Network-side mobile position location using factor graphs

Chen, Jung-Chieh; Wang, Yeong-Cheng; Maa, Ching-Shyang; Chen, Jiunn-Tsair

doi:10.1109/twc.2006.03401

Cited by 46 publications

(11 citation statements)

References 13 publications

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“…Ref. [9] proposes a TOA-based FG technique which is basically the same as the technique shown in [10]. However, it takes into account that the measurement error of TOA is included in measured data of each sensor, in the process of the conversion of the parameters to the distance.…”

Section: Related Workmentioning

confidence: 99%

A PTDOA-DRSS hybrid factor graph-based unknown radio wave geolocation

Karimah

Aziz

Matsumoto

2018

2018 International Conference on Signals and Systems (ICSigSys)

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We propose a hybrid Pythagorean Time Difference of Arrival and Differential Received Signal Strength based Factor Graph (PTDOA-DRSS-FG) to estimate the location of unknown radio wave emitter in outdoor environments. The term of "unknown" indicates that the knowledge of neither time of departure (TOD) nor absolute transmit power of the radio wave emitter are required. The PTDOA-FG can eliminate the necessity of TOD knowledge of the target signal transmission and DRSS-FG can eliminate the necessity of the knowledge of target absolute transmit power. However, PTDOA-FG alone requires perfect time synchronism between sensors, which is difficult in practice. On the other hand, DRSS-FG alone requires the most suitable monitoring spots. In this paper, PTDOA-FG is used to provide the rough estimation of the target position to select the most suitable monitoring spots for DRSS-FG technique. It is shown that, in terms of root mean square error (RMSE) vs. iteration times, the achieved RMSE of the proposed technique is better than the PTDOA-FG alone and very close to the DRSS reference, i.e., the DRSS-FG technique with the idealistic monitoring spots. Performing the proposed technique in the framework of factor graph (FG) does not require excessive computational complexity due to the fact that using the Gaussian distribution model, it uses mean and variance only with the sum-product algorithm.

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Section: Related Workmentioning

confidence: 99%

A PTDOA-DRSS hybrid factor graph-based unknown radio wave geolocation

Karimah

Aziz

Matsumoto

2018

2018 International Conference on Signals and Systems (ICSigSys)

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“…In [5], a 'balancing' bias variable is introduced for simplification. Furthermore, given a good initial point and the Taylor series expansion technology [6,7], the highly nonlinear joint estimation problem of location and biases can be reduced to a linear least squares issue, which called TS-LQP. Although TS-LQP will result in some accuracy degradation than the sequential quadratic programming algorithm, TS-LQP has much less computation complexity.…”

Section: Related Work and Main Contributionsmentioning

confidence: 99%

“…, where m is a constant, and depended on applications. -Calculate correlation coefficient by (6) for each grid, and sort the coefficient vector.…”

Section: Framework Of the Algorithmmentioning

confidence: 99%

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Grid-based Correlation Localization Method in Mixed Line-of-Sight/Non-Line-of-Sight Environments

2015

KSII TIIS

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Considering the localization estimation issue in mixed line-of-sight (LOS)/non-LOS(NLOS) environments based on received signal strength (RSS) measurements in wireless sensor networks, a grid-based correlation method based on the relationship between distance and RSS is proposed in this paper. The Maximum-Likelihood (ML) estimator is appended to further improve the localization accuracy. Furthermore, in order to reduce computation load and enhance performance, an improved recursively version with NLOS mitigation is also proposed. The most advantages of the proposed localization algorithm is that, it does not need any prior knowledge of the propagation model parameters and therefore does not need any offline calibration effort to calibrate the model parameters in harsh environments, which makes it more convenient for rapid implementation in practical applications. The simulation and experimental results evidence that the proposed localization algorithm exhibits good localization performance and flexibilities for different devices.

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“…Among all positioning algorithms, the FG-based technique is famous for the low computational complexity and high positioning accuracy [18]. Many FG positioning methods based on different measurement information have been developed in recent years.…”

Section: Introductionmentioning

confidence: 99%

Indoor 3-D Localization Based on Received Signal Strength Difference and Factor Graph for Unknown Radio Transmitter

Zhang

Jiang

2019

Sensors

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Accurate localization of the radio transmitter is an important work in radio management. Previous research is more focused on two-dimensional (2-D) scenarios, but the localization of an unknown radio transmitter under three-dimensional (3-D) scenarios has more practical significance. In this paper, we propose a novel 3-D localization algorithm with received signal strength difference (RSSD) information and factor graph (FG), which is suitable for both line-of-sight (LOS) and non-line-of-sight (NLOS) condition. Considering the stochastic properties of measurement errors caused by the indoor environment, RSSD measurements are processed with mean and variance in the form of Gaussian distribution in the FG framework. A new 3-D RSSD-based FG model is constructed with the relationship between RSSD and location coordinates by local linearization technique. The soft-information computation and iterative process of the proposed model are derived by using the sum-product algorithm. In addition, the impacts of different grid distances and number of signal receivers on positioning accuracy are explored. Finally, the performance of our proposed approach is experimentally evaluated in a real scenario. The results show that the positioning performance of the proposed algorithm is not only superior to the k-nearest neighbors (kNN) algorithm and least square (LS) algorithm, but also it can achieve a mean localization error as low as 1.15 m. Our proposed scheme provides a good solution for the accurate detection of an unknown radio transmitter under indoor 3-D space and has a good application prospect.

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Network-side mobile position location using factor graphs

Cited by 46 publications

References 13 publications

A PTDOA-DRSS hybrid factor graph-based unknown radio wave geolocation

A PTDOA-DRSS hybrid factor graph-based unknown radio wave geolocation

Grid-based Correlation Localization Method in Mixed Line-of-Sight/Non-Line-of-Sight Environments

Indoor 3-D Localization Based on Received Signal Strength Difference and Factor Graph for Unknown Radio Transmitter

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