An Indoor Navigation Algorithm Using Multi-Dimensional Euclidean Distance and an Adaptive Particle Filter

Sensors

Hongying Xu¹  Biyu Tang²  Yunbing Hu²  Ao Peng² 

[1] Artificial Intelligence and Big Data College, Chongqing College of Electronic Engineering, Chongqing 401331, China;School of Informatics, Xiamen University, Xiamen 361001, China;

关键词: inertial navigation system;    WiFi fingerprint matching;    adaptive particle filter;    multidimensional Euclidean distance;   

DOI  :  10.3390/s21248228

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown