Chengming Luo

Implementation of Mobile Target Positioning Technology Integrating SINS with WSN Measurements

Accurate and robust positioning technology is expected to promote management level and service efficiency in various industrial applications. The strap-down inertial navigation system (SINS) approach has short-term accurate positioning performance, but the SINS is known for its accumulative error over time. Meanwhile, wireless sensor networks (WSN) approach can keep the mobile target on effective tracking after a long time monitoring, but the WSN may have large positioning error in certain areas. In order to make the positioning method allow profit from their advantages, this paper proposes a positioning technology using SINS approach in conjunction with WSN approach. The measurement parameters by SINS and WSN approaches are used. Then the SINS, anchor nodes, mobile tags, XBee, and computer are applied to design the positioning system. The estimation results indicate that the proposed method can make up for the shortcomings by pure SINS or WSN method and can be available for some accurate and robust applications.

Research on the Strategy of Motion Constraint-Aided ZUPT for the SINS Positioning System of a Shearer

The accurate measurement of position and orientation for shearers is a key technology in realizing an automated, fully-mechanized, coal mining face. Since Global Positioning System (GPS) signal cannot arrive at the coal mine underground, wireless sensor network positioning system cannot operate stably in the coal mine; thus a strap-down inertial navigation system (SINS) is used to measure the position and orientation of the shearer. Aiming at the problem of the SINS accumulative error, this paper proposes a positioning error correction method based on the motion constraint-aided SINS zero velocity updated (ZUPT) model. First of all, a stationary state detection model of the shearer is built with median filter based on the acceleration and angular rate measured by the SINS. Secondly, the motion of the shearer is analyzed using coal mining technology, then the motion constraint model of the shearer is established. In addition, the alternate action between the motion constraint model and the ZUPT model is analyzed at the process of movement and cessation of the shearer, respectively; hence, the motion constraint-aided SINS ZUPT model is built. Finally, by means of the experimental platform of the SINS for the shearer, the experimental results show that the maximum position error with the positioning model proposed in this paper is 1.6 m in 180 s, and increases by 92.0% and 88.1% compared with the single motion constraint model and single ZUPT model, respectively. It can then restrain the accumulative error of the SINS effectively.

Research on Error Compensation Property of Strapdown Inertial Navigation System Using Dynamic Model of Shearer

The strapdown inertial navigation system (SINS) can be installed on a shearer and used for monitoring its position. However, under the complex environment of the mechanized mining face, the strong vibration of the shearer may cause large calculation error. First, the dynamic model of a double-drum shearer is built with a force analysis, and the spectrum characteristics of linear vibration and angular vibration for the fuselage are then obtained. Second, the coning error and sculling error compensation models of SINS for the shearer are derived based on vibration characteristics. Meanwhile, according to the factor of the uncompensated model, multi-sample compensation model, and different coal and rock traits and different vibration frequencies of the fuselage, the shearer SINS error compensation property under multiple parameters is researched and analyzed in simulation. Finally, simulations indicate that the SINS error compensation model with the three-sample algorithm and four-sample algorithm can improve the calculating accuracy of the shearer SINS. The coning and sculling errors can be compensated effectively by the shearer error compensation model under many vibration conditions, such as different coal and rock traits and different frequencies of the fuselage.

Positioning Accuracy Evaluation for the Collaborative Automation of Mining Fleet With the Support of Memory Cutting Technology

Taking mining fleet constituted by a shearer, hydraulic supports and a scraper conveyor as the object, the mining fleet needs to move to the intended position in accordance with the functional requirements, such as machinery tracking of hydraulic supports and memory cutting of the shearer. This paper proposes a shearer wireless positioning method under the conditions of inaccurate anchor nodes. First, action rules for hydraulic supports and an adaptive height adjustment strategy for the shearer are arranged based on analyzing the cooperative movement of mining fleet. Second, the duality mapping between local strong signal sets and positioning spatial domain is revealed, and inaccurate anchor nodes can be refined using the memory cutting and motion constraints of mining fleet. Third, an extended Cramer-Rao lower bound is derived to seek the inherent relationships among shearer positioning accuracy, multi-source errors, and coordinate errors of anchor nodes. Finally, comprehensive experiments for the analytical accuracy assessment and node configuration of shearer positioning are achieved with the support of memory cutting technology. Research results indicate that the proposed shearer positioning can satisfy the requirements of mining fleet, which can provide the theoretical basis for the collaborative automation of mining fleet on fully mechanized mining face.

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

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.

A Fuzzy Adaptive Tightly-Coupled Integration Method for Mobile Target Localization Using SINS/WSN

In recent years, mobile target localization for enclosed environments has been a growing interest. In this paper, we have proposed a fuzzy adaptive tightly-coupled integration (FATCI) method for positioning and tracking applications using strapdown inertial navigation system (SINS) and wireless sensor network (WSN). The wireless signal outage and severe multipath propagation of WSN often influence the accuracy of measured distance and lead to difficulties with the WSN positioning. Note also that the SINS are known for their drifted error over time. Using as a base the well-known loosely-coupled integration method, we have built a tightly-coupled integrated positioning system for SINS/WSN based on the measured distances between anchor nodes and mobile node. The measured distance value of WSN is corrected with a least squares regression (LSR) algorithm, with the aim of decreasing the systematic error for measured distance. Additionally, the statistical covariance of measured distance value is used to adjust the observation covariance matrix of a Kalman filter using a fuzzy inference system (FIS), based on the statistical characteristics. Then the tightly-coupled integration model can adaptively adjust the confidence level for measurement according to the different measured accuracies of distance measurements. Hence the FATCI system is achieved using SINS/WSN. This innovative approach is verified in real scenarios. Experimental results show that the proposed positioning system has better accuracy and stability compared with the loosely-coupled and traditional tightly-coupled integration model for WSN short-term failure or normal conditions.

Energy Efficiency and QoS Enhancement for Wireless Sensor Networks with Applications to Long-Narrow Structures

There is a class of special environments, such as roads, mines tunnels, rivers, bridges, and pipelines, whose geographical shapes are long-narrow for several hundred meters. Wireless sensor networks (WSN) can be applied to monitor these environments. Long-narrow structure makes the WSN face plenty of challenges, such as unbalanced energy consumption and data aggregation. This paper proposes a nonuniform symmetric cluster model (NUSCM) using reasonable coverage routing controlling. The NUSCM consists of two base stations, sensor node clusters (SNCs) and transmission node clusters (TNCs), which can make the sensor networks be scalable to cover various long-narrow structures. Hierarchical nodes spacing and routing strategy of NUSCM are addressed. Furthermore, we simulate the proposed NUSCM, in comparison with the nonuniform deployment with two base stations (NUD-TBS) and uniform deployment with two base stations (UD-TBS). Research results indicate that the NUSCM and NUD-TBS have the same energy efficiency, which are better than that of UD-TBS. Moreover, NUSCM is superior to the UD-TBS and NUD-TBS in the link communication load and network survivability.

A Single-Way Ranging Localization of AUVs Based on PSO of Outliers Elimination

Localization of autonomous underwater vehicles (AUVs) is a very important and challenging task for the AUVs applications. In long baseline underwater acoustic localization networks, the accuracy of single-way range measurements is the key factor for the precision of localization of AUVs, whether it is based on the way of time of arrival (TOA), time difference of arrival (TDOA), or angle of arrival (AOA). The single-way range measurements do not depend on water quality and can be taken from long distances; however, there are some limitations which exist in these measurements, such as the disturbance of the unknown current velocity and the outliers caused by sensors and errors of algorithm. To deal with these problems, an AUV self-localization algorithm based on particle swarm optimization (PSO) of outliers elimination is proposed, which improves the performance of angle of arrival (AOA) localization algorithm by taking account of effects of the current on the positioning accuracy and eliminating possible outliers during the localization process. Some simulation experiments are carried out to illustrate the performance of the proposed method compared with another localization algorithm.

Positioning technology of mobile vehicle using self-repairing heterogeneous sensor networks

Abstract Mobile vehicle positioning can provide the reference to navigation, tracking and multi vehicles collaboration. Applying spatiotemporal distribution characteristics of positioning errors between strap-down inertial navigation system (SINS) and wireless sensor network (WSN) approaches, a mobile vehicle positioning is proposed as a component of heterogeneous sensor networks (HSN). The attitude, velocity and position equations of mobile vehicle are derived based on the kinematics parameter constraints and inertial parameter errors. Meanwhile, WSN approach can provide position estimation using inaccurate anchor nodes. However, SINS is known for its cumulative errors over long time, while WSN approach can have large positioning errors in certain areas. As an effort to overcome the limitations of pure SINS or WSN approach, an integrated SINS and WSN approach is proposed to form a self-repairing HSN approach, which can provide sound position and attitude for mobile vehicle. Then, multi-parameter interaction and cooperative correction strategy are explored when SINS or WSN measurement is abnormal. Finally, a comprehensive set of experiments of position and attitude estimations for mobile vehicle are performed on the actual environment platform.