Journal of Sensors

Volume 2017, Article ID 7879198, 11 pages

https://doi.org/10.1155/2017/7879198

## Integrated SINS/WSN Positioning System for Indoor Mobile Target Using Novel Asynchronous Data Fusion Method

^{1}School of Mechatronic Engineering, Southwest Petroleum University, Chengdu, Sichuan 610500, China^{2}School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China^{3}College of Internet of Things Engineering, Hohai University, Changzhou, Jiangsu 213022, China

Correspondence should be addressed to Wei Li; moc.oohay@215tmuceemc

Received 21 March 2017; Accepted 13 June 2017; Published 20 July 2017

Academic Editor: Mohannad Al-Durgham

Copyright © 2017 Hai Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

According to the asynchronous transmission of data for the SINS/WSN integrated positioning system, this paper proposes a novel asynchronous data fusion method using Unscented Kalman Filter for SINS/WSN integrated positioning system based on indoor mobile target. The state equation of the integrated system is built with the motion characteristic of mobile target. The pseudo measurement equation is built based on the time sequence of SINS/WSN measured value through detecting the measurement of WSN in every fusion period. Considering that the improved state-space model, comprised of the state equation and pseudo measurement equation, is the nonlinear equations, the Unscented Kalman Filter is applied to estimate the state value of the state-space model. Hence the asynchronous data fusion method for SINS/WSN integrated positioning system can be achieved. Simulation results and experimental tests show that the positioning system with proposed asynchronous data fusion algorithm performs feasibility and stability under circumstances of the asynchronous time, and it is superior to the traditional asynchronous data fusion and synchronous data fusion methods.

#### 1. Introduction

Indoor localization of mobile target is playing an increasingly important role on home intelligence, factory controllability, and shopping malls automation [1]. Indoor positioning technology is a critical part in the location based services for indoor mobile target. High-precision positioning system can improve the automation and intelligence of mobile targets [2]. Strap-down Inertial Navigation System (SINS), entirely self-contained within the mobile target, neither sends signal to external nor depends on external signal [3]. The SINS can continuously supply the overall motion parameters and the short-term high performance for indoor positioning; however the SINS are known for their drift with time [4]. In order to achieve long-term stability, other technologies will be used to support the SINS. It is well known that the Global Positioning System (GPS) can supply the high-precision position and velocity of the mobile target [5]. According to the cumulative error of SINS, the GPS can be applied to correct the SINS; then the integrated positioning system will be established based on SINS and GPS [6]. However, the signal of GPS is obstructed by the building; a non-GPS localization system need be aided by the SINS for indoor mobile target.

A Wireless Sensor Network (WSN) has enormous potential for the short-range indoor localization with intelligent and distributed network [7]. WSN is composed of some mobile nodes and a large number of anchor nodes through the self-organization and multihop methods [8]. In order to achieve continuous indoor positioning, some scholars put forward the INS/WSN integrated positioning system [9]. Hur and Ahn [10] propose a localization technique for mobile target using INS/WSN based on an intelligent filter with low complexity. Chen et al. [11] propose an INS/WSN integration system of mobile target with adaptive extended Kalman Filter.

Because the WSN and SINS are two separate (self-contained) subsystems, the data alignment discrepancies between SINS and WSN would appear with the clock difference and data transmission latency. The time synchronization between WSN and SINS becomes a matter of great public interest before the integrated positioning system is implemented [12]. The data alignment discrepancies could lead the suboptimal fusion algorithm and low-precision for the integrated system. This is due to the reason that the majority of the data fusion theories need to work in ideal condition without system time bias. However, the actual positioning system with multisensor cannot meet the condition of synchronous data fusion model [13]. In order to research this problem, Hu et al. [14] propose a batch asynchronous data fusion algorithm which can strictly synchronize asynchronous measurement in the time domain. Skog and Handel [15] and Yang and Shim [16] analyze the effects of time synchronization errors in a GPS-aided INS integrated positioning system and proposed a software-based time synchronization method using a data integration filter. Gao [17] proposes an asynchronous data fusion algorithm to solve problems about asynchronous fusion of multisensors and achieved good results for the SINS/GPS/CNS integrated navigation system. However, the data type of GPS is different from the WSN. The time synchronization method of GPS/INS integrated positioning system cannot be applied to the WSN/SINS positioning system. At the present, the proposed fusion method iteratively calculates the state equation for whole asynchronous data in every update period, which is described as the traditional asynchronous fusion algorithm. Furthermore, the traditional asynchronous fusion algorithms require large calculation burden and cost a lot of time, which may cause large position error occurring in a high-dynamic system.

Given all that, nowadays the research for time synchronization algorithms of integrated positioning system is mainly used for the GPS/INS. The traditional asynchronous data fusion algorithms have some adverse conditions which are the large calculation burden, being time-consuming, and large position error for high-dynamic system. To solve these problems, this paper proposed an asynchronous data fusion algorithm with Unscented Kalman Filter (UKF) to apply the SINS/WSN integrated positioning system. The state equation of state-space model is built with the motion characteristic of mobile target. The measurement equations of two subsystems are established based on the measurements of SINS and WSN, respectively. The time duration which is comprised of some sample periods of SINS is used for time unit of the fusion center. If the measurement of WSN existed in a time unit, the pseudo measurement equation of SINS/WSN will be built based on the asynchronous data fusion method. On the contrary, the measurement equation of pure SINS will be used. Hence the state-space model for asynchronous data fusion method is established. Owing to the nonlinear improved state-space model, the UKF is applied to estimate the state value. Then the novel asynchronous fusion with UKF for SINS/WSN integrated positioning system is achieved.

The rest of the paper is organized as follows. The proposed state-space model established with asynchronous data fusion theory is presented in Section 2. Section 3 describes Unscented Kalman Filter algorithm, while Section 4 evaluates the asynchronous data fusion method for SINS/WSN integrated positioning system in simulation. In Section 5, we examine the performance of the proposed method. Finally, Section 6 concludes the paper.

#### 2. State-Space Model for Positioning System

This section describes the process of establishment for the state-space model. This model is built with state equation and pseudo measurement equation of SINS/WSN integrated positioning system through the asynchronous data fusion method.

##### 2.1. State Equation

The state vector of integrated system can be expressed as and are the position and velocity of mobile target in the navigation frame (-frame), respectively. is the triaxis attitude angle of mobile target and is the acceleration of mobile target in the body frame (-frame).

According to the movement characteristics of mobile target, the motion equations are illustrated as follows:where is the --frame transformation matrix. is the sample time of system. Then the state equation of mobile target is shown aswhere represents system noise vector, . and are gyros drift and accelerometer bias, respectively. represents the -dimension nonlinear vector function. is the model input matrix of system.

##### 2.2. Measurement Equations of SINS and WSN

The measurement vector of SINS can be expressed as , where and are the position and velocity of SINS. The measurement equation of SINS can be expressed in a generic form aswhere , the Gaussian process noise, is the measurement noise of SINS as . The measurement transformation matrix is denoted as

The measurement vector of WSN can be defined as , where is the position of WSN. Then the measurement equation of WSN can be expressed aswhere , the Gaussian process noise, is the measurement noise of WSN as . The measurement transformation matrix is denoted as .

##### 2.3. Description for Asynchronous Sensing

According to the systematic characteristics of SINS and WSN, the SINS and WSN measure the motion information of mobile target independently. The sample time of SINS and WSN can be denoted by and , and . We can define as the number of measurements for all sensors at the interval (, ]. Then note that one or more measured values might be supplied by a sensor, or none of measured values might be supplied by this sensor. and are the number of measurements from SINS and WSN at (, ], respectively. So the relationship is given by

is the time interval between and the moment for th measurement of SINS (). is the time interval between and the moment for* i*th measurement of WSN . According to the time stamps of data, all measurements are sorted with the order of measured time in fusion center, after all measurements have reached the fusion center at (, ]. Because the update frequency of SINS is considerably greater than that of WSN, it may lead to nonmeasurement of WSN in some fusion periods when the fusion period is smaller than the sampling period of WSN. Considering the high accuracy clock frequency of SINS, the time range with a fixed number of sampling periods of SINS is set as the data fusion period. Figure 1 displays the timeline of measured value.