Haiyu Lan

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.

WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices

This paper presents a WiFi-aided magnetic matching (MM) algorithm for indoor pedestrian navigation with consumer portable devices. This algorithm reduces both the mismatching rate (i.e., the rate of matching to an incorrect point that is more than 20 m away from the true value) and computational load of MM by using WiFi positioning solutions to limit the MM search space. Walking tests with Samsung Galaxy S3 and S4 smartphones in two different indoor environments (i.e., Environment #1 with abundant WiFi APs and significant magnetic features, and Environment #2 with less WiFi and magnetic information) were conducted to evaluate the proposed algorithm. It was found that WiFi fingerprinting accuracy is related to the signal distributions. MM provided results with small fluctuations but had a significant mismatch rate; when aided by WiFi, MM’s robustness was significantly improved. The outcome of this research indicates that WiFi and MM have complementary characteristics as the former is a point-by-point matching approach and the latter is based on profile-matching. Furthermore, performance improvement through integrating WiFi and MM depends on the environment (e.g., the signal distributions of magnetic intensity and WiFi RSS): In Environment #1 tests, WiFi-aided MM and WiFi provided similar results; in Environment #2 tests, the former was approximately 41.6% better. Our results supported that the WiFi-aided MM algorithm provided more reliable solutions than both WiFi and MM in the areas that have poor WiFi signal distribution or indistinctive magnetic-gradient features.

A Robust Self-Alignment Method for Ship's Strapdown INS Under Mooring Conditions

Strapdown inertial navigation systems (INS) need an alignment process to determine the initial attitude matrix between the body frame and the navigation frame. The conventional alignment process is to compute the initial attitude matrix using the gravity and Earth rotational rate measurements. However, under mooring conditions, the inertial measurement unit (IMU) employed in a ships strapdown INS often suffers from both the intrinsic sensor noise components and the external disturbance components caused by the motions of the sea waves and wind waves, so a rapid and precise alignment of a ships strapdown INS without any auxiliary information is hard to achieve. A robust solution is given in this paper to solve this problem. The inertial frame based alignment method is utilized to adapt the mooring condition, most of the periodical low-frequency external disturbance components could be removed by the mathematical integration and averaging characteristic of this method. A novel prefilter named hidden Markov model based Kalman filter (HMM-KF) is proposed to remove the relatively high-frequency error components. Different from the digital filters, the HMM-KF barely cause time-delay problem. The turntable, mooring and sea experiments favorably validate the rapidness and accuracy of the proposed self-alignment method and the good de-noising performance of HMM-KF.

A Hybrid WiFi/Magnetic Matching/PDR Approach for Indoor Navigation With Smartphone Sensors

This paper presents a hybrid pedestrian navigation algorithm based on investigation of different combinations of pedestrian dead-reckoning (PDR), WiFi fingerprinting, and magnetic matching (MM). A multilevel quality-control mechanism is developed based on the interaction between different techniques. The algorithms were evaluated by walking in two indoor environments, with two smartphones, and under four motion conditions (i.e., handheld, at an ear, dangling with hand, and in a pants pocket). It was found that 2D accuracy of WiFi fingerprinting and MM is related with received signal strength and magnetic distribution, respectively. MM results had small errors on some occasions but suffered from significant mismatches. WiFi-aided MM provided better results than either WiFi or MM, but still had a risk of mismatching. Furthermore, integration of PDR, WiFi, and MM reduced dependency on both navigation environment and motion condition. The proposed algorithm provided more reliable solutions than both PDR/WiFi and PDR/MM, especially in areas with poor WiFi signal distribution or indistinctive magnetic features.

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.

Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter

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.

Wireless Access Point Localization Using Nonlinear Least Squares and Multi-Level Quality Control

Locations of WiFi access points (APs) are important for WiFi positioning when a propagation model is used. The pre-surveyed propagation parameters, such as the path-loss exponent, are usually not available when localizing the APs in a new environment. This letter introduces a novel method that estimates the AP locations and the parameters of the received signal strength (RSS) propagation model simultaneously using the weighted nonlinear least squares (NLLS) method. This method can run on consumer portable devices autonomously in real time without any a-priori information, and eliminate the need of pre-survey. Another contribution of this letter is to introduce a multi-level quality control mechanism, and utilize the statistical testing method in AP localization and propagation parameters (PPs) determination for the first time. Indoor experiments show that the proposed method provided more promising results than previous methods.

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.

Real-time indoor navigation using smartphone sensors

This paper presents an indoor navigation algorithm that uses multiple kinds of sensors and technologies, such as MEMS sensors (i.e., gyros, accelerometers, magnetometers, and a barometer), WiFi, and magnetic matching. The corresponding real-time software on smartphones includes modules such dead-reckoning, WiFi positioning, and magnetic matching. DR is used for providing continuous position solutions and for the blunder detection of both WiFi fingerprinting and magnetic matching. Finally, WiFi and magnetic matching results are passed into the position-tracking module as updates. Meanwhile, a barometer is used to detect floor changes, so as to switch floors and the WiFi and magnetic databases. This algorithm was tested during the 5th EvAAL indoor navigation competition. Position errors on three quarters (75 %) of test points (totally 62 test points were selected to evaluate the algorithm) were under 6.6 m.

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.