approach for Synchronization of Chaotic Sensor Systems

“…For superiorities of parallelism and easy implementation by hardware, the RNN model can be implemented by utilizing embedded systems such as field-programmable gate arrays (FPGAs) [26,27,60]. Many state-of-the-art studies [27,61,62] have been reported for the effective implementation of the neural network model such as the MLP. In [27], the MLP was effectively implemented by utilizing the FPGA Ciclone IV GX FPGA DE2i-150 from Altera.…”

Direction of arrival estimation of mobile stochastic electromagnetic sources with variable radiation powers using hierarchical neural model

“…1,[10][11][12] Artificial neural networks (ANNs) [13][14][15][16] represent an another direction of research for solving the DoA problems in real-time. [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] Beside ANNs, the more general methods in the field of artificial intelligence, such as genetic programming (GP) and support-vector machines (SVM), are used in today research for the same purpose, as demonstrated for the DoA estimation problem solving in Reference 35 and Reference 36, respectively. Although the GP and SVM-based DoA models have more generalization capabilities than the ANN models in modeling highlynonlinear dependences present in DoA problems, DoA ANN models still represent a good alternative.…”

“…Therefore, some researches have been focused to adapt some of the previously mentioned super‐resolution algorithms, based on time‐consuming inverse matrix calculations and Eigen‐value decompositions, to the strict real‐time demands . Artificial neural networks (ANNs) represent an another direction of research for solving the DoA problems in real‐time . Beside ANNs, the more general methods in the field of artificial intelligence, such as genetic programming (GP) and support‐vector machines (SVM), are used in today research for the same purpose, as demonstrated for the DoA estimation problem solving in Reference and Reference , respectively.…”

Precision Motion Control of a Linear Permanent Magnet Synchronous Machine Based on Linear Optical-Ruler Sensor and Hall Sensor

“…In general, the ENN can be regarded as an especial kind of feed-forward NN with added memory neurons. The ENN has certain dynamic fits over the static neural network (NN) [ 17 , 18 , 19 ], owing to the context nodes in the ENN. In addition, the ENN has been applied abroad for identification and control of systems [ 20 , 21 , 22 , 23 ].…”