Yuan Xu

INS/WSN-Integrated Navigation Utilizing LS-SVM and ∞ Filtering

In order to achieve continuous navigation capability in areas such as tunnels, urban canyons, and indoors a new approach using least squares support vector machine (LS-SVM) and 𝐻∞ filter (HF) for integration of INS/WSN is proposed. In the integrated system, HF estimates the errors of position and velocity while the signals in WSNs are available. Meanwhile, the compensation model is trained by LS-SVM with corresponding HF states. Once outages of the signals in WSNs, the model is used to correct INS solution as HF does. Moreover, due to device reasons, there are slight fluctuations in sampling period in practice. For overcoming this problem of integrated navigation, the theoretical analysis and implementation of HF for an integrated navigation system with stochastic uncertainty are also given. Simulation shows the performance of HF is more robust compared with INS-only solution and Kalman filter (KF) solution, and the prediction of LS-SVM has the smallest error compared with INS-only and back propagation (BP), the improvement is particularly obvious.

Autonomous Integrated Navigation for Indoor Robots Utilizing On-Line Iterated Extended Rauch-Tung-Striebel Smoothing

In order to reduce the estimated errors of the inertial navigation system (INS)/Wireless sensor network (WSN)-integrated navigation for mobile robots indoors, this work proposes an on-line iterated extended Rauch-Tung-Striebel smoothing (IERTSS) utilizing inertial measuring units (IMUs) and an ultrasonic positioning system. In this mode, an iterated Extended Kalman filter (IEKF) is used in forward data processing of the Extended Rauch-Tung-Striebel smoothing (ERTSS) to improve the accuracy of the filtering output for the smoother. Furthermore, in order to achieve the on-line smoothing, IERTSS is embedded into the average filter. For verification, a real indoor test has been done to assess the performance of the proposed method. The results show that the proposed method is effective in reducing the errors compared with the conventional schemes.

Adaptive iterated extended Kalman filter and its application to autonomous integrated navigation for indoor robot.

As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF) which used the noise statistics estimator in the iterated extended Kalman (IEKF), and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS)/wireless sensors networks (WSNs)-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE) of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF.

Application of Adaptive Extended Kalman Smoothing on INS/WSN Integration System for Mobile Robot Indoors

The inertial navigation systems (INS)/wireless sensor network (WSN) integration system for mobile robot is proposed for navigation information indoors accurately and continuously. The Kalman filter (KF) is widely used for real-time applications with the aim of gaining optimal data fusion. In order to improve the accuracy of the navigation information, this work proposed an adaptive extended Kalman smoothing (AEKS) which utilizes inertial measuring units (IMUs) and ultrasonic positioning system. In this mode, the adaptive extended Kalman filter (AEKF) is used to improve the accuracy of forward Kalman filtering (FKF) and backward Kalman filtering (BKF), and then the AEKS and the average filter are used between two output timings for the online smoothing. Several real indoor tests are done to assess the performance of the proposed method. The results show that the proposed method can reduce the error compared with the INS-only, least squares (LS) solution, and AEKF.

Study of the algorithm of backtracking decoupling and adaptive extended Kalman filter based on the quaternion expanded to the state variable for underwater glider navigation.

High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method.

Improved Unscented Kalman Filter Based on Decimation in Frequency Domain Fast Fourier Transform for Attitude Estimation Applied to Underwater Gliders

In order to estimate the attitude fast and accurately for the underwater glider using the lower cost and lower power underwater navigation system, this paper designs a new underwater navigation system which is made up of the inertial sensors aided the magnetometer and proposes an improved unscented Kalman filter based on decimation in frequency domain fast Fourier transform (UKF-DF). UKF-DF makes better use of the estimate advantage of UKF in the nonlinear system, and in this basis DIF-FFT is integrated into UKF to increase the speed of calculation. Therefore, the attitude of a glider can be estimated fast and accurately. The real vehicle experiment is done to assess the performance of the proposed UKF-DF algorithm, the experimental results show that the attitude convergence of UKF-DF is better than EKF (extended Kalman filter) and the attitude estimated by UKF-DF is more precise than EKF.

A New Method of Seamless Navigation for the INS/WSN-Integrated System

This work presents a new method using Kalman filter (KF) and Least Squares Support Vector Machine (LS-SVM) for the inertial navigation systems (INS)/wireless sensors network (WSN) integrated navigation. In this mode, when the ultrasonic-based WSN is working well, LS-SVM is trained for the mapping between the position measured by INS and the corresponding error. Once the ultrasonic-based WSN is outage, the LS-SVM is used to predict the error of position, which is the unavailable measurement vector of the integrated filter when the ultrasonic-based WSN is outage. Thus, the filter in this mode is able to work where there is no data from the ultrasonic devices. The results show that the proposed method is able to provide continuous navigation information when the data of indoor positioning system is outage, and it is effective to reduce the probability of the estimating outliers.

Optical Flow-Based Monocular Vision/INS Integrated Navigation for Mobile Robot Indoors

This paper proposed a new algorithm for optical flow-based monocular vision (MV)/ inertial navigation system (INS) integrated navigation. In this mode, a downward-looking camera is used to get the image sequences, which is used to estimate the velocity of the mobile robot by using optical flow algorithm. INS is employed for the yaw variation. In order to evaluate the performance of the proposed method, a real indoor test has done. The result shows that the proposed method has good performance for velocity estimation. It can be applied to the autonomous navigation of mobile robots when the Global Positioning System (GPS) and code wheel is unavailable.

The Influence Factor Analysis on Stability of Communication Links Based on Cluster-Tree WSN Using ZigBee

Zigbee technology which targets low data rate, low power consumption and low cost application is suitable for Wireless Sensor Network (WSN) as its wireless communication technology. In the construction of WSN using Zigbee, the factors which could affect the stability of communication links should be considered. In this work, we discuss the construction environment of WSN. Then, in order to access the performance of Cluster-Tree network, we develop an NS2 simulator for Cluster-Tree WSN, and analysis the influence to stability of communication links caused by sending packet ratio and communication range. The simulation results show that it is not useful to improve the stability of communication links by increasing sending packet ratio and communication range simply. Both of them should be in a relatively balanced state.