Observability of satellite launcher navigation with INS, GPS, attitude sensors and reference trajectory

Beaudoin, Y.; Desbiens, André; Gagnon, Éric; Landry, René

doi:10.1016/j.actaastro.2017.10.038

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…There is still correlation accumulation, and, at the end of the mission, the inter-INS correlation for the attitude estimation error remains high ( Figure 17 ). However, the attitude reference sensor measurements provide the attitude observability required to prevent navigation filter divergence [ 46 ].…”

Section: Results Analysismentioning

confidence: 99%

Satellite Launcher Navigation with One Versus Three IMUs: Sensor Positioning and Data Fusion Model Analysis

Beaudoin

Desbiens

Gagnon

et al. 2018

Sensors

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Using multiple IMUs allows both their distribution along vehicle structures and a reliance on integration methods, which is not possible with a single IMU. This paper addresses the issue of relying on three IMUs instead of only one of a higher quality in the context of a satellite launcher. The impact of the IMU positions was tested by comparing collocated IMUs against IMUs installed in the head of each launcher stage. For multi-IMU configurations, three integration methods were tested: all IMUs fused in a single INS, multiple INSs fused in a stacked filter, and multiple INSs fused in a stacked filter with geometrical constraints. All navigation solutions were aided by a three-axis attitude reference sensor and were tested with and without a GPS receiver. The results show that distributing IMUs along the launcher structure does not improve navigation performances compared to having them collocated. The fusion of multiple IMUs in one INS provides equivalent results as one IMU. However, fusing multiple INSs greatly reduces estimation errors. Performances are further improved with the addition of geometrical constraints. During long GPS outages, relative velocity and position constraints should not be exploited, as they may lead to filter divergence.

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Section: Results Analysismentioning

confidence: 99%

Satellite Launcher Navigation with One Versus Three IMUs: Sensor Positioning and Data Fusion Model Analysis

Beaudoin

Desbiens

Gagnon

et al. 2018

Sensors

Self Cite

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“…where where u sn is the rotation angle of {O s x s y s z s } relative to {O n x n y n z n }, which can be calculated according to equation (2) x sen = e gz e ax e ay ½ T According to equation (26), matrix A sen can be determined as follows…”

Section: Rins Error Propagationmentioning

confidence: 99%

“…According to Beaudoin et al, 26 the GNSS error model can be described using a first-order Markov process…”

Section: Gnss Error Modelmentioning

confidence: 99%

An improved integrated navigation method based on RINS, GNSS and kinematics for port heavy-duty AGV

Feng

Duan³

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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The current navigation methods for port heavy-duty automated guided vehicle mainly include the antenna radar-transponder navigation and the global navigation satellite system. However, the former has a huge cost and the latter will generate multi-path error easily. To avoid these problems, an improved integrated navigation method based on single-axis rotating inertial navigation system, global navigation satellite system and kinematics is proposed. First, the rotating inertial navigation system/ global navigation satellite system and rotating inertial navigation system/Kinematics integrated navigation methods generate corresponding estimates and filtering error covariances through their respective extended Kalman filter filters, and then the two sets of results are fused by the optimal weighted voting fusion method. The proposed method is applied to a heavy-duty automated guided vehicle for engineering verification. Without multi-path error, the navigation accuracy is 1.8–2.98 times higher than that of the traditional global navigation satellite system navigation. In the case of multi-path error, the improved method still has high fault tolerance and high navigation accuracy. The accuracy of this method satisfies the requirements of port heavy-duty automated guided vehicle, which can greatly reduce the number of transponders and has high practical value.

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“…To obtain the reliable and precise information, multisensor systems have become more and more popular in a wide range of applications such as cooperative tracking, autonomous navigation, signal processing, and guidance [1,2]. Therefore, how to best fuse the observations from different sensors has gained much attention in several decades.…”

Section: Introductionmentioning

confidence: 99%

A Novel Robust Student’s t-Based Cubature Information Filter with Heavy-Tailed Noises

Shui

Wang

Qin

et al. 2020

International Journal of Aerospace Engineering

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In this paper, a novel robust Student’s t-based cubature information filter is proposed for a nonlinear multisensor system with heavy-tailed process and measurement noises. At first, the predictive probability density function (PDF) and the likelihood PDF are approximated as two different Student’s t distributions. To avoid the process uncertainty induced by the heavy-tailed process noise, the scale matrix of the predictive PDF is modeled as an inverse Wishart distribution and estimated dynamically. Then, the predictive PDF and the likelihood PDF are transformed into a hierarchical Gaussian form to obtain the approximate solution of posterior PDF. Based on the variational Bayesian approximation method, the posterior PDF is approximated iteratively by minimizing the Kullback-Leibler divergence function. Based on the posterior PDF of the auxiliary parameters, the predicted covariance and measurement noise covariance are modified. And then the information matrix and information state are updated by summing the local information contributions, which are computed based on the modified covariance. Finally, the state, scale matrix, and posterior densities are estimated after fixed point iterations. And the simulation results for a target tracking example demonstrate the superiority of the proposed filter.

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Observability of satellite launcher navigation with INS, GPS, attitude sensors and reference trajectory

Cited by 11 publications

References 31 publications

Satellite Launcher Navigation with One Versus Three IMUs: Sensor Positioning and Data Fusion Model Analysis

Satellite Launcher Navigation with One Versus Three IMUs: Sensor Positioning and Data Fusion Model Analysis

An improved integrated navigation method based on RINS, GNSS and kinematics for port heavy-duty AGV

A Novel Robust Student’s t-Based Cubature Information Filter with Heavy-Tailed Noises

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