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RSSI distance estimation based on Genetic Programming

The obtention of distances to different Access Points from RSSI readings in indoor environments is a difficult task due to intrinsic RF propagation effects like refraction, diffraction, reflection or absorption. This paper proposes a new model of distances estimation from RSSI data based on Genetic Programming; this new model estimates the distances from the receiver position to each WiFi AP depending on all RSSI WiFi measurements available in this point. Other methods, as fingerprinting, use the RSSI WiFi measures to determine directly the position but they need a careful choice of the set of calibration points. In our method, we obtain specific expressions that obtain distances to each AP taking into account the RSSI received from all the APs available in the coverage area with few restrictions about the location of such calibration points. Our model is compared with two classical propagation models (Hata-Okumura and COST 231 multi-wall) in a real scenario obtaining better results. The distances to the APs obtained can be used by any positioning algorithm (as Gauss-Newton one) to obtain the position of the receiver.