Aligning training modelswith smartphone properties in WiFi fingerprinting based indoor localization

We are concerned with the so-called fingerprinting method for WiFi-based indoor positioning, where the measured received signal strength index (RSSI) is compared with training data to come up with an estimate of the user's location. We introduce a method for adapting the trained models to the statistics of the RSSI values of the target (testing) WiFi device, which is derived from the Maximum Likelihood Linear Regression (MLLR) framework. By introducing regression classes the assumption of a linear relationship between the RSSI readings of the testing device and the training data is relaxed, leading to superior adaptation performance. Parameter adaptation formulas are derived for the general case of censored and dropped data. While censoring occurs due to the limited sensitivity of WiFi chips, dropping is probably caused by limitations of the operating system of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms.