Abstract
The inertial navigation system has high short-term positioning accuracy but features cumulative error. Although no cumulative error occurs in WiFi fingerprint localization, mismatching is common. A popular technique thus involves integrating an inertial navigation system with WiFi fingerprint matching. The particle filter uses dead reckoning as the state transfer equation and the difference between inertial navigation and WiFi fingerprint matching as the observation equation. Floor map information is introduced to detect whether particles cross the wall; if so, the weight is set to zero. For particles that do not cross the wall, considering the distance between current and historical particles, an adaptive particle filter is proposed. The adaptive factor increases the weight of highly trusted particles and reduces the weight of less trusted particles. This paper also proposes a multidimensional Euclidean distance algorithm to reduce WiFi fingerprint mismatching. Experimental results indicate that the proposed algorithm achieves high positioning accuracy.
Highlights
In open outdoor areas, the global satellite navigation system offers reliable positioning accuracy
A multi-dimensional Euclidean distance algorithm using historical fingerprints can reduce the impact of the “jump point" fingerprints, reducing fingerprint mismatching; To solve the problem of particle degradation, the historical position information is introduced, and the adaptive factor is used to improve the weight of high trust particles and reduce the weight of low trust particles; Two smartphones are used to conduct navigation experiments in two navigation areas
Compared with Dead Reckoning (DR), WiFi, and Auxiliary value particle filter (AVPF) [32], the average error of MED+APF declined by 65.23%, 27.05%, and 13.17%, respectively; the root mean square error fell by 62.01%, 29.97%, and 26.37%, respectively
Summary
The global satellite navigation system offers reliable positioning accuracy. The inertial navigation system (INS) is an autonomous navigation system that does not depend on external information and does not radiate energy to the outside This system can provide continuous and real-time data on the carrier’s position, attitude, and speed. Reference [6,7] introduced map information and used a map-aided algorithm to eliminate the direction drift of inertial navigation. WiFi signals fluctuate with time, and a complex indoor environment (e.g., with walking pedestrians or changing obstacles) can quickly lead to deviation in the propagation model’s parameter estimation along with poor positioning accuracy. Magnetic fingerprint matching was introduced in Reference [9], to further reduce position error due to WiFi fingerprint mismatching. Given the two signal sources’ complementary characteristics, an adaptive particle filter is proposed in this paper to integrate INS and WiFi
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