Dongyan Wei

A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model

Indoor positioning has recently become an important field of interest because global navigation satellite systems (GNSS) are usually unavailable in indoor environments. Pedestrian dead reckoning (PDR) is a promising localization technique for indoor environments since it can be implemented on widely used smartphones equipped with low cost inertial sensors. However, the PDR localization severely suffers from the accumulation of positioning errors, and other external calibration sources should be used. In this paper, a context-recognition-aided PDR localization model is proposed to calibrate PDR. The context is detected by employing particular human actions or characteristic objects and it is matched to the context pre-stored offline in the database to get the pedestrian’s location. The Hidden Markov Model (HMM) and Recursive Viterbi Algorithm are used to do the matching, which reduces the time complexity and saves the storage. In addition, the authors design the turn detection algorithm and take the context of corner as an example to illustrate and verify the proposed model. The experimental results show that the proposed localization method can fix the pedestrian’s starting point quickly and improves the positioning accuracy of PDR by 40.56% at most with perfect stability and robustness at the same time.

Geomagnetism-Aided Indoor Wi-Fi Radio-Map Construction via Smartphone Crowdsourcing

Wi-Fi radio-map construction is an important phase in indoor fingerprint localization systems. Traditional methods for Wi-Fi radio-map construction have the problems of being time-consuming and labor-intensive. In this paper, an indoor Wi-Fi radio-map construction method is proposed which utilizes crowdsourcing data contributed by smartphone users. We draw indoor pathway map and construct Wi-Fi radio-map without requiring manual site survey, exact floor layout and extra infrastructure support. The key novelty is that it recognizes road segments from crowdsourcing traces by a cluster based on magnetism sequence similarity and constructs an indoor pathway map with Wi-Fi signal strengths annotated on. Through experiments in real world indoor areas, the method is proved to have good performance on magnetism similarity calculation, road segment clustering and pathway map construction. The Wi-Fi radio maps constructed by crowdsourcing data are validated to provide competitive indoor localization accuracy.

A foot-mounted PDR system based on IMU/EKF+HMM+ZUPT+ZARU+HDR+compass algorithm

A foot-mounted pedestrian dead reckoning system is a self-contained technique for indoor localization. An inertial pedestrian navigation system includes wearable MEMS inertial sensors, such as an accelerometer, gyroscope, barometer, or magnetometer, which enable the measurement of the step length and the heading direction. In this plan, a method based on IMU/EKF+HMM+ZUPT+ZARU+HDR+the Earth Magnetic Yaw was designed to realize foot-mounted pedestrian navigation. Based on the characteristics of pedestrian navigation, the general likelihood ratio test (GLRT) and the Hidden Markov Model (HMM) were used to realize the detection of zero speed interval at different speed states. When the zero speed state is detected, the zero velocity update (ZUPT) method is used to limit the accumulation of IMU. The Zero Angular Rate Update (ZARU) + (heuristic heading reduction) HDR+the Earth Magnetic Yaw method is used to limit the IMU attitude and heading drift. Finally, the EKF method is used to realize the effective estimation and feedback of the speed, attitude and heading error of the pedestrian navigation system. Meanwhile, a fault detection algorithm based on the innovation vector is added to the EKF system to effectively detect and eliminate the gross errors in the measurements, to improve the filtering effect of EKF algorithm, and ensure the accuracy of pedestrian navigation results.

The PDR System Based on Improved QSF+ Map Matching Algorithm

The Zero-velocity Update (ZUPT) aided Extended Kalman Filter (EKF) is commonly used in the classical INS-based PDR system, which can effectively suppress the error growth of the inertial based pedestrian navigation systems. However, the system still suffers from the drift of heading error. The magnetic field is very useful to estimate the heading of the system, but the magnetic disturbance has a severely effect on the estimation. The Quasi-static magnetic Field (QSF) method was developed to estimate heading errors using magnetic field in perturbed environments, but the method may bring extra errors to system as the high false alarm probability of detecting the quasi-static field. In this paper, the improved QSF method is proposed to estimate the heading in the perturbed magnetic field. Also, the improved QSF method is combined with a compass filter, which can successfully extract the desired magnetic measurements and feedback them into the EKF to estimate the heading errors. At last, the iterative 2D map matching method is proposed to refine the trajectory of the PDR system, which can effectively suppress the long-term drift errors of the trajectory. The experiment result shows that the trial trajectory closed error length is 0.109%.

Research on Geomagnetic Matching Localization for Pedestrian

Nowadays, geomagnetic localization has become a new locating method for pedestrian. In this paper, geomagnetic matching localization method is proposed, the result of localization is based on the correlation between the measured geomagnetic data and the known base. In the flat terrain, pedestrian dead reckoning (PDR) is used to obtain mileage accumulation information to assist the geomagnetic matching locating, while in the rough terrain like elevators, stairways, etc., where PDR cannot provide reliable mileage information, the dynamic time warping (DTW) algorithm is used to warp the measured geomagnetic data and the known base. The experimental results show that the proposed method can provide the results of locating reliably and continuously and meet the demands of pedestrian localization.

Research on Topological Map Building Based on Crowdsourcing Data

Topological map is the basis of navigation and can be used for positioning. At present, the acquisition of map mainly relies on manual or machine measurement, which is costly. And for indoor situation, re-measurements are needed when the topology of the building changes. Aiming at the above problems and fully considering the widely use of smart mobile phone terminals and applications, this paper proposes a method for establishing topological map based on crowdsourcing data. Through conducting similarity analysis and matching of magnetic field intensity and WLAN/Bluetooth signal in crowdsourcing data, this method establishes connections between crowdsourcing data, and then construct a topological map with the main features of road segments. Experimental analysis shows that the method proposed can realize the self-generation of topology of general buildings by using data collected by ordinary users.

A Position Approach Based on the Special Sub-frame in TDD LTE-A System

With the urgent need for indoor and outdoor seamless navigation service, it is imperative to develop high precise positing technique suitable for various scenarios. In this paper, considering the widely deployment and well coverage of the wireless cellular communication system, a position approach is proposed based on time duplex division long term evolution advanced (TDD LTE-A) network. A novel complex frame structure for positing is proposed based on the current TDD LTE-A protocol. In GP (Guard Period) of the special sub-frame, i.e., the downlink-uplink switch period of TDD system, the navigation (NV) signal is added for positing. NV signals are transmitted simultaneously from different base station (BS) with orthogonal PN (Pseudo Noise) code. By measuring the distance to different BSs which are time synchronized in TDD system, user’s position can be fixed as in the GNSS system. In this paper, the theoretical performance of the proposed position scheme will be analyzed, and the impact to the current system will also be discussed. The results show that, by properly allocating the time length of NV signal based on the coverage of the cell, signal conflicts with the current LTE-A signal can be effectively avoided.

Research on Multi-Source Fusion Based Seamless Indoor/Outdoor Positioning Technology

GNSS has been applied widely. Yet because satellite signals are vulnerable and susceptible to blockage, the operability of GNSS in urban canyons are greatly hampered and GNSS even proves useless for indoor settings. This paper proposes system architecture for the integration of WLAN fingerprinting, visual positioning, baroceptor-derived altitude estimation and GNSS for seamless indoor/outdoor positioning for vehicles and pedestrians. This architecture augments GNSS through the integration of terminal-side/network-side positioning and position/measurement domain. After temporal and spatial synchronization, data from each sensor is filtered by sub filters and then processed by the main filter. The purpose of these operations is to provide accurate and continuous estimates of positions. Tests conducted in the new technology center of CAS show that the architecture proposed can achieve seamless indoor/outdoor positioning, with a better accuracy performance than any single-source method as the former still maintains accuracy and continuity when the later generates noticeable errors. Calculation shows that multi-source fusion has an accuracy level of better than 1 m (outdoor)/3 m (indoor), hence capable of meeting users’ demand for seamless indoor/outdoor positioning.

Mixed Interacting Filter for Tracking with Multiple System and Model

Due to the complicated and changeable environment for moving target. Tracking is difficult through single system to observate. And single motion model cannot describe the moving state for changeable state. On Bayesian estimation theorem, the mixed interacting multiple system and model filter algorithm for tracking is proposed (IMSM). Its performance is better than single dimension interacting filter. Finally simulation results show the effectiveness of the proposed algorithm.