A genetic based indoor positioning algorithm using Wi-Fi received signal strength and motion data

Abstract

<span lang="EN-GB">The recent trend in location-based services has led to a proliferation of studies in indoor positioning technology. Wi-Fi</span><span lang="VI"> received signal strength </span><span lang="EN-US">indicator</span><span lang="VI"> (RSSI) </span><span lang="EN-GB">Fingerprinting and pedestrian dead reckoning (PDR) are the two best representatives from both approaches. This</span><span lang="VI"> research </span><span lang="EN-GB">proposed a genetic algorithm to combine Wi-Fi Fingerprinting and PDR. By taking advantage of PDR</span><span lang="VI"> and genetic algorithm</span><span lang="EN-GB">, we only need to collect a limited</span><span lang="VI"> number of</span><span lang="EN-GB"> points for the fingerprint dataset with known coordinates, then target trajectories' position can be estimated with high accuracy. Results from our experiments and simulations have shown that even in the scenario of noisy inertial</span><span lang="VI"> measurement unit (</span><span lang="EN-GB">IMU</span><span lang="VI">)</span><span lang="EN-GB"> sensors data, using RSSI measurements and the coordinate of 8 points, our proposed method was able to achieve 1.589 meters of average distance error which is 34.4 percent lower than the conventional Fingerprinting method.</span>