Hybrid Kernel Based Machine Learning Using Received Signal Strength Measurements for Indoor Localization

In this paper, a new indoor localization algorithm with received signal strength indicators (RSSI) fingerprints by a kernel-based learning technique is proposed. In the offline phase, after the training set pre-processing by the iterative self-organizing data analysis techniques algorithm (ISODATA), the measurement-label set is utilized for classification learning by the c-support vector classification approach. Moreover, each measurement-position training subset is chosen for regressing learning with hybrid kernel and cross-validation techniques. In the online phase, based on the classification result of the received RSSI measurement, the corresponding regression function is chosen for target position estimation. Compared with existing learning techniques, it can increase the training ability of a localization system dramatically. The field tests show that, without adding any extra computation complexity, the proposed algorithm can obtain more accurate position estimation than other existing learning-based localization approaches achieve.