You Li

PDR/INS/WiFi Integration Based on Handheld Devices for Indoor Pedestrian Navigation

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.

Autonomous Calibration of MEMS Gyros in Consumer Portable Devices

This paper presents a real-time calibration method for gyro sensors in consumer portable devices. The calibration happens automatically without the need for external equipment or user intervention. Multilevel constraints, including the pseudoobservations, the accelerometer and magnetometer measurements, and the quasi-static attitude updates, are used to make the method reliable and accurate under natural user motions. Walking tests with the Samsung Galaxy S3 and S4 smartphones showed that the method provided promising calibration results even under challenging motion modes, such as dangling and pocket, and in challenging indoor environments with frequent magnetic interferences.

A Novel Kalman Filter with State Constraint Approach for the Integration of Multiple Pedestrian Navigation Systems

Numerous solutions/methods to solve the existing problems of pedestrian navigation/localization have been proposed in the last decade by both industrial and academic researchers. However, to date there are still major challenges for a single pedestrian navigation system (PNS) to operate continuously, robustly, and seamlessly in all indoor and outdoor environments. In this paper, a novel method for pedestrian navigation approach to fuse the information from two separate PNSs is proposed. When both systems are used at the same time by a specific user, a nonlinear inequality constraint between the two systems’ navigation estimates always exists. Through exploring this constraint information, a novel filtering technique named Kalman filter with state constraint is used to diminish the positioning errors of both systems. The proposed method was tested by fusing the navigation information from two different PNSs, one is the foot-mounted inertial navigation system (INS) mechanization-based system, the other PNS is a navigation device that is mounted on the user’s upper body, and adopting the pedestrian dead reckoning (PDR) mechanization for navigation update. Monte Carlo simulations and real field experiments show that the proposed method for the integration of multiple PNSs could improve each PNS’ navigation performance.

A Two-Filter Integration of MEMS Sensors and WiFi Fingerprinting for Indoor Positioning

Indoor positioning has become increasingly important in the past decade. Some approaches for the integration of micro-electro-mechanical systems (MEMS) sensors and WiFi fingerprinting (FP) have been proposed for indoor positioning. However, most of the existing integration approaches only focus on aiding MEMS sensors by WiFi FP. This letter proposes a two-filter integration for MEMS sensors and WiFi FP. In the proposed approach, the integrated positioning solution is used to constrain the search space of WiFi FP, and achieve a constrained constrained FP (CFP) solution. Then, a Kalman filter serves for obtaining a smoothed CFP solution (SCFP). Finally, an extended Kalman filter serves for the integration of SCFP and MEMS sensors. Field tests show the proposed integration approach can improve both positioning accuracy and computational efficiency.

Self-Contained Indoor Pedestrian Navigation Using Smartphone Sensors and Magnetic Features

This paper presents an algorithm for navigating in challenging indoor environments that do not have WiFi. Dead-reckoning (DR) based on off-the-shelf smartphone sensors and magnetic matching (MM) based on indoor magnetic features are integrated. For DR, we utilize a two-filter algorithm structure and multi-level constraints to navigate under different human motion conditions. For MM, we use several approaches to enhance its performance. These approaches include multi-dimensional dynamic time warping, weighted k -nearest neighbor, and utilization of magnetic gradient fingerprints. Furthermore, realized that the key to enhance the DR/MM performance is to mitigate the impact of MM mismatches, we introduced and evaluated two mismatch-detection approaches, including a threshold-based method that sets the measurement noises of MM positions based on their distances to the historical DR/MM position solutions, and an adaptive Kalman filter-based method that introduces the estimation of the innovation sequence covariance into the calculation of the gain matrix instead of adjusting the measurement noises. The proposed mismatch-detection mechanism reduced the DR/MM errors by 45.9%-67.9% in indoor tests with two smartphones, in two buildings, and under four motion conditions.

A Pervasive Integration Platform of Low-Cost MEMS Sensors and Wireless Signals for Indoor Localization

Location service is fundamental to many Internet of Things applications such as smart home, wearables, smart city, and connected health. With existing infrastructures, wireless positioning is widely used to provide the location service. However, wireless positioning has the limitations such as highly depending on the distribution of access points (APs); providing a low sample-rate and noisy solution; requiring extensive labor costs to build databases; and having unstable RSS values in indoor environments. To reduce these limitations, this paper proposes an innovative integrated platform for indoor localization by integrating low-cost microelectromechanical systems (MEMS) sensors and wireless signals. This proposed platform consists of wireless AP localization engine and sensor fusion engine, which is suitable for both dense and sparse deployments of wireless APs. The proposed platform can automatically generate wireless databases for positioning, and provide a positioning solution even in the area with only one observed wireless AP, where the traditional trilateration method cannot work. This integration platform can integrate different kinds of wireless APs together for indoor localization (e.g., WiFi, Bluetooth low energy, and radio frequency identification). The platform fuses all of these wireless distances with low-cost MEMS sensors to provide a robust localization solution. A multilevel quality control mechanism is utilized to remove noisy RSS measurements from wireless APs and to further improve the localization accuracy. Preliminary experiments show the proposed integration platform can achieve the average accuracy of 3.30 m with the sparse deployment of wireless APs (1 AP per 800 m2).

An efficient method for evaluating the performance of integrated multiple pedestrian navigation systems

This paper introduces a new sensor fusion approach for using multiple MEMS sensor-based pedestrian navigation systems (PNSs) to enhance the performance of each individual navigation system. First, we propose a novel single IMU-based PNS which integrates both the inertial navigation system (INS) mechanization and the pedestrian dead reckoning (PDR) mechanization. When two identical PNSs are used by a user at the same time, the output of each PNS is then shared within a Kalman filter (KF) with the state-constrained approach, which, in turn, feeds the state error correction information back to each PNS. Several real experiments are done to assess the proposed methodology for the integration of multiple PNSs. The experimental studies clearly indicate that through applying the proposed state-constrained approach, using motion sensor data from multiple mobile/wearable devices could provide more accurate navigation information for a pedestrian in all indoor and outdoor environments.

An automatic multi-level gyro calibration architecture for consumer portable devices

A novel calibration architecture that calculates the gyro biases without any external equipment and without any user intervention is proposed. This architecture uses a Kalman filter algorithm and utilizes multi-level constraints, such as pseudo-observation updates and the accelerometer and magnetometer measurements. Walking tests with smartphones show that the proposed architecture is effective and accurate when being used under various scenarios with different phone contexts.