Location Accuracy Estimates for Signal Fingerprinting

Abstract

Location fingerprinting is a technique for determining the location of a device by measuring ambient signals such as radio signal strength, temperature, or any signal that varies with location. The accuracy of the technique is compromised by signal noise, quantization, and limited calibration resources. We develop generic, probabilistic models of location fingerprinting to find accuracy estimates. In one case, we look at predeployment modeling to predict accuracy before any signals have been measured using a new concept of noisy reverse geocoding. In another case, we model a previously deployed system to predict its accuracy. The models allow us to explore the accuracy implications of signal noise, calibration effort, and quantization of signals and space.