Artificial neural-network-based visible light positioning algorithm with a diffuse optical channel

Huang, Heqing; Yang, Aiying; Feng, Lihui; Ni, Guoqiang; Guo, Peng

doi:10.3788/col201715.050601

Cited by 25 publications

(5 citation statements)

References 13 publications

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“…The results shown in [12] prove the effectiveness of BP algorithms in reducing positioning errors and the verified positioning error is 6.39 cm. In fact, the main advantage of BP neural network is the outstanding ability of non-linear mapping and classification which is ideal for applications in indoor positioning systems [17].…”

Section: Theory and Methodsmentioning

confidence: 84%

“…The frequency of background light is different from the transmitted signal and can be ignored during demodulation. Supposing that the LEDs have a Lambertian radiation pattern, when only the line-of-sight (LOS) channel is considered, the distance between an LED and the receiver can be estimated by measuring the received signal power, which can be expressed by [12]. Pri=Ar(m+1)2πdi2cosmfalse(ϕifalse)cosfalse(φifalse)Ttransg(φi)Pti,i=1,2,3,4 where Pri(i=1,2,3,4) is the received signal power of the four LEDs, Pt is the transmitted signal power, Ar is the physical area of the detector, d is the distance between the LED and the receiver, ϕ is the angle of irradiance, φ is the angle of incidence, Ttrans is the optical transmittance, g(φ...…”

Section: Theory and Methodsmentioning

confidence: 99%

“…In recent works, machine learning has been used in VLP based on RSS. In [12], the average positioning error was only 6.39 cm in the simulation stage with the Levenberg-Marquardt algorithm and the number of training data was 2630 within a 5 m × 5 m × 3 m area. Earlier this year, Hsu et al realized 3.65 cm accuracy in a 1 m × 0.9 m triangular unit cell [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Zhang

Cui

Feng

et al. 2019

Sensors

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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, we experimentally demonstrate two different training data acquisition methods adopting either even or arbitrary training sets. In addition, we also demonstrate the positioning accuracy of the traditional RSS algorithm. Experimental results show that the average localization accuracy optimized by our proposed algorithm is only 1.88 cm for the arbitrary set and 1.99 cm for the even set, while the average positioning error of the traditional RSS algorithm reaches 14.34 cm. Comparison indicates that the positioning accuracy of our proposed algorithm is 7.6 times higher. Results also show that the performance of our system is higher than some previous reports based on RSS and RSS fingerprint databases using complex machine learning algorithms trained by a large amount of training points.

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Section: Theory and Methodsmentioning

confidence: 84%

Section: Theory and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Zhang

Cui

Feng

et al. 2019

Sensors

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“…Although many proposed methods based on deep neural networks reported robustness against minor changes of system parameters, such as the reflectivity of walls and field-of-view of the receiver [33], there are still some drawbacks of using these methods. A deep neural network functions as a black box; therefore, the inner logic cannot be easily manipulated.…”

Section: Risks Of Applying Machine Learning Methods In Vlpmentioning

confidence: 99%

AdVLP: unsupervised visible light positioning by adversarial deep learning

Hua¹,

Zhuang²,

Qi³

et al. 2021

Meas. Sci. Technol.

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Visible light positioning (VLP) is a promising technique to bring location-based service for numerous Internet of Things applications. Recent advances in VLP have shown that machine learning (ML)-based positioning algorithms show satisfying performance in physical environments under highly noisy and interference-rich conditions. With so many ML methods proposed, one major concern is that trained models could fail due to environmental heterogeneity. In this paper, we propose AdVLP, a novel adversarial training method for VLP based on deep neural networks, to address the issue of the vulnerability of data-driven approaches, which happens when channel parameters change. Our proposed method, which is inspired by generative adversarial networks, manages to adapt the two domains of the source dataset and the target dataset. The channel parameters, e.g. the Lambertian order of light-emitting diode (LED) lights and a photodiode, the LED power as well as the location error of the lights, were discussed. We observed in experiments that the accuracy of a neural network method would decrease as the bias of the parameters became larger. Secondly, a higher dimension of the parameter change would make the neural network method more vulnerable. However, our proposed approach could achieve substantial improvement in positioning accuracy.

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“…Note that, tilting angles are independent for each PD and optimised for vehicular VLP in Section 4.2. The main parameters that was used for the simulation are shown in Table 1 [17,18]. Table 1.…”

Section: System Descriptionmentioning

confidence: 99%

Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning

et al. 2021

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This paper proposes a 2-D vehicular visible light positioning (VLP) system using existing streetlights and diversity receivers. Due to the linear arrangement of streetlights, traditional positioning techniques based on triangulation or similar algorithms fail. Thus, in this work, we propose a spatial and angular diversity receiver with machine learning (ML) techniques for VLP. It is shown that a multi-layer neural network (NN) with the proposed receiver scheme outperforms other ML algorithms and can offer high accuracy with root mean square (RMS) error of 0.22 m and 0.14 m during the day and night time, respectively. Furthermore, the NN shows robustness in VLP across different weather conditions and road scenarios. The results show that only dense fog deteriorates the performance of the system due to reduced visibility across the road.

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Artificial neural-network-based visible light positioning algorithm with a diffuse optical channel

Cited by 25 publications

References 13 publications

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

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

AdVLP: unsupervised visible light positioning by adversarial deep learning

Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning

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