Ziyan Jia

The indoor localization method based on the integration of RSSI and inertial sensor

The research of localization has become a more and more important topic with the popularity of ubiquitous mobile computing. In indoor environment, since the global positioning system (GPS) is disabled, many miniaturized wireless and sensing technologies have shown giant potential in positioning applications such as Inertial Navigation. In this context, this paper present a methodology to locate and track pedestrians accurately in indoor scenarios, the proposed method employs the extended Kalman filter (EKF) to integration Received Signal Strength Indication (RSSI) measurements with the Inertial Navigation technology. Aiming at the cumulative errors existed in Pedestrian Dead Reckoning (PDR) algorithm, this method uses RSSI information as measurement vector of EKF to correct the cumulative errors. Experimental results show that the proposed fusion method can present more reliable positioning estimations.

A hybrid indoor positioning system based on UWB and inertial navigation

The paper illustrates to integrate UWB technology with inertial navigation, and introduces the design of the hybrid indoor positioning system including server, gateway and sensor nodes. A linear regression matrix model is proposed to modify inertial navigation path. Least squares method is used to determine equations relating UWB trajectories to inertial navigation trajectories. Experimental results are shown to compare error distributions of hybrid algorithm and pure UWB solution.

Differential coding and detection methods in SC-FDMA systems

In this paper, the differential encoding and detection methods in single-carrier frequency-division multiple access (SC-FDMA) systems are investigated. Like in OFDM systems, the differential encoding can be performed in time-domain or frequency-domain in SC-FDMA systems. Also, we can use differential encoding before or after the discrete Fourier transform (DFT) process. Thus the detail design of transmitter with each of the four differential coding methods and the corresponding detection methods is considered and analyzed.

The Optimal Placement Method of Anchor Nodes toward RSS-based Localization Systems

The needs for location-aware applications are increasing with the popularity of ubiquitous mobile computing. However, as an important factor affecting the positioning performance, the deployment of anchor nodes has received little attention during the past years. In this paper we investigate the anchor placement in the received signal strength (RSS) based wireless localization systems. An optimization method based on integer-coded genetic algorithm is proposed. It takes the average localization error and the signal coverage estimate as a joint optimization criterion. The influence of RSS fluctuation on the system is also considered in the algorithm. We evaluate the performance of our proposed algorithm in simulations and the results are discussed in details.

A novel cooperative global spectrum sensing algorithm based on variational Bayesian inference

This paper proposed an approximate model for the global power spectral density (PSD) of primary users (PUs). Based on the proposed model, a novel cooperative spectrum sensing algorithm was proposed, and its overall flow was also built to obtain global information in the network of PUs. The global information includes locations, occupied frequency bands and transmit powers of the PUs. Then, an estimator of model coefficient vector was designed by utilizing the theory of Variational Bayesian Inference (VBI). Simulation results show that the proposed approximate model has good accuracy, and the corresponding estimation algorithm of model coefficient vector has good convergence and stability. Meanwhile, it is proved that the proposed algorithm has better Mean Square Error (MSE) performance since the sparsity of model coefficient vector is utilized.

An indoor positioning algorithm for mobile objects based on track smoothing

In this paper, a new indoor positioning algorithm is proposed for mobile objects based on track smoothing. Considering the effects of indoor environment and the individual anchor nodes, the algorithm is realized in two filtering phases to modify the UWB range measurements and the trilateration localization results by the Unscented Kalman Filter, which reduces the influence of the additive non-Gaussian noise. Furthermore, the filtered positioning results of a mobile object are smoothed by a proposed adaptive smoothing algorithm, and the accuracy has been significantly improved with the positioning RMSE less than 0.8m and the maximum error is about 1m.

Experimental demonstration of pulse amplitude modulated visible light communications

We experimentally demonstrate a visible light communication (VLC) system using pulse amplitude modulation (PAM). A commercially available phosphorised white LED is used as the optical transmitter and a Si photodiode (PD) is used as the optical receiver. The experiment is mainly implemented in an indoor environment which is influenced by the sunlight and fluorescent lamp. In variant conditions, the experimental results using 4-PAM and 2-PAM (i.e. on-off-keying (OOK)) and the related analysis are reported.

A VLC-Based 3-D Indoor Positioning System Using Fingerprinting and K-Nearest Neighbor

In this paper, a three-dimensional (3-D) positioning system using fingerprinting and K-Nearest neighbor is proposed. The implementation of the proposed positioning system based on visible light communication (VLC) is discussed. On-off keying (OOK) modulation, Manchester coding and time division multiplexing (TDM) are utilized to obtain both lossless data and accurate received signal strength indications (RSSIs) without sacrificing the comfort of indoor illumination. The proposed 3-D positioning algorithm is conducted in three processes. Firstly, we quickly traverse all sampling points in the fingerprint database to find the nearest neighbor and choose candidate sampling points around the nearest neighbor. Secondly, we conduct an iterative search among candidate sampling points to find K-Nearest neighbors. Finally, we obtain estimated coordinates using weighted average method. Experimental result shows that, the proposed system possesses fast location and high accuracy.

Indoor Localization System for Mobile Target Tracking Based on Visible Light Communication

In this paper, a positioning system is designed based on the visible light communication (VLC) technology for the tracking of mobile target in the indoor environment. The practical received signal strength (RSS) can be measured for subsequent positioning algorithm by using the designed system. Based on the practical measurements, an empirical equation is proposed to estimate the targets distances relative to the referenced LEDs. Then, taking advantage of the relative distances, the location estimation can be obtained by the trilateration method. Furthermore, Kalman Filter is used for improving the positioning result computed by trilateration method. Finally, experimental results show that the average location errors by adopting the derived empirical formula and trilateration can reach to 3.4cm, and the accuracy of positioning can be promoted to 2.6cm by using Kalman Filter in a real wireless environment.

A novel robust spatial spectrum sensing algorithm

This paper proposed a robust spatial spectrum sensing algorithm to apply to a communication scenario which exist the estimated error of noise power and the jamming. A modified approximate model is proposed to include the estimated error of noise power and the jamming. The corresponding performance indicators are proposed. The proposed robust algorithm estimates the estimated error vector of noise power and the jamming vector, independently. The estimated error vector of noise power and the jamming vector are separated from model coefficient vector, and the influence of estimated error and jamming is mitigated. The robustness of proposed algorithm is achieved. Simulation results show that the proposed robust algorithm has best sensing performances, and best accuracy of PU estimation, comparing with other sparsity algorithms. The sensing performances of proposed robust algorithm are not easy to be affected by the sampling rate of secondary users. When the sampling rate of secondary users or SNR is increasing, these sensing performances of proposed robust algorithm are also increasing.