WiFi fingerprinting indoor positioning systems have extensive applied prospects. However, a vast amount of data in a particular environment has to be gathered to establish a fingerprinting database. Deficiencies of these systems are the lack of universality of multipath effects and a burden of heavy workload on fingerprint storage. Thus, this paper presents a novel Random Forest fingerprinting localization (RFFP) method using channel state information (CSI), which utilizes the Random Forest model trained in the offline stage as fingerprints in order to economize memory space and possess a good anti-multipath characteristic. Furthermore, a series of specific experiments are conducted in a microwave anechoic chamber and an office to detail the localization performance of RFFP with different wireless channel circumstances, system parameters, algorithms, and input datasets. In addition, compared with other algorithms including K-Nearest-Neighbor (KNN), Weighted K-Nearest-Neighbor (WKNN), REPTree, CART, and J48, the RFFP method provides far greater classification accuracy as well as lower mean location error. The proposed method offers outstanding comprehensive performance including accuracy, robustness, low workload, and better anti-multipath-fading.

With the rapid development of WIFI technology, WIFI-based indoor positioning technology has been widely studied for location-based services. To solve the problems related to the signal strength database adopted in the widely used fingerprint positioning technology, we first introduce a new system framework in this paper, which includes a modified AP firmware and some cheap self-made WIFI sensor anchors. The periodically scanned reports regarding the neighboring APs and sensor anchors are sent to the positioning server and serve as the calibration points. Besides the calculation of correlations between the target points and the neighboring calibration points, we take full advantage of the important but easily overlooked feature that the signal attenuation model varies in different regions in the regression algorithm to get more accurate results. Thus, a novel method called RSSI Geography Weighted Regression (RGWR) is proposed to solve the fingerprint database construction problem. The average error of all the calibration points’ self-localization results will help to make the final decision of whether the database is the latest or has to be updated automatically. The effects of anchors on system performance are further researched to conclude that the anchors should be deployed at the locations that stand for the features of RSSI distributions. The proposed system is convenient for the establishment of practical positioning system and extensive experiments have been performed to validate that the proposed method is robust and manpower efficient.

In this paper, we present a novel method for 3D pedestrian navigation of foot-mounted inertial systems by integrating a MEMS-IMU, barometer, and permanent magnet. Zero-velocity update (ZUPT) is a well-known algorithm to eliminate the accumulated error of foot-mounted inertial systems. However, the ZUPT stance phase detector using acceleration and angular rate is threshold-based, which may cause incorrect stance phase estimation in the running gait pattern. A permanent magnet-based ZUPT detector is introduced to solve this problem. Peaks extracted from the magnetic field strength waveform are mid-stances of stance phases. A model of peak-peak information and stance phase duration is developed to have a quantitative calculation method of stance phase duration in different movement patterns. Height estimation using barometer is susceptible to the environment. A height difference information aided barometer (HDIB) algorithm integrating MEMS-IMU and barometer is raised to have a better height estimation. The first stage of HDIB is to distinguish level ground/upstairs/downstairs and the second stage is to calculate height using reference atmospheric pressure obtained from the first stage. At last, a ZUPT-based adaptive average window length algorithm (ZUPT-AAWL) is proposed to calculate the true total travelled distance to have a more accurate percentage error (TTDE). This proposed method is verified via multiple experiments. Numerical results show that TTDE ranges from 0.32% to 1.04% in both walking and running gait patterns, and the height estimation error is from 0 m to 2.35 m.

The pedestrian navigation system (PNS) based on inertial navigation system-extended Kalman filter-zero velocity update (INS-EKF-ZUPT or IEZ) is widely used in complex environments without external infrastructure owing to its characteristics of autonomy and continuity. IEZ, however, suffers from performance degradation caused by the dynamic change of process noise statistics and heading estimation errors. The main goal of this study is to effectively improve the accuracy and robustness of pedestrian localization based on the integration of the low-cost foot-mounted microelectromechanical system inertial measurement unit (MEMS-IMU) and ultrasonic sensor. The proposed solution has two main components: (1) the fuzzy inference system (FIS) is exploited to generate the adaptive factor for extended Kalman filter (EKF) after addressing the mismatch between statistical sample covariance of innovation and the theoretical one, and the fuzzy adaptive EKF (FAEKF) based on the MEMS-IMU/ultrasonic sensor for pedestrians was proposed. Accordingly, the adaptive factor is applied to correct process noise covariance that accurately reflects previous state estimations. (2) A straight motion heading update (SMHU) algorithm is developed to detect whether a straight walk happens and to revise errors in heading if the ultrasonic sensor detects the distance between the foot and reflection point of the wall. The experimental results show that horizontal positioning error is less than 2% of the total travelled distance (TTD) in different environments, which is the same order of positioning error compared with other works using high-end MEMS-IMU. It is concluded that the proposed approach can achieve high performance for PNS in terms of accuracy and robustness.

Channel state information (CSI) fingerprint based indoor positioning has been extensively studied. While centimeter-level positioning accuracy has been demonstrated for Wi-Fi systems, only meterlevel accuracy was reported for Long-term Evolution (LTE) systems. To investigate whether the centimeter accuracy is achievable or not for LTE systems, we examine the factors that affect the positioning accuracy to guide the implementation of a LTE-CSI fingerprinting system, where a novel phase compensation method is proposed to improve the CSI quality. We demonstrate for the first time that the centimeter-level positioning accuracy is achievable for LTE systems through extensive experiments.

Non-line-of-sight (NLOS) propagation is one of the main reasons for degrading the performance of ultra-wideband (UWB) positioning technique. Aiming to improve the performance for pedestrian location estimation in underground space environment with UWB, fuzzy comprehensive evaluation (FCE) NLOS identification algorithm based on Wilcoxon Signed Rank Test (WSRT) and entropy weight method (EWM) is proposed, firstly. Then, this article presents a two-stage UWB-based fusion positioning scheme with the aid of inertial measurement unit (IMU) and geomagnetic (MAG). In first stage, an combination algorithm based on genetic algorithm (GA) is proposed for IMU and MAG data fusion. In second stage, when a sufficient number of UWB ranging values are not available under NLOS condition, a IMU/MAG aided UWB fusion improvement robust extent Kalman filtering (IREKF) for location estimation is introduced, which makes full use of the ranges value of the UWB in the residual LOS condition. Finally, the experiment is carried out in underground space and the experimental results show that using two-stage fusion algorithm, the proposed fusion positioning scheme can effectively improve positioning accuracy and robustness, especially in the area of UWB NLOS situation.