A Pseudorange Measurement Scheme Based on Snapshot for Base Station Positioning Receivers

Mo, Jun; Deng, Zhongliang; Jia, Buyun; Bian, Xinmei

doi:10.3390/s17122783

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…The Taylor-iteration method uses an iterative method to sole the set of hyperbolic equations and estimate of the position of an MS. 8 The procedure is started with an initial guessed coordinate values ( x 0 , y 0 ) and determines the local linear least-square solution as the deviations at each of iteration. In every iteration, x 0 and y 0 are set to be x = x 0 + Δ x and y = y 0 + Δ y , the whole process is repeated until the deviation Δ x and Δ y are sufficiently small.…”

Section: Localization Model Using Tdoamentioning

confidence: 99%

“…Jiang et al 7 deals with source localization with TDOA measurements in two-dimensional (2D) scenarios. The Taylor series method 8 and Chan’s method 9 are the basic TDOA localization methods. The Taylor series method linearizes the set of equations by Taylor series expansion, then uses an iterative method to solve the system of linear equations.…”

Section: Introductionmentioning

confidence: 99%

“…The Taylor series method linearizes the set of equations by Taylor series expansion, then uses an iterative method to solve the system of linear equations. 8 Chan’s method is a non-iterative solution to the hyperbolic position-estimation problem, which is capable of achieving optimum performance for arbitrarily placed sensors; when TDOA estimation errors are small, this method is an approximation to the maximum likelihood (ML) estimator. 9 Xiong et al 10 presents a tag localization algorithm based on the TDOA of mobile tag signal for asynchronous wireless sensor network with N anchors and a large number of mobile tags.…”

Section: Introductionmentioning

confidence: 99%

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A novel time difference of arrival localization algorithm using a neural network ensemble model

Zhang

Jiang

et al. 2018

International Journal of Distributed Sensor Networks

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In a localization system, time difference of arrival technique is widely used to estimate the location of a mobile station. To improve the performance of mobile station location estimation, a novel algorithm-based artificial neural network ensemble and time difference of arrival information is proposed in non-line-of-sight environments. Back propagation neural network is a classic artificial neural network and may be effectively used for mathematical modeling and prediction, and an artificial neural network ensemble has better generalization ability and stability than a single network. First, the parameters, such as the weights and biases of the single neural network are optimized by the ant lion optimization method which is novel and effective. Then four types of different information from the time difference of arrival measurements are respectively used to train the individual neural network. Finally, the weighted average method is improved to combine the outputs of the different individual neural network, where weights are determined by the training errors. The estimation accuracy of the locating system is evaluated through experimental measurements. The simulation results show that the proposed algorithm is efficient in improving the generalization ability and localization precision of the neural network ensemble model.

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Section: Localization Model Using Tdoamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel time difference of arrival localization algorithm using a neural network ensemble model

Zhang

Jiang

et al. 2018

International Journal of Distributed Sensor Networks

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show abstract

“…They found that the positions had a mean precision of ±22 m with an SD of 49 m. Ground-based TDoA location systems can experience substantial noise due to multipathing where the time differences are exaggerated due to signal paths that are not line-of-sight, this is the kind of error we seek to address in this study. The examined localization method uses TDoA among four fixed receivers to estimate the origin of the transmission, the two most basic analytical methods are the Taylor series method [17] and Chan method [3] that can localize objects with minimal error in the absence of multipathing. To address noise in TDoA location estimates machine learning-based methods are introduced for localization in an arbitrarily complex system, current approaches to this problem falls into two main methodologies.…”

Section: Introductionmentioning

confidence: 99%

A Noise Robust Automatic Radiolocation Animal Tracking System

Wang

Diakogiannis

Mills

et al. 2021

Preprint

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Agriculture is becoming increasingly reliant upon accurate data from sensor arrays, with localization an emerging application in the livestock industry. Ground-based Time Difference of Arrival (TDoA) radiolocation methods have the advantage of being lightweight and exhibit higher energy effciency than methods reliant upon Global Navigation Satellite System (GNSS). Such methods can employ small primary cell batteries, rather than rechargeable cells, and still deliver a multi-year deployment. In this paper, we present a novel deep learning algorithm adapted from a one dimensional U-Net like a convolutional neural network (CNN) model, originally developed for the task of semantic segmentation. This model both converts TDoA sequences directly to positions and reduces positional errors introduced by sources such as multipathing. We have evaluated the model by using simulated animal movements in the form of TDoA position sequences in combination with known distributions of TDoA error. When errors with a standard deviation of 50 m and 100 m are added to simulated TDoA transmissions the model is able to reduce this error to 22 m and 27 m (RMSE) respectively. Without correction, the standard deviation of these errors is on the order of 90 and 200 m. Accordingly, the model can reduce the error by greater than 80 m (> 80%), demonstrating the effectiveness of this novel 1D CNN U-Net like encoder/decoder for error correction of TDoA position estimates.

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“…They found that the positions had a mean precision of ±22 m with an SD of 49 m. Ground-based TDoA location systems can experience substantial noise due to multipathing where the time differences are exaggerated due to signal paths that are not line-of-sight, this is the kind of error we seek to address in this study. The examined localization method uses TDoA among four fixed receivers to estimate the origin of the transmission; the two most basic analytical methods are the Taylor series method [18] and Chan method [3] that can localize objects with minimal error in the absence of multipathing. To address noise in TDoA location estimates, machine learning-based methods are introduced for localization in an arbitrarily complex system.…”

Section: Introductionmentioning

confidence: 99%

A noise robust automatic radiolocation animal tracking system

et al. 2021

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Agriculture is becoming increasingly reliant upon accurate data from sensor arrays, with localization an emerging application in the livestock industry. Ground-based time difference of arrival (TDoA) radio location methods have the advantage of being lightweight and exhibit higher energy efficiency than methods reliant upon Global Navigation Satellite Systems (GNSS). Such methods can employ small primary battery cells, rather than rechargeable cells, and still deliver a multi-year deployment. In this paper, we present a novel deep learning algorithm adapted from a one-dimensional implementing a convolutional neural network (CNN) model, originally developed for the task of semantic segmentation. The presented model () both converts TDoA sequences directly to positions and reduces positional errors introduced by sources such as multipathing. We have evaluated the model using simulated animal movements in the form of TDoA position sequences in combination with real-world distributions of TDoA error. These animal tracks were simulated at various step intervals to mimic potential TDoA transmission intervals. We compare to a Kalman filter to evaluate the performance of our algorithm to a more traditional noise reduction approach. On average, for simulated tracks having added noise with a standard deviation of 50 m, the described approach was able to reduce localization error by between 66.3% and 73.6%. The Kalman filter only achieved a reduction of between 8.0% and 22.5%. For a scenario with larger added noise having a standard deviation of 100 m, the described approach was able to reduce average localization error by between 76.2% and 81.9%. The Kalman filter only achieved a reduction of between 31.0% and 39.1%. Results indicate that this novel 1D CNN like encoder/decoder for TDoA location error correction outperforms the Kalman filter. It is able to reduce average localization errors to between 16 and 34 m across all simulated experimental treatments while the uncorrected average TDoA error ranged from 55 to 188 m.

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A Pseudorange Measurement Scheme Based on Snapshot for Base Station Positioning Receivers

Cited by 8 publications

References 17 publications

A novel time difference of arrival localization algorithm using a neural network ensemble model

A novel time difference of arrival localization algorithm using a neural network ensemble model

A Noise Robust Automatic Radiolocation Animal Tracking System

A noise robust automatic radiolocation animal tracking system

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