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18 pages, 5093 KiB

Open AccessArticle

GAM-Based Mooring Alignment for SINS Based on An Improved CEEMD Denoising Method

by Hanxiao Rong, Yanbin Gao, Lianwu Guan, Qing Zhang, Fan Zhang and Ningbo Li

Sensors 2019, 19(16), 3564; https://doi.org/10.3390/s19163564 - 15 Aug 2019

Cited by 14 | Viewed by 3450

To solve the self-alignment problem of the Strapdown Inertial Navigation System (SINS), a novel adaptive filter based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) is proposed. The Gravitational Apparent Motion (GAM) is used in the coarse alignment, and the problem of obtaining the attitude matrix between the body frame and the navigation frame is attributed to obtaining the matrix between the initial body frame and the current navigation frame using two gravitational apparent motion vectors at different moments. However, the accuracy and time of this alignment method always suffer from the measurement noise of sensors. Thus, a novel adaptive filter based on CEEMD using an

l_{2}

-norm to calculate the similarity measure between the Probability Density Function (PDF) of each Intrinsic Mode Function (IMF) and the original signal is proposed to denoise the measurements of the accelerometer. Furthermore, the advantage of this filter is verified by comparing with other conventional denoising methods, such as PDF-based EMD (EMD-PDF) and the Finite Impulse Response (FIR) digital low-pass filter method. The results of the simulation and experiments indicate that the proposed method performs better than the conventional methods in both alignment time and alignment accuracy. Full article

(This article belongs to the Special Issue Inertial Sensors)

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25 pages, 10384 KiB

Open AccessArticle

A Coarse Alignment Method Based on Digital Filters and Reconstructed Observation Vectors

by Xiang Xu, Xiaosu Xu, Tao Zhang, Yao Li and Zhicheng Wang

Sensors 2017, 17(4), 709; https://doi.org/10.3390/s17040709 - 29 Mar 2017

Cited by 19 | Viewed by 4777

Abstract

In this paper, a coarse alignment method based on apparent gravitational motion is proposed. Due to the interference of the complex situations, the true observation vectors, which are calculated by the apparent gravity, are contaminated. The sources of the interference are analyzed in detail, and then a low-pass digital filter is designed in this paper for eliminating the high-frequency noise of the measurement observation vectors. To extract the effective observation vectors from the inertial sensors’ outputs, a parameter recognition and vector reconstruction method are designed, where an adaptive Kalman filter is employed to estimate the unknown parameters. Furthermore, a robust filter, which is based on Huber’s M-estimation theory, is developed for addressing the outliers of the measurement observation vectors due to the maneuver of the vehicle. A comprehensive experiment, which contains a simulation test and physical test, is designed to verify the performance of the proposed method, and the results show that the proposed method is equivalent to the popular apparent velocity method in swaying mode, but it is superior to the current methods while in moving mode when the strapdown inertial navigation system (SINS) is under entirely self-contained conditions. Full article

(This article belongs to the Special Issue Inertial Sensors and Systems 2016)

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19 pages, 9574 KiB

Open AccessArticle

A Kalman Filter for SINS Self-Alignment Based on Vector Observation

by Xiang Xu, Xiaosu Xu, Tao Zhang, Yao Li and Jinwu Tong

Sensors 2017, 17(2), 264; https://doi.org/10.3390/s17020264 - 29 Jan 2017

Cited by 44 | Viewed by 7383

Abstract

In this paper, a self-alignment method for strapdown inertial navigation systems based on the q-method is studied. In addition, an improved method based on integrating gravitational apparent motion to form apparent velocity is designed, which can reduce the random noises of the observation vectors. For further analysis, a novel self-alignment method using a Kalman filter based on adaptive filter technology is proposed, which transforms the self-alignment procedure into an attitude estimation using the observation vectors. In the proposed method, a linear psuedo-measurement equation is adopted by employing the transfer method between the quaternion and the observation vectors. Analysis and simulation indicate that the accuracy of the self-alignment is improved. Meanwhile, to improve the convergence rate of the proposed method, a new method based on parameter recognition and a reconstruction algorithm for apparent gravitation is devised, which can reduce the influence of the random noises of the observation vectors. Simulations and turntable tests are carried out, and the results indicate that the proposed method can acquire sound alignment results with lower standard variances, and can obtain higher alignment accuracy and a faster convergence rate. Full article

(This article belongs to the Section Physical Sensors)

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10 pages, 3031 KiB

Open AccessArticle

Observations of the Structure and Dynamics of the Inner M87 Jet

by R. Craig Walker, Philip E. Hardee, Fred Davies, Chun Ly, William Junor, Florent Mertens and Andrei Lobanov

Galaxies 2016, 4(4), 46; https://doi.org/10.3390/galaxies4040046 - 18 Oct 2016

Cited by 23 | Viewed by 5306

Abstract

M87 is the best source in which to study a jet at high resolution in gravitational units because it has a very high mass black hole and is nearby. The angular size of the black hole is second only to Sgr A*, which does not have a strong jet. The jet structure is edge brightened with a wide opening angle base and a weak counterjet. We have roughly annual observations for 17 years plus intensive monitoring at three week intervals for a year and five day intervals for 2.5 months made with the Very Long Baseline Array (VLBA) at 43 GHz. The inner jet shows very complex dynamics, with apparent motions both along and across the jet. Speeds from zero to over 2c are seen, with acceleration observed over the first 3 milli-arcseconds. The counterjet decreases in brightness much more rapidly than the main jet, as is expected from relativistic beaming in an accelerating jet oriented near the line-of-sight. Details of the structure and dynamics are discussed. The roughly annual observations show side-to-side motion of the whole jet with a characteristic time scale of about 9 years. Full article

(This article belongs to the Special Issue Blazars through Sharp Multi-wavelength Eyes)

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27 pages, 1274 KiB

Open AccessArticle

A Self-Alignment Algorithm for SINS Based on Gravitational Apparent Motion and Sensor Data Denoising

by Yiting Liu, Xiaosu Xu, Xixiang Liu, Yiqing Yao, Liang Wu and Jin Sun

Sensors 2015, 15(5), 9827-9853; https://doi.org/10.3390/s150509827 - 27 Apr 2015

Cited by 24 | Viewed by 6790

Abstract

Initial alignment is always a key topic and difficult to achieve in an inertial navigation system (INS). In this paper a novel self-initial alignment algorithm is proposed using gravitational apparent motion vectors at three different moments and vector-operation. Simulation and analysis showed that this method easily suffers from the random noise contained in accelerometer measurements which are used to construct apparent motion directly. Aiming to resolve this problem, an online sensor data denoising method based on a Kalman filter is proposed and a novel reconstruction method for apparent motion is designed to avoid the collinearity among vectors participating in the alignment solution. Simulation, turntable tests and vehicle tests indicate that the proposed alignment algorithm can fulfill initial alignment of strapdown INS (SINS) under both static and swinging conditions. The accuracy can either reach or approach the theoretical values determined by sensor precision under static or swinging conditions. Full article

(This article belongs to the Special Issue Inertial Sensors and Systems)

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