Lagrange Programming Neural Network Approaches for Robust Time-of-Arrival Localization

Wang, Hao; Feng, Ruibin; Leung, Andrew Chi-Sing; Tsang, Kim Fung

doi:10.1007/s12559-017-9495-z

Cited by 15 publications

(13 citation statements)

References 36 publications

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“…However, the Lagrangian function in (10) is not stable enough to miss the sufficient condition of strict local convexity, which is also confirmed in some preliminary works [ 22 , 23 ]. Following the analysis in [ 16 ], an augmented term is included to improve the convexity and stability of the Lagrangian function as:

where

is a positive constant.…”

Section: Proposed Methodsmentioning

confidence: 61%

“…This methodology was then extended to the TDOA model [ 23 ]. These preliminary works exhibit the excellent performance of LPNN and reference herein [ 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 86%

“…Among the existing neural circuits, the LPNN is capable of solving many types of optimization problems with constraints [ 16 ]. Recently, the LPNN method was applied in waveform design [ 20 ], MIMO radar [ 2 ] and source localization [ 21 , 22 , 23 ]. For example, the authors of [ 21 , 22 ] built two robust LPNN using TOA measurements in ideal localization scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Lagrange Programming Neural Network for TOA-Based Localization with Clock Asynchronization and Sensor Location Uncertainties

Jia

Yin

Wang

et al. 2018

Sensors

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Source localization based on time of arrival (TOA) measurements in the presence of clock asynchronization and sensor position uncertainties is investigated in this paper. Different from the traditional numerical algorithms, a neural circuit named Lagrange programming neural network (LPNN) is employed to tackle the nonlinear and nonconvex constrained optimization problem of source localization. With the augmented term, two types of neural networks are developed from the original maximum likelihood functions based on the general framework provided by LPNN. The convergence and local stability of the proposed neural networks are analyzed in this paper. In addition, the Cramér-Rao lower bound is also derived as a benchmark in the presence of clock asynchronization and sensor position uncertainties. Simulation results verify the superior performance of the proposed LPNN over the traditional numerical algorithms and its robustness to resist the impact of a high level of measurement noise, clock asynchronization, as well as sensor position uncertainties.

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where

is a positive constant.…”

Section: Proposed Methodsmentioning

confidence: 61%

“…This methodology was then extended to the TDOA model [ 23 ]. These preliminary works exhibit the excellent performance of LPNN and reference herein [ 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lagrange Programming Neural Network for TOA-Based Localization with Clock Asynchronization and Sensor Location Uncertainties

Jia

Yin

Wang

et al. 2018

Sensors

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“…In order to deal with the miscellaneous noise sources and harsh factory conditions, an artificial neural network (ANN) approach is developed in [18]. Lagrange programming neural network or its improved forms based on the TOA measurements are proposed in [19]- [21] to locate a mobile source, it is shown that the localization accuracy of this method approaches to the Cramer-Rao lower bound. By using AOA derived from phase differences in the signal received at the multiple antenna array, a structured deep neural network [22] is proposed to infer the position of MT.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Localization With TOA Measurements Based on Error Learning and Matching

Zhang

et al. 2019

IEEE Access

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Due to the widespread application of location information, the neural network localization method with the advantage of high localization accuracy has received significant interests in recent years. In this paper, we present two new neural network localization methods with time-of-arrival (TOA) measurements. In order to deal with three types of error about TOA measurements such as measurement error, non-line of sight (NLOS) error, and synchronization error, the proposed methods contain an offline training stage and an online localization stage. In the offline stage, the artificial neural network (ANN) or radial basis function (RBF) neural network is utilized to train the range measurements with the output of range errors rather than the position of the mobile terminal (MT). Moreover, due to the unknown signal propagation condition whether it is the line of sight or NLOS propagation, the k-mean clustering algorithm is used to classifying the range errors into different clusters. In the online stage, the range errors are predicted and updated, and then, the linear least square algorithm with the adjusted range measurements is applied for the position estimate of MT. Comparing with the ANN or RBF neural network localization methods, the simulation results show that the proposed methods can effectively reduce the localization error, especially when the training sample is not adequate. In addition, they are insensitive to measurement error, synchronization error, and the distribution of NLOS error. Finally, the memory requirement and computational complexity about different algorithms are analyzed and compared. INDEX TERMSArtificial neural network (ANN), localization, linear least square (LLS), non-line of sight (NLOS), radial basis function (RBF), time of arrival (TOA).However, the absence of direct line of sight (LOS) propagation with satellite will lead to severe deterioration of the localization performance [1]. There are some important supplements to the satellite-based technique, such as Wireless Sensor Network (WSN), Wireless Cellular Network (WCN) and Wireless Local Area Network (WLAN) [2], [3] techniques are used to localize the MT, especially in indoor environments and some outdoor environments where the satellite signals are heavily attenuated or blocked. These supplemental techniques have common principle to localize the MT. By measuring some signal parameters between MT and Fixed

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“…Recently, the LPNN framework has been applied in many fields, such as source localization [19], [21]- [23], multiple-input and multiple-output (MIMO) radar [24] and waveform design [25]. In terms of source localization, Leung et al [21] and Wang et al [22] propose to localize a single source in an ideal positioning scenario with only measurement noise, and they develop two LPNN models based on TOA measurements respectively. Then the LPNN framework is directly extended to build up a TDOA model for single source case [19].…”

Section: Introductionmentioning

confidence: 99%

Joint Multiple Sources Localization Using TOA Measurements Based on Lagrange Programming Neural Network

Jia¹,

Wang²,

Yin³

et al. 2019

IEEE Access

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Multiple sources' localization using the time-of-arrival (TOA) measurements in the presence of sensor position uncertainty is studied in this paper. The non-cooperative scenario where the clock between the source and sensors is not synchronized is also considered. First, we theoretically prove that the Cramér-Rao lower bound of multi-source joint localization is lower than that of the single source case when the TOA measurements from different sources possess the same sensor position errors. Moreover, different from the conventional numerical algorithms, we propose to employ a neural circuit named Lagrange programming neural network (LPNN) to fulfill this non-trivial task of jointly locating multiple sources. The maximum likelihood problem of multi-source localization is reformulated by utilizing the LPNN framework, and then we build up a neural model, which is proved to be asymptotically stable through both mathematical analysis and numerical experiments. The simulation results show that the proposed method is superior to other positioning algorithms, and it has excellent localization performance and robustness even in the case of large measurement noise, synchronization error, and sensor position displacements. INDEX TERMS Multi-source localization, time-of-arrival (TOA), clock asynchronization, sensor position uncertainties, Lagrange programming neural network (LPNN).

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Lagrange Programming Neural Network Approaches for Robust Time-of-Arrival Localization

Cited by 15 publications

References 36 publications

Lagrange Programming Neural Network for TOA-Based Localization with Clock Asynchronization and Sensor Location Uncertainties

Lagrange Programming Neural Network for TOA-Based Localization with Clock Asynchronization and Sensor Location Uncertainties

Neural Network Localization With TOA Measurements Based on Error Learning and Matching

Joint Multiple Sources Localization Using TOA Measurements Based on Lagrange Programming Neural Network

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