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In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the APF by decreasing its update frequency and the number of particles used in this research. In the lower filter (Kalman filter), zero velocity update and non-holonomic constraints are used to correct the error of the inertial navigation-derived solutions. The innovation of the design lies in the combination of upper filter (particle filter) map-matching and map-aiding methods to further constrain the navigation solutions. This proposed navigation method simplifies indoor positioning and makes it accessible to individual and group users, while guaranteeing the system's accuracy. The availability and accuracy of the proposed algorithm are tested and validated through experiments in various practical scenarios. [ABSTRACT FROM AUTHOR]

Providing an accurate and practical navigation solution anywhere with portable devices, such as smartphones, is still a challenge, especially in environments where global navigation satellite systems (GNSS) signals are not available or are degraded. This paper proposes a new algorithm that integrates inertial navigation system (INS) and pedestrian dead reckoning (PDR) to combine the advantages of both mechanizations for micro-electro-mechanical systems (MEMS) sensors in pedestrian navigation applications. In this PDR/INS integration algorithm, a pseudo-velocity-vector, which is composed of the PDR-derived forward speed and zero lateral and vertical speeds from non-holonomic constraints (NHC), works as an update for the INS to limit the velocity errors. To further limit the drift of MEMS inertial sensors, trilateration-based WiFi positions with small variances are also selected as updates for the PDR/INS integrated system. The experiments illustrate that positioning error is decreased by 60%-75% by using the proposed PDR/INS integrated MEMS solution when compared with PDR. The positioning error is further decreased by 15%-55% if the proposed PDR/INS/WiFi integrated solution is implemented. The average accuracy of the proposed PDR/INS/WiFi integration algorithm achieves 4.5 m in indoor environments. [ABSTRACT FROM AUTHOR]

In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the APF by decreasing its update frequency and the number of particles used in this research. In the lower filter (Kalman filter), zero velocity update and non-holonomic constraints are used to correct the error of the inertial navigation-derived solutions. The innovation of the design lies in the combination of upper filter (particle filter) map-matching and map-aiding methods to further constrain the navigation solutions. This proposed navigation method simplifies indoor positioning and makes it accessible to individual and group users, while guaranteeing the system's accuracy. The availability and accuracy of the proposed algorithm are tested and validated through experiments in various practical scenarios., Competing Interests: The authors declare no conflict of interest.