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 t...
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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 multiplesignalclassification (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.
MUSIC algorithm is a classic subspace method for spatial spectrum estimation. In this paper, a pretreatment model is presented to construct the reference signal. The configuration of the multi-stage Wiener filter that...
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MUSIC algorithm is a classic subspace method for spatial spectrum estimation. In this paper, a pretreatment model is presented to construct the reference signal. The configuration of the multi-stage Wiener filter that is fit for MUSIC algorithm is proposed. Due to avoiding eigenvalue decomposition of the sampling data autocorrelation, the new method has less computational complexity. Simulation results demonstrate the effectiveness of the new method.
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