Sensor Selection for TDOA-Based Localization in Wireless Sensor Networks With Non-Line-of-Sight Condition

Zhao, Yue; Li, Zan; Hao, Benjian; Shi, Jia

doi:10.1109/tvt.2019.2936110

Cited by 70 publications

(39 citation statements)

References 55 publications

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“…where ν = 10 ln 10 . Then, the terminal location can be estimated by using the estimated distanced = d 0 exp P0−Pi γν , the azimuth angle measurements, φ i , the elevation angle measurements, ψ i , and the unbiasing constants, δ i andδ i , as in (4). Here, γ is the estimated pass-loss exponent.…”

Section: A Weighted Linear Least Squares (Wlls)mentioning

confidence: 99%

Wireless Sensor Network Localization Using AoA Measurements With Two-Step Error Variance-Weighted Least Squares

Watanabe

2021

IEEE Access

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We propose a novel localization method using angle-of-arrival (AoA) measurements with two-step error variance-weighted least squares (TELS). The first step is to estimate the terminal location provisionally using least squares. The second step is to estimate the terminal location using weighted least squares, with the weights for each anchor and each evaluation-function term, calculated from the error variance based on the first step. The proposed method does not require previous information on the environment while achieving high performance. The simulation results indicate that a root mean square error (RMSE) of the proposed method is superior to that of the existing hybrid received signal strength (RSS)/AoA localization methods. When 11 anchors are deployed inside a cube with edge length 15 m, and the standard deviations of measurements are small, the RMSE of the proposed method reaches about 0.34 m. It is nearly equal to that of Cramer-Rao lower bound (CRLB) on AoA. INDEX TERMS Angle-of-arrival (AoA), received signal strength (RSS), localization, error variance, least squares (LS), wireless sensor networks (WSN) I. INTRODUCTION

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Section: A Weighted Linear Least Squares (Wlls)mentioning

confidence: 99%

Wireless Sensor Network Localization Using AoA Measurements With Two-Step Error Variance-Weighted Least Squares

Watanabe

2021

IEEE Access

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“…Among them, the component method uses one of the three components of R, G, and B in the color image as the gravy value; the maximum value 3 Wireless Communications and Mobile Computing method uses the maximum value as the gravy value; the average value method uses the average of the three components of R, G, and B in the color image as the gravy value. Different gray scale processing methods will produce different gray scale feature images [21]. In this paper, we select the gray scale processing method of the average method and its gray scale value calculation formula is…”

Section: Machine Learning Decision Theory-based Communication Signal Modulation Pattern Recognitionmentioning

confidence: 99%

Automatic Recognition of Communication Signal Modulation Based on the Multiple‐Parallel Complex Convolutional Neural Network

Huang

et al. 2021

Wireless Communications and Mobile Computing

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This paper implements a deep learning-based modulation pattern recognition algorithm for communication signals using a convolutional neural network architecture as a modulation recognizer. In this paper, a multiple-parallel complex convolutional neural network architecture is proposed to meet the demand of complex baseband processing of all-digital communication signals. The architecture learns the structured features of the real and imaginary parts of the baseband signal through parallel branches and fuses them at the output according to certain rules to obtain the final output, which realizes the fitting process to the complex numerical mapping. By comparing and analyzing several commonly used time-frequency analysis methods, a time-frequency analysis method that can well highlight the differences between different signal modulation patterns is selected to convert the time-frequency map into a digital image that can be processed by a deep network. In order to fully extract the spatial and temporal characteristics of the signal, the CLP algorithm of the CNN network and LSTM network in parallel is proposed. The CNN network and LSTM network are used to extract the spatial features and temporal features of the signal, respectively, and the fusion of the two features as well as the classification is performed. Finally, the optimal model and parameters are obtained through the design of the modulation recognizer based on the convolutional neural network and the performance analysis of the convolutional neural network model. The simulation experimental results show that the improved convolutional neural network can produce certain performance gains in radio signal modulation style recognition. This promotes the application of machine learning algorithms in the field of radio signal modulation pattern recognition.

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“…Their locations should be calculated by using exchanged signalling messages between network nodes and some pre‐localized sensors [3, 4]. To that end, different types of signal can be used which may be divided into the four major categories: (i) Time‐of‐arrival (TOA)‐based localization [5, 6]; (ii) Time‐difference‐of‐arrival (TDOA) localization [7, 8]; (iii) Angle‐of‐arrival (AOA) localization [9, 10]; and (iv) Received‐signal strength (RSS) [11, 12].…”

Section: Introductionmentioning

confidence: 99%

One‐to‐one non‐linear transformation for RSS‐based localization with unknown transmit power

Danaee

2021

IET Communications

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For most of the positioning algorithms, based on the received signal strength, the objective function of the estimator is linearized so that the conventional least square algorithm can be executed. This would necessitate using a data model containing many noisy elements which implicitly violates the least squares assumptions. To avoid this violation, an optimized data modelling would have to be set up as shown in this paper. The authors then trivially provide the solution of this new least square problem which is still a primary coarse position estimation because of ignoring the non‐linear relationships existing among parameters of the estimation. To extract quantitative information from these non‐linear relationships, the authors also propose two new signal space points for using Taylor‐series expansion as effectively as possible in accordance of the new data model. The proposed algorithms work without any knowledge about the transmit power. The performance evaluation of the proposed idea indicates that the new suggested estimators perform significantly better than the other existing linear position estimators for a large range of noise variance and path‐loss exponent values.

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Sensor Selection for TDOA-Based Localization in Wireless Sensor Networks With Non-Line-of-Sight Condition

Cited by 70 publications

References 55 publications

Wireless Sensor Network Localization Using AoA Measurements With Two-Step Error Variance-Weighted Least Squares

Wireless Sensor Network Localization Using AoA Measurements With Two-Step Error Variance-Weighted Least Squares

Automatic Recognition of Communication Signal Modulation Based on the Multiple‐Parallel Complex Convolutional Neural Network

One‐to‐one non‐linear transformation for RSS‐based localization with unknown transmit power

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