摘要：This paper proposed and evaluated an estimation method for indoor positioning. The
method combines location fingerprinting and dead reckoning differently from the conventional
combinations. It uses compound location fingerprints, which are composed of radio
fingerprints at multiple points of time, that is, at multiple positions, and displacements
between them estimated by dead reckoning. To avoid errors accumulated from dead reckoning,
the method uses short-range dead reckoning. The method was evaluated using 16
Bluetooth beacons installed in a student room with the dimensions of 11 × 5 m with furniture
inside. The Received Signal Strength Indicator (RSSI) values of the beacons were collected at
30 measuring points, which were points at the intersections on a 1 × 1 m grid with no
obstacles. A compound location fingerprint is composed of RSSI vectors at two points and
a displacement vector between them. Random Forests (RF) was used to build regression
models to estimate positions from location fingerprints. The root mean square error of
position estimation was 0.87 m using 16 Bluetooth beacons. This error is lower than that
received with a single-point baseline model, where a feature vector is composed of only RSSI
values at one location. The results suggest that the proposed method is effective for indoor
positioning.
关键词：Indoor positioning ; integrated estimation ; radio fingerprinting ; dead reckoning ; machine learning ; non-linear regression
