Implementation and performance evaluation of a distributed GNSS/SINS ultra-tightly integrated navigation system

Jiang, Changhui; Shuai, Chen; Liu, Yaling; Han, Xiao

doi:10.1109/i2mtc.2016.7520490

Cited by 5 publications

(14 citation statements)

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“…Inertial Navigation System (INS) is a self-contained navigation system which generates continuous navigation solutions by processing the measurements from Inertial Measurement Unit (IMU). IMU usually contains three orthogonal gyroscope and three orthogonal accelerometers, in which the gyroscope measures the angle rate and the accelerometer collects the acceleration motion [24,25,26]. When compared with GNSS, the navigation solutions updating frequency is higher, while the INS is usually 400 Hz or 200 Hz, and the updating frequency of the GNSS solutions is commonly 1–10 Hz [21,22,23,24,25].…”

Section: Introductionmentioning

confidence: 99%

“…Microelectromechanical systems (MEMS) IMU is increasingly popular recently, which is manufactured using MEMS technology. MEMS IMU has much smaller size and lower cost, and it has been widely used in mobile or handheld devices, vehicle navigation and weapons guidance [26,27,28,29,30,31,32,33,34,35]. However, compared with the highly accurate laser or fiber optic IMU, MEMS IMU usually experiences more complicated noises [26,27,28,29,30,31,32].…”

Section: Introductionmentioning

confidence: 99%

“…MEMS IMU has much smaller size and lower cost, and it has been widely used in mobile or handheld devices, vehicle navigation and weapons guidance [26,27,28,29,30,31,32,33,34,35]. However, compared with the highly accurate laser or fiber optic IMU, MEMS IMU usually experiences more complicated noises [26,27,28,29,30,31,32]. Scholars are always devoted to model the noises and compensate the divergence errors to improve the accuracy of MEMS IMU based navigation system, which can broaden the MEMS IMU applications furtherly [27,28,29,30,31,32,33,34,35].…”

Section: Introductionmentioning

confidence: 99%

“…For both statistical or AI methods, gyroscope or accelerometer raw signals are treated as time series, and a model is described or learned to compensate the errors caused by the noises. Performance of the statistical method is limited by the fixed model parameters, and the traditional AI method has restricted learning capacity which is determined by the structure and working principles [26,27,28,29,30,31,32,33,34,35,36]. Recently, Deep Learning (DL) gains a boom in various applications, which has a better learning capacity than conventional SVM or neural networks [37,38].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Performance Analysis of a Deep Simple Recurrent Unit Recurrent Neural Network (SRU-RNN) in MEMS Gyroscope De-Noising

Jiang

Chen

et al. 2018

Sensors

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Microelectromechanical System (MEMS) Inertial Measurement Unit (IMU) is popular in the community for constructing a navigation system, due to its small size and low power consumption. However, limited by the manufacturing technology, MEMS IMU experiences more complicated noises and errors. Thus, noise modeling and suppression is important for improving accuracy of the navigation system based on MEMS IMU. Motivated by this problem, in this paper, a deep learning method was introduced to MEMS gyroscope de-noising. Specifically, a recently popular Recurrent Neural Networks (RNN) variant Simple Recurrent Unit (SRU-RNN) was employed in MEMS gyroscope raw signals de-noising. A MEMS IMU MSI3200 from MT Microsystem Company was employed in the experiments for evaluating the proposed method. Following two problems were furtherly discussed and investigated: (1) the employed SRU with different training data length were compared to explore whether there was trade-off between the training data length and prediction performance; (2) Allan Variance was the most popular MEMS gyroscope analyzing method, and five basic parameters were employed to describe the performance of different grade MEMS gyroscope; among them, quantization noise, angle random walk, and bias instability were the major factors influencing the MEMS gyroscope accuracy, the compensation results of the three parameters for gyroscope were presented and compared. The results supported the following conclusions: (1) considering the computation brought from training dataset, the values of 500, 3000, and 3000 were individually sufficient for the three-axis gyroscopes to obtain a reliable and stable prediction performance; (2) among the parameters, the quantization noise, angle random walk, and bias instability performed 0.6%, 6.8%, and 12.5% improvement for X-axis gyroscope, 60.5%, 17.3%, and 34.1% improvement for Y-axis gyroscope, 11.3%, 22.7%, and 35.7% improvement for Z-axis gyroscope, and the corresponding attitude errors decreased by 19.2%, 82.1%, and 69.4%. The results surely demonstrated the effectiveness of the employed SRU in this application.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance Analysis of a Deep Simple Recurrent Unit Recurrent Neural Network (SRU-RNN) in MEMS Gyroscope De-Noising

Jiang

Chen

et al. 2018

Sensors

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“…Dear editor, Owing to little exploration experience and great technical difficulty of deep space exploration autonomous navigation, the corresponding navigation performance evaluation system is seldom perfected [1]. Therefore, the establishment of an effective method to evaluate the navigation systems for deep space exploration has become a hot topic in the aerospace field [2,3]. Navigation particle filtering is a key to achieve a high accuracy autonomous navigation [4].…”

mentioning

confidence: 99%

Development of a navigation performance evaluation system

Zhao

Wei

2020

Sci. China Inf. Sci.

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A gradient descend method‐based none‐line‐of‐sight‐induced code delay searching in global navigation satellite system

Liu

Jiang

Zhu

2021

IET Radar Sonar & Navi

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In urban areas with narrow streets and tall buildings, none-line-of-sight (NLOS) or multipath (MP) signals severely attenuate the positioning accuracy of the global navigation satellite system (GNSS). vector tracking loop (VT) has been employed in NLOS/MP signal detection and correction; its feasibility and superior performance have been assessed and demonstrated in previous research studies. In VT, the navigation solutions are fed back to calculate the signal tacking parameters and complete the carrier and code tracking. The NLOS/MP reception induces code delay to the code tracking. The generated local signal replicas are mixed with the received signal; the code and carrier discriminators' outputs are employed as the measurements to estimate the navigation solutions in the VT navigation filter. In order to extract the code delay induced by the NLOS or MP, multiple correlators (MCs) are usually utilised to search for the code delay. However, more correlators will bring much heavier computation load and a real position is necessary for calculating the signal tracking parameters. The strap-down inertial navigation system (SINS) can generate short-term accurate navigation solutions. In this work, a gradient descend (GD) method is designed for extracting the NLOS/MP-induced delay in the correlators' measurements level. The VT/SINS ultra-tightly coupled navigation system (VT-UTC) is developed to obtain mutual aiding between these individual systems. In VT-UTC, the corrected SINS navigation solutions are employed for calculating the signal tracking parameters, and reliable navigation solutions from the SINS can help to extract the code delays induced by the NLOS. Two field tests with global positioning system L1 signal are carried out for assessing the performance of the GD method in the VT-UTC framework. Comparisons between the MC and GD method show that the correlators' computation reduces a lot since much fewer correlators are calculated in the code delay searching. Also, reliable navigation solutions from the SINS could help to calculate the signal tracking parameter while in-view GNSS satellites are limited.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Implementation and performance evaluation of a distributed GNSS/SINS ultra-tightly integrated navigation system

Cited by 5 publications

References 4 publications

Performance Analysis of a Deep Simple Recurrent Unit Recurrent Neural Network (SRU-RNN) in MEMS Gyroscope De-Noising

Performance Analysis of a Deep Simple Recurrent Unit Recurrent Neural Network (SRU-RNN) in MEMS Gyroscope De-Noising

Development of a navigation performance evaluation system

A gradient descend method‐based none‐line‐of‐sight‐induced code delay searching in global navigation satellite system

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