2019
DOI: 10.3390/s19102324
|View full text |Cite
|
Sign up to set email alerts
|

High-Precision Indoor Visible Light Positioning Using Modified Momentum Back Propagation Neural Network with Sparse Training Point

Abstract: In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that require a large number of training data points to locate accurately, we have realized high-precision positioning for 100 test points with only 20 training points in a 1.8 m × 1.8 m × 2.1 m localization area. In order to verify the adaptability of the MMBP algorithm, … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
16
0

Year Published

2020
2020
2022
2022

Publication Types

Select...
6
1

Relationship

0
7

Authors

Journals

citations
Cited by 21 publications
(16 citation statements)
references
References 21 publications
0
16
0
Order By: Relevance
“…The classical VLP schemes have limitations, as often they require close to ideal behavior of the propagation model and its parameters to perform well. Recently, however, it was demonstrated that Machine Learning (ML) techniques like for example Artificial Neural Networks (ANNs) are capable of delivering accurate localization results in the context of VLP [21][22][23][24][25], as they can handle noise naturally and are not bounded by ideal physical behavior. Furthermore, linear interpolation techniques can be used to reduce the amount of required measured data [26].…”
Section: Introductionmentioning
confidence: 99%
“…The classical VLP schemes have limitations, as often they require close to ideal behavior of the propagation model and its parameters to perform well. Recently, however, it was demonstrated that Machine Learning (ML) techniques like for example Artificial Neural Networks (ANNs) are capable of delivering accurate localization results in the context of VLP [21][22][23][24][25], as they can handle noise naturally and are not bounded by ideal physical behavior. Furthermore, linear interpolation techniques can be used to reduce the amount of required measured data [26].…”
Section: Introductionmentioning
confidence: 99%
“…Similar solutions have been proposed based on various classification methods over the received signal strengths of lights [73][74][75][76][77][78] recently. Both [73] and [74] also used the KNN classifier for their localization algorithms.…”
Section: Light Intensity As Fingerprintsmentioning
confidence: 99%
“…In [75], multiple classifiers were leveraged, and two fusion localization algorithms were proposed (i.e., grid-independent and grid-dependent least square) to combine the outputs of multiple trained classifiers, whereas [77] used two popular functions (classification and regression) of ML-based algorithms (such as KNN, decision trees (DT), support vector machines (SVM), and random forest (RF)). On the other hand, [76] and [78] leveraged neural networks in their VLL systems. In [76], the three axes of the coordinate of the receiver location based on three neural networks were inferred, while [78] used an artificial neural network with one hidden layer trained by the modified momentum back propagation method.…”
Section: Light Intensity As Fingerprintsmentioning
confidence: 99%
See 2 more Smart Citations