Indoor localization of wireless emitter using direct position determination and particle swarm optimization

Self Cite

Accurate indoor localization technologies are currently in high demand in wireless sensor networks, which strongly drive the development of various wireless applications including healthcare monitoring, patient tracking and endoscopic capsule localization. The precise position determination requires exact estimation of the time varying characteristics of wireless channels. In this paper, we address this issue and propose a three-phased scheme, which employs an optimal single stage TDOA/FDOA/AOA indoor localization based on spatial sparsity. The first contribution is to formulate the received unknown signals from the emitter as a compressive sensing problem. Then, we solve an ℓ1 minimization problem to localize the emitter's position. To combat the nonstationary behavior of wireless channels between sensor nodes, the results of our proposed localization algorithm are finally fused using a novel fusion method based on the adaptive normal hedge algorithm. To improve the accuracy of the estimated location, an optimal set of weighed coefficients are derived through introducing a new loss function. Monte Carlo simulation results show that the accuracy of the proposed localization framework is superior compared to the existing indoor localization schemes in low SNR regimes.

Section: Problem Formulationmentioning

confidence: 99%

Improving performance of indoor localization using compressive sensing and normal hedge algorithm

Hassanhosseini¹,

Taban²,

Abouei³

et al. 2020

Self Cite

“…Many methods to compute the matrix inverse such as Gaussian elimination, LU decomposition, or least squares (LS) estimation may be used to find the solution to (10). The solution to the estimation based on matrix inversion can be expressed as follows:…”

Section: Angulation Algorithm Position Estimationmentioning

confidence: 99%

“…While the former uses an angulation algorithm to determine the PE, the later woks with a lateration algorithm to compute the PE. Both methods are implemented as a two-stage process that involves first determining the PDSP and the use of the PDSP to perform PE [10,11]. MLAT can perform PE in 2D with a minimum of three GRS sensors and with more sensors it can perform 3D PE [12].…”

Section: Introductionmentioning

confidence: 99%

Analysis of condition number and position estimation error for multiangulation position estimation system

Yarima¹,

Sha’ameri²,

Yaro³

2020

A passive wireless positioning system could be used to detect the location of low-level airborne targets such as drones or unmanned aircraft systems from the electromagnetic emission detected at spatially deployed ground receiving stations (GRSs). The multiangulation system proposed in this paper makes use of the angle of arrival (AOA) of the transmitted signal from the target to estimate its position through a 2-stage process. The AOA is the positiondependent signal parameter (PDSP) obtained from the target emission in the first stage, and using the PDSP and GRSs, the target location is estimated in the second stage by the angulation algorithm. Noise in the received signal results in AOA estimation error and subsequently error in the position estimation (PE). This paper focuses on the angulation process, which is the second stage of the multiangulation target location estimation process. Analysis is conducted to determine the correlation between the PE error and the condition number of the coefficient matrix of a multiangulation position estimation system. The results based on Monte Carlo simulations show that both condition number and PE error distribution increase with the target range, where the higher condition number values correlate with higher PE error values appearing around 80 • to 110 • and 260 • to 280 • of both distributions.

“…Because the BP algorithm depends on the gradient descend method, it has some disadvantages, such as the lower convergent speed and being easily trapped into local minimum. Thus, many evolutionary algorithms have been proposed to conquer the abovementioned disadvantages, such as genetic algorithm (GA) [17], differential evolution (DE) [18], and particle swarm optimization (PSO) [19]. Recently, PSO has been commonly used to perform parameter learning in various models.…”

Section: Introductionmentioning

confidence: 99%

An efficient recurrent fuzzy CMAC model based on a dynamic-group–based hybrid evolutionary algorithm for identification and prediction applications

Lee¹,

Lin²

2018

This article presents an efficient TSK-type recurrent fuzzy cerebellar model articulation controller (T-RFCMAC) model based on a dynamic-group-based hybrid evolutionary algorithm (DGHEA) for solving identification and prediction problems. The proposed T-RFCMAC model is based on the traditional CMAC model and the Takagi-Sugeno-Kang (TSK) parametric fuzzy inference system. Otherwise, the recurrent network, which imports feedback links with a receptive field cell, is embedded in the T-RFCMAC model, and the feedback units are used as memory elements. The DGHEA, which is a hybrid of the dynamic-group quantum particle swarm optimization (QPSO) and the Nelder-Mead method, is proposed for adjusting the parameters of the T-RFCMAC model. In DGHEA, an entropybased grouping technique is adopted to improve the searching capability and the convergent speed of quantum particles swarm optimization. Experimental results show that the proposed DGHEA-based T-RFCMAC model is more effective at identification and prediction than other models. IntroductionFor nonlinear system processing, neural networks or neural fuzzy networks [1][2][3] are the most commonly used model. If a feedforward network is applied in the dynamic system, we first need to obtain the delay numbers of outputs and inputs [3]. In this situation, the accurate order of the dynamic system is usually not clear. To solve this problem, recurrent networks [4,5] are adopted for processing the dynamic system. In the present study, we present a novel recurrent network for solving different problems.The cerebellar model articulation controller (CMAC) model was proposed by Albus [6,7]. It is a simple network architecture with a high convergence rate, high learning speed, good generalization capability, ease of hardware implementation, etc. The CMAC network has been employed in various areas successfully, such as robot control [8], pattern recognition [9], and signal processing [10]. However, Albus's CMAC network still has three major limitations. First, it is difficult to select the memory structure parameters. While the common CMAC network has a constant value allocated to each hypercube, the derivative information is not maintained and the data for a quantized state are constant. In order to solve this problem, inconstant differentiable basis functions are used, such as fuzzy membership functions by Jou [11] and spline functions by Lane et al.