Monte Carlo algorithm for the evaluation of the distance estimation variance in RSS-based visible light positioning

In this work, the Monte Carlo algorithm to determine the variance on the distance estimation in Received Signal Strength-based visible light positioning is considered. The method is build on the maximization of the signal-to-noise-ratio by means of matched filtering, and leads to a number of characteristics that are typically only obtained after intensive analytical elaborations. It is shown that the results match those obtained by calculating the Cramer-Rao lower bound when only the noise is considered as non-deterministic. It is demonstrated that the method is also applicable when multiple physical parameters exhibit a probability distribution, leading to an assessment of the distance estimation accuracy in more realistic settings

Zigbee as a means to reduce the number of blind spot incidents of a truck

Every year in Europe, about 1500 people die in traffic because they are not noticed by the truck driver. This problem could be solved by developing a wireless communication system where the truck driver and the cyclist are informed about each others presence. In this paper a test setup is presented in which the position of the cyclist is determined and displayed on a screen when being in the neighborhood of a truck. The cyclist gets an indication about notification by the truck. Because of the fast changing network, the cyclist must be added quickly to the network and the position must be updated very fast. For this reason a Zigbee communication system is used. The position of the cyclist is displayed in zones around the truck. The setup is experimentally tested and it is demonstrated that the proposed setup leads to a reliable and fast method to reduce the number of blind spot incidents

Assessment of the influence of photodiode size on RSS-based visible light positioning precision

This work discusses the influence of a photodiodes effective area size on the precision of received signal strength-based visible light positioning. It analyzes how two silicon-based photodiodes with different effective area perform as a receiver under varying illumination conditions. The two main findings are that it is not particularly needed to select a photodiode with a large surface area, despite the higher received signal strength, due to a higher noise contribution. On the other hand, the spread on the distance estimation is much smaller than 1 mm under standard illumination levels for the two photodiodes with a significantly different surface area, so that both photodiodes deliver enough precision

Experimental evaluation of the precision of received signal strength based visible light positioning

In this work, the experimental evaluation of the distance estimation variance is executed for received signal strength based visible light positioning. It is shown that based on the signal to noise ratio at the matched filter output, an accurate determination of the precision is achieved. In order to suppress dc ambient light which contains no information regarding the distance between the LED and the receiver, matched filtering with the dc-balanced part of the transmitted signal is required. As a consequence, the theoretical lower bound for the precision can not be achieved

On the impact of LED power uncertainty on the accuracy of 2D and 3D visible light positioning

This paper presents a simulation study of the impact of Light Emitting Diode (LED) output power uncertainty on the accuracy of Received Signal Strength (RSS)-based two-dimensional (2D) and three-dimensional (3D) Visible Light Positioning (VLP). The actual emitted power of a LED is never exactly equal to the value that is tabulated in the datasheet, with possible variations (or tolerances) up to 20%. Since RSS-based VLP builds on converting estimated channel attenuations to distances and locations, this uncertainty will impact VLP accuracy in real-life setups. For 2D, a typical configuration with four LEDs is assumed here, and a Monte-Carlo simulation is executed to investigate the distribution of the resulting positioning errors for four tolerance values at seven locations. It is shown that median errors are the highest just below the LEDs, when using a traditional Least-Squares minimization metric. When tolerance values on the LED power increase from 5% to 20%, median errors vary from at most 2 cm to at most 10 cm. Maximal errors can be as high as 17 cm just below the LED, already for tolerance values of only 5%, and increase up to 40 cm for tolerance values of 20%. An alternative cost metric using normalized Least-Squares minimization makes the errors spatially more homogeneously distributed and reduces them by 35%. For a 3D case, median errors of around 5 cm for a tolerance value of 5% increase to as much as 22 cm for a tolerance value of 20%. As the receiver heights increase, positioning errors decrease significantly

Monte-Carlo simulation of the impact of LED power uncertainty on visible light positioning accuracy

This paper presents a simulation study of the impact of Light Emitting Diode (LED) output power uncertainty on the accuracy of Received Signal Strength (RSS)-based Visible Light Positioning (VLP). The actual emitted power of a LED is never exactly equal to the value that is tabulated in the datasheet, with possible variations (or tolerances) up to 20%. Since RSS-based VLP builds on converting estimated channel attenuations to distances and locations, this uncertainty will impact VLP accuracy in real-life setups. A typical configuration with four LEDs is assumed here, and a Monte-Carlo simulation is executed to investigate the distribution of the resulting positioning errors for four tolerance values at seven locations. It is shown that median errors are the highest just below the LEDs. When tolerance values on the LED power increase from 5% to 20%, median errors vary from at most 2 cm to at most 10 cm. Maximal errors can be as high as 17 cm just below the LED, already for tolerance values of only 5%, and increase up to 40 cm for tolerance values of 20%

New photodiode responsivity model for RSS-based VLP

Visible Light Positioning (VLP) might enable auspicious tracking systems, well-suited for low-cost and route-configurable autonomous guided vehicles. Yielding the high accuracy required, necessitates a detailed modelling of a photodiode (PD) receiver's angular characteristics. Still lacking, current RSS-based VLP systems implicitly cope by measuring and (arbitrarily) fitting the received power - distance relation. Upon PD changeover, a recalibration is needed. In this paper, it is shown that adequately modelling the receiver's angular dependencies (i.e. the responsivity) obsoletes the calibrating fit. Hereto, a new responsivity model is proposed, which is a function of the square of the incidence angle rather than its cosine. An extensive measurement set highlights that this model better matches the measured angular characteristics. In terms of the coefficient of determination R2, the new model outscores the baseline Lambertian and generalised Lambertian responsivity models by 1.64% and 0.17% for a Lambertian-like receiver, and by 133% and 1.24% for a non-Lambertian-resembling receiver

Wireless energy transfer by means of inductive coupling for dairy cow health monitoring

The increase of herd sizes hinders the capability of the dairy farmer to timely detect illnesses. Therefore, automatic health monitoring systems are deployed, but due to their high energy consumption, the application possibilities remain limited. In this work, a wireless, inductive charging solution for dairy cow monitoring is designed. This system is mounted at the eating trough, and the amount of energy transferred each eating turn is determined experimentally. For the first time, inductive wireless power transfer is used to charge on-body sensor networks for cattle. Measurements at a research farm on 40 dairy cows show an average energy transfer of 96 J per meal, for an average eating time of 160 s. It is demonstrated that inductive power transfer is a viable technology to resolve the energy provision challenge for the automatic and real-time health monitoring of dairy cows

Optimal energy storage solution for an inductively powered system for dairy cows

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Experimental evaluation of machine learning methods for robust received signal strength-based visible light positioning

In this work, the use of Machine Learning methods for robust Received Signal Strength (RSS)-based Visible Light Positioning (VLP) is experimentally evaluated. The performance of Multilayer Perceptron (MLP) models and Gaussian processes (GP) is investigated when using relative RSS input features. The experimental set-up for the RSS-based VLP technology uses light-emitting diodes (LEDs) transmitting intensity modulated light and a single photodiode (PD) as a receiver. The experiments focus on achieving robustness to cope with unknown received signal strength modifications over time. Therefore, several datasets were collected, where per dataset either the LEDs transmitting power is modified or the PD aperture is partly obfuscated by dust particles. Two relative RSS schemes are investigated. The first scheme uses the maximum received light intensity to normalize the received RSS vector, while the second approach obtains RSS ratios by combining all possible unique pairs of received intensities. The Machine Learning (ML) methods are compared to a relative multilateration implementation. It is demonstrated that the adopted MLP and GP models exhibit superior performance and higher robustness when compared to the multilateration strategies. Furthermore, when comparing the investigated ML models, the GP model is proven to be more robust than the MLP for the considered scenarios