Zhian Deng

Extended Kalman Filter for Real Time Indoor Localization by Fusing WiFi and Smartphone Inertial Sensors

Indoor localization systems using WiFi received signal strength (RSS) or pedestrian dead reckoning (PDR) both have their limitations, such as the RSS fluctuation and the accumulative error of PDR. To exploit their complementary strengths, most existing approaches fuse both systems by a particle filter. However, the particle filter is unsuitable for real time localization on resource-limited smartphones, since it is rather time-consuming and computationally expensive. On the other hand, the light computation fusion approaches including Kalman filter and its variants are inapplicable, since an explicit RSS-location measurement equation and the related noise statistics are unavailable. This paper proposes a novel data fusion framework by using an extended Kalman filter (EKF) to integrate WiFi localization with PDR. To make EKF applicable, we develop a measurement model based on kernel density estimation, which enables accurate WiFi localization and adaptive measurement noise statistics estimation. For the PDR system, we design another EKF based on quaternions for heading estimation by fusing gyroscopes and accelerometers. Experimental results show that the proposed EKF based data fusion approach achieves significant localization accuracy improvement over using WiFi localization or PDR systems alone. Compared with a particle filter, the proposed approach achieves comparable localization accuracy, while it incurs much less computational complexity.

Heading Estimation for Indoor Pedestrian Navigation Using a Smartphone in the Pocket

Heading estimation is a central problem for indoor pedestrian navigation using the pervasively available smartphone. For smartphones placed in a pocket, one of the most popular device positions, the essential challenges in heading estimation are the changing device coordinate system and the severe indoor magnetic perturbations. To address these challenges, we propose a novel heading estimation approach based on a rotation matrix and principal component analysis (PCA). Firstly, through a related rotation matrix, we project the acceleration signals into a reference coordinate system (RCS), where a more accurate estimation of the horizontal plane of the acceleration signal is obtained. Then, we utilize PCA over the horizontal plane of acceleration signals for local walking direction extraction. Finally, in order to translate the local walking direction into the global one, we develop a calibration process without requiring noisy compass readings. Besides, a turn detection algorithm is proposed to improve the heading estimation accuracy. Experimental results show that our approach outperforms the traditional uDirect and PCA-based approaches in terms of accuracy and feasibility.

Continuous Indoor Positioning Fusing WiFi, Smartphone Sensors and Landmarks

To exploit the complementary strengths of WiFi positioning, pedestrian dead reckoning (PDR), and landmarks, we propose a novel fusion approach based on an extended Kalman filter (EKF). For WiFi positioning, unlike previous fusion approaches setting measurement noise parameters empirically, we deploy a kernel density estimation-based model to adaptively measure the related measurement noise statistics. Furthermore, a trusted area of WiFi positioning defined by fusion results of previous step and WiFi signal outlier detection are exploited to reduce computational cost and improve WiFi positioning accuracy. For PDR, we integrate a gyroscope, an accelerometer, and a magnetometer to determine the user heading based on another EKF model. To reduce accumulation error of PDR and enable continuous indoor positioning, not only the positioning results but also the heading estimations are recalibrated by indoor landmarks. Experimental results in a realistic indoor environment show that the proposed fusion approach achieves substantial positioning accuracy improvement than individual positioning approaches including PDR and WiFi positioning.

Carrying Position Independent User Heading Estimation for Indoor Pedestrian Navigation with Smartphones

This paper proposes a novel heading estimation approach for indoor pedestrian navigation using the built-in inertial sensors on a smartphone. Unlike previous approaches constraining the carrying position of a smartphone on the user’s body, our approach gives the user a larger freedom by implementing automatic recognition of the device carrying position and subsequent selection of an optimal strategy for heading estimation. We firstly predetermine the motion state by a decision tree using an accelerometer and a barometer. Then, to enable accurate and computational lightweight carrying position recognition, we combine a position classifier with a novel position transition detection algorithm, which may also be used to avoid the confusion between position transition and user turn during pedestrian walking. For a device placed in the trouser pockets or held in a swinging hand, the heading estimation is achieved by deploying a principal component analysis (PCA)-based approach. For a device held in the hand or against the ear during a phone call, user heading is directly estimated by adding the yaw angle of the device to the related heading offset. Experimental results show that our approach can automatically detect carrying positions with high accuracy, and outperforms previous heading estimation approaches in terms of accuracy and applicability.

Reputation-Based Framework for Internet of Things

Internet of Things (IoT) is going to create a world where physical objects are integrated into traditional networks in order to provide intelligent services for human-beings. Trust plays an important role in communications and interactions of objects in IoT. Two vital tasks of trust management are trust model design and reputation evaluation. However, current literature cannot be simply and directly applied to the IoT due to smart node hardware constraints, very limited computing and energy resources. Therefore a general and flexible model is needed to meet the special requirements for IoT. In this paper, we firstly design LTrust, a layered trust model for IoT. Then, a Reputation Evaluation Scheme for the Node (RES-N) has been presented. The proposed trust model and reputation evaluation scheme provide a general framework for the study of trust management for IoT. The efficiency of RES-N is validated by the simulation results.

Data Association of AIS and Radar Based on Multi-factor Fuzzy Judgment and Gray Correlation Grade

This paper proposes a data association algorithm based on multi-factor fuzzy judgment and gray correlation analysis, in order to improve the correct correlation between AIS and radar targets. The target track is formatted into a sequence of four factors in this algorithm, such as distance, bearing, speed and course. We compute preliminary the algorithm of multi-factor fuzzy judgment based on four factors. And if the target satisfies the preliminary associated conditions with four factors, we continue to do the gray correlation analysis. Compared to the multi-factor fuzzy judgment, the simulation results of this paper show that the algorithm can reduce the probability of false association effectively. And compared to the gray correlation analysis, the algorithm can reduce the calculation range effectively.

WLAN Indoor Positioning Based on D-LDA Feature Extraction Algorithm

This paper introduces the Direct Linear Discriminant Analysis (D-LDA) algorithm for feature extraction to reduce noise and redundant location information of the access points (APs) signals in wireless LAN (WLAN) indoor positioning system. Feature database is obtained by deploying D-LDA algorithm to extract the low-dimensional and discriminative positioning features from the original WLAN signal database. The dimensionality of the extracted features may be chosen by setting appropriate retained eigenvalues ratio of between-class scatter matrix. Based on the generated feature database, three typical localization algorithms including weighted k-nearest neighbor (WKNN), nearest-neighbor (NN) and maximum likelihood (ML) are carried for real-time positioning and the results are compared. D-LDA feature extraction algorithm obtains the higher accuracy than traditional localization algorithms while reducing the storage and computation cost significantly.

Outlier Filtering Algorithm for Indoor Pedestrian Walking Direction Estimation

This paper introduces an outlier filtering algorithm to improve the indoor pedestrian walking direction estimation accuracy performance. Our previous proposed RMPCA approach combines rotation matrix (RM) and Principal Component Analysis (PCA) to extract pedestrian walking direction using a smartphone in the trouser pocket. Performance of the RMPCA approach may deteriorate if an irregular leg locomotion occurs or device slides in the pocket. If this situation occurs, it may be detected by the proposed outlier filtering algorithm. Then, walking direction of the current step may be obtained by averaging the walking direction estimations of the adjacent normal walking steps. Experiments show that the proposed outlier filtering algorithm may avoid large estimation errors and improve accuracy performance of RMPCA approach.

Review on Cognitive Radio Technology for SatComs

The demand on the crowded spectrum in satellite communication (SatComs) is greatly increasing due to the explosive growth of mobile terminals and applications. However, the present spectrum allocation policy suffers from a lot of drawbacks, because it leaves spectrum holes for the incumbent users. At present, much attention is paid to cognitive satellite technology because it copes with these drawbacks. In this paper, we investigate several major aspects of cognitive satellite communication technology. First, we sort out main scenarios of cognitive SatComs. Then, we list the development of the essential techniques in cognitive radios for SatComs. More specifically, we elaborate three techniques including spectrum sensing, interference modeling and beamhopping. At last, we make a conclusion and prospect of cognitive SatComs.

Machine Learning and Its Applications in Wireless Communications

Machine Learning (ML) can improve system performance in many fields such as data dining, object tracking, spectrum sensing and indoor positioning. A review on ML was presented in this paper. Firstly, we looked back to the development, definition and classification of ML; secondly, we summarized the basic principle, mathematical formulation and application methods of two classic algorithms named error back-propagation (BP) and clustering; then, we focused on advanced and typical applications of ML in communication systems like cognitive radio networks (CRNs) and positioning system; finally, we concluded that the system performance could be improved by ML technique.