Observability analysis of Mars entry integrated navigation

Wang, Liansheng; Xia, Yuanqing

doi:10.1016/j.asr.2015.05.016

Cited by 19 publications

(8 citation statements)

References 27 publications

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“…Because the observability and observable degree directly determine the performance of the navigation algorithm, observability analysis is an important work for the integrated navigation. The observability of both IMU/surface radio beacons and IMU/orbiting radio beacons integrated navigation had been discussed in the previous work [18][19][20][21][22]. The aim of this paper is to develop a new integrated navigation algorithm for Mars atmospheric entry.…”

Section: Observability Analysismentioning

confidence: 97%

“…Previous studies show that the accuracy of radio/IMU integrated navigation is significantly better than that of traditional inertial navigation when both the configuration of multiple beacons and observability meet certain conditions. Unfortunately, the accuracy of radio/IMU integrated navigation is hardly upgraded when only one radio beacon is available [20][21][22]. Hence, the alone radio/IMU integrated navigation only using one radio beacon is not a feasible solution to support high-precision entry guidance of the first Mars orbiting/landing collaborative exploration mission.…”

Section: Introductionmentioning

confidence: 99%

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Radio/FADS/IMU integrated navigation for Mars entry

Jiang

Huang³

2018

Advances in Space Research

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Supposing future China's orbiting and landing collaborative exploration mission as the potential project background, this paper addresses the issue of Mars entry integrated navigation using radio beacon, flush air data sensing system (FADS), and inertial measurement unit (IMU). The range and Doppler information sensed from an orbiting radio beacon, the dynamic pressure and heating data sensed from flush air data sensing system, and acceleration and attitude angular rate outputs from an inertial measurement unit are integrated in an unscented kalman filter to perform state estimation and suppress the system and measurement noise. Computer simulations show that the proposed integrated navigation scheme can enhance the navigation accuracy, which enables precise entry guidance for the given Mars orbiting and landing collaborative exploration mission.

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Section: Observability Analysismentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Radio/FADS/IMU integrated navigation for Mars entry

Jiang

Huang³

2018

Advances in Space Research

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“…Brown is the first to open the issue of ‘Not just observable, but how observable?’ . After that, Ham and Brown present a method termed as the ‘stochastic approach’ to determine the DoO based on Kalman filtering (KF) theory . They state that the eigenvalues and eigenvectors of the estimation error covariance matrix, when properly normalized, can provide additional insight into the DoO of a system.…”

Section: Introductionmentioning

confidence: 99%

“…They state that the eigenvalues and eigenvectors of the estimation error covariance matrix, when properly normalized, can provide additional insight into the DoO of a system. Based on Ham's stochastic approach of determining the DoO, Wang and Xia make an observability analysis of Mars entry integrated navigation . In addition, Cui, Yu, et al chose the condition number of the observability matrix to quantify the observability and present a condition‐number‐based observability analysis method to optimize the beacon configuration for Mars entry navigation .…”

Section: Introductionmentioning

confidence: 99%

“…Rothman et al investigate the observability and estimability of a navigation system; however, they do not study the individual estimability of each error state . In addition, the eigenvalues and eigenvectors of the estimation error covariance matrix as well as the condition number do not enable us to properly evaluate how well estimated each navigation error‐state is during the whole navigation process . What is more, the existing methods mentioned earlier require calculating the eigenvalues and eigenvectors of the error covariance matrix or the maximum and minimum singular values of the observability matrix, which usually leads to a great amount of computation when the state transition matrix of the system has a high order.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying Observability and Analysis in Integrated Navigation

et al. 2018

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This paper concerns the problem of quantifying the observability of different states in linear (or linearized) time-varying systems with focus on the usability to integrated navigation. The main goal is the derivation of a novel scalar method to measure the degree of observability in order to quantitatively analyze the observability of each state variable of a system. The new proposed observability analysis method is easily employed in practice with less computer memory and clearer physical meanings. The application to integrated navigation systems was illustrated to analyze the observability properties of navigation error-states. According to the simulation results, the degree of observability can help us to quantitatively compare the observability of each state variable and further function as an indicator to analyze the influence of physical model parameters on the observability and estimability of navigation errors.

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Mars entry navigation under biases based on adaptive Huber divided difference filter

Xia

Zhu

Cui

et al. 2022

Adaptive Control & Signal

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Summary To address the problems of unknown disturbances and measurement outliers at Mars entry phase, three integrated navigation schemes are proposed in this study by complying with divided difference filters, that is, integrated navigation schemes based on the Huber first‐order divided difference filter (HDDF1), adaptive Huber first‐order divided difference filter (AHDDF1), and adaptive Huber second‐order divided difference filter (AHDDF2). The novel versions of predictive state estimation error covariance (PSEEC) and measurement noise covariance (MNC) are deduced by adopting the ρ function of the Huber case to modify the cost function of the standard Kalman filter. To be specific, the HDDF1 is derived by embedding the novel PSEEC and MNC in the general first‐order divided difference filter. To more specifically enhance the performance of the filter under significant biases, an adaptive forgetting factor is introduced in the HDDF1. On that basis, the AHDDF1 is derived. Likewise, the AHDDF2 is yielded. The numerical simulations are presented to verify the effectiveness of the proposed navigation schemes.

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Observability analysis of Mars entry integrated navigation

Cited by 19 publications

References 27 publications

Radio/FADS/IMU integrated navigation for Mars entry

Radio/FADS/IMU integrated navigation for Mars entry

Quantifying Observability and Analysis in Integrated Navigation

Mars entry navigation under biases based on adaptive Huber divided difference filter

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