Adaptive Smoothing: A General Tool for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In Wi-Fi based location fingerprinting, the cost of constructing a radio map is high due to the calibration of measured RSS data at a large number of RPs and the update of database. Interpolating a coarse radio map into a dense one may reduce the cost. An interpolated radio map, however, has low accuracy, especially at space discontinuity such as a wall. We present a method of constructing a high-density radio map which preserves the discontinuity of RSS by localized smoothing in accordance with the layout of a building. Experimental results show that the radio map by discontinuity preserving smoothing has higher accuracy than conventional interpolating methods. With sampling density ≥ 35%, the performance is close to a genuine full density radio map. With sampling density 60%, the performance is even better than the original full density map.