Abstract
ABSTRACTThis 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.
Highlights
To localize a position in an indoor environment, wireless-based methods and dead reckoning methods are widely used (Liu et al 2007; Pei et al 2016; Kjærgaard 2007)
The results suggest that the proposed method is effective for indoor positioning
A compound location fingerprint gi;k;j;m is composed of an Received Signal Strength Indicator (RSSI) vector ri;k 2 Ri ð1 k KÞ, an RSSI vector rj;m 2 Rjð1 KÞ, and a displacement di;j 1⁄4 ci À cj between coordinate ci and coordinate cj
Summary
To localize a position in an indoor environment, wireless-based methods and dead reckoning methods are widely used (Liu et al 2007; Pei et al 2016; Kjærgaard 2007). Fingerprinting methods are used for indoor position estimation (Kjærgaard 2007) They are based on a radio fingerprint, or a feature vector, which is composed of the strength of the signals from multiple transmitters. Fingerprinting methods can utilize the local increase and decrease in signal strength as features, and their estimation errors are regarded as relatively small These methods have been used mainly for area estimation or for finding a reference point. Combinations of fingerprinting and dead reckoning are often used to locate a reference point by fingerprinting and to measure the displacement by dead reckoning (Chang et al 2015; Seitz et al 2010) They are useful but do not solve the accumulation of estimation errors caused by dead reckoning. We believe that dead reckoning and fingerprinting can complement each other in this combination and improve the estimation accuracy
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