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Multi-Layer Perceptrons to Reduce Ground Reflection Effect in Real-Time Locating Systems

Dante I. Tapia, Juan F. De Paz, Cristian I. Pinzón, Javier Bajo, Ricardo S. Alonso, Juan M. Corchado


Nowadays, indoor Real-Time Locating Systems represent one of the most exciting applications based on Wireless Sensor Networks using wireless technologies such as Wi-Fi or ZigBee. Indoor Real-Time Locating Systems based on Wireless Sensor Networks use different measurements from radio frequency signals such as Received Signal Strength Indication (RSSI) levels in order to estimate distances between reference nodes and the devices to be located. In ideal conditions, the modeling of the relationship between RSSI levels and distances between antennas has a decaying exponential shape. Nevertheless, radio frequency waves used by indoor Real-Time Locating Systems can be affected by different undesired propagation effects, such as attenuation, diffraction, reflection and scattering, which can lead to multipath effect. In this sense, this paper presents a novel mathematical model based on Multi-Layer Perceptrons for reducing the ground reflection effect, a kind of multipath effect, in indoor Real-Time Locating Systems. Presented results demonstrate that the use of Multi-Layer Perceptrons to forecast distances from RSSI levels allows reducing the ground reflection effect that occurs when considering only the current RSSI measurement.


Wireless Sensor Networks, Real-Time Location Systems, Ground Reflection Effect, Artificial Neural Networks, Multi-Layer Perceptrons.

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