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Lu Y.,Key Laboratory of Micro Inertial Instrument and Advanced Navigation Technology | Cheng X.,Nanjing Southeast University
Measurement: Journal of the International Measurement Confederation | Year: 2014

Shipborne aircrafts normally do not have regular position on the carrier. This would lead to large misalignment between the master strapdown inertial navigation system (M-SINS) and slave strapdown inertial navigation system (S-SINS) as well as random lever arm vector. It is critical for the accuracy of the transfer alignment. The large attitude error will make the linear alignment algorithm invalid. And the lever arm vector caused by the location difference will lead to the lever arm effect which is sensed by the accelerometers in the S-SINS. Therefore it is necessary for the shipborne aircraft to estimate the lever arm vector and misalignment before the transfer alignment takes place. In this paper, a new misalignment and lever arm vector online estimation method based on gyroscope, accelerometer measurement and filtering is presented. Sensor measurements of M-SINS and S-SINS will be recorded for a few seconds. Misalignment and the lever arm vector will be calculated from these measurements directly. The values will be filtered according to the chosen threshold of the error gain. Then a second stage estimation based on least square estimation will be applied to acquire a better result. Simulation results demonstrate the effectiveness of the estimation algorithm in the situation when both large misalignment and random lever arm vector exist. © 2013 Elsevier Ltd. All rights reserved. Source


Zhang T.,Nanjing Southeast University | Xu X.,Key Laboratory of Micro Inertial Instrument and Advanced Navigation Technology | Xu S.,Key Laboratory of Micro Inertial Instrument and Advanced Navigation Technology
Measurement: Journal of the International Measurement Confederation | Year: 2015

A high-precision underwater digital elevation model (DEM) is the premise of terrain-aided navigation. This study introduces the establishment of such a model. The process includes the construction and optimization of a triangular irregular network, selection of interpolation data based on terrain roughness degree, kriging interpolation, and error corrections. Results show that the established interpolation data selection method based on terrain roughness degree is better than other traditional interpolation methods in terms of accuracy. The proposed kriging interpolation method and error corrections exhibit excellent performance. © 2014 Elsevier Ltd. All rights reserved. Source


Liu X.,Nanjing Southeast University | Xu X.,Key Laboratory of Micro Inertial Instrument and Advanced Navigation Technology | Zhao Y.,Key Laboratory of Micro Inertial Instrument and Advanced Navigation Technology | Wang L.,Key Laboratory of Micro Inertial Instrument and Advanced Navigation Technology | Liu Y.,Key Laboratory of Micro Inertial Instrument and Advanced Navigation Technology
Measurement: Journal of the International Measurement Confederation | Year: 2014

Inspired by the alignment mechanism of Octans, an initial alignment method for strapdown gyrocompass is designed based on gravitational apparent motion in inertial frame. In this method, the gyro outputs are used to trace the body frame and the measurement from accelerometers are projected to the inertial frame to form the track of gravitational apparent motion. According to the calculations on the vectors involved in this track, the attitude matrix between the inertial frame and navigation frame can be obtained and further alignment can be finished. In order to identify the true gravitational apparent motion from the accelerometer measurement containing random noise as well as to avoid the collinear of vectors in vector operation, a reconstruction algorithm concerning parameter identification and reconstruction of apparent motion is devised and simulated. Simulation and turntable test show that the proposed alignment method can realize self-alignment in a swinging condition. The alignment accuracy is able to reach the theoretical one determined by sensor errors and no external information is needed. © 2014 Elsevier Ltd. All rights reserved. Source


Liu X.,Nanjing Southeast University | Xu X.,Key Laboratory of Micro Inertial Instrument and Advanced Navigation Technology | Liu Y.,Key Laboratory of Micro Inertial Instrument and Advanced Navigation Technology | Wang L.,Key Laboratory of Micro Inertial Instrument and Advanced Navigation Technology
Measurement: Journal of the International Measurement Confederation | Year: 2014

There are two viewpoints in the transfer alignment process given as follows: (1) the information used for matching in strapdown inertial navigation system (SINS) originates from the mathematical frame which cuts off the direct relationship between sensor measurement and misalignment angles; (2) in close correction mode, the estimated parameters from data fusion filter are used to participate in SINS navigation solution. Based on the above viewpoints, a novel transfer alignment method based on iterative calculation is designed, and in this method the alignment time is shortened and alignment accuracy is improved and the real-time property of alignment result is ensured with the following two methods: (1) during every data fusion period, sensor data and external reference navigation parameters are stored, and the backward and forward navigation solution and data fusion are executed once or several times with the help of high performance computer; (2) with the help of multi-tasking method, the operations for storing sensor data in the current fusion period, iterative calculation for the previous fusion period, and navigation solution for the current fusion period based on the stored data and real-time data are run in parallel but with different priorities. The simulation and turntable test under ship swinging condition indicate that alignment time of the proposed method is shortened compared with that of classical method when the data fusion times of two methods are equal; and alignment accuracy is improved when the alignment times are equal. © 2014 Elsevier Ltd. All rights reserved. Source


Xu Y.,Nanjing Southeast University | Xu Y.,Key Laboratory of Micro Inertial Instrument and Advanced Navigation Technology | Chen X.,Nanjing Southeast University | Chen X.,Key Laboratory of Micro Inertial Instrument and Advanced Navigation Technology | And 2 more authors.
The Scientific World Journal | Year: 2014

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. © 2014 Yuan Xu et al. Source

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