Performance Characterization of Tightly-Coupled GNSS Precise Point Positioning Inertial Navigation within a Simulation Environment

Watson, Ryan M.; Sivaneri, Victor O.; Gross, Jason N.

doi:10.2514/6.2016-1869

Cited by 18 publications

(24 citation statements)

References 7 publications

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“…That is, the antennas were placed according to the following matrix: where x i,b , y i,b , and z i,b are the body-centric coordinates of the ith antenna, i = 1, denoting the master antenna. GNSS carrier-phase data was simulated for each flight profile at a rate of 10 Hz using the MATLAB SatNav toolbox [36], which was modified by Watson et al [37] to include additional GNSS error sources. A number of deterministic and nondeterministic error sources are associated with GNSS measurements [21].…”

Section: Gnss Observable Simulationmentioning

confidence: 99%

Performance Trades for Multiantenna GNSS Multisensor Attitude Determination Systems

Tehrani

Gross

2018

International Journal of Aerospace Engineering

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We present various performance trades for multiantenna global navigation satellite system (GNSS) multisensor attitude estimation systems. In particular, attitude estimation performance sensitivity to various error sources and system configurations is assessed. This study is motivated by the need for system designers, scientists, and engineers of airborne astronomical and remote sensing platforms to better determine which system configuration is most suitable for their specific application. In order to assess performance trade-offs, the attitude estimation performance of various approaches is tested using a simulation that is based on a stratospheric balloon platform. For GNSS errors, attention is focused on multipath, receiver measurement noise, and carrierphase breaks. For the remaining attitude sensors, different performance grades of sensors are assessed. Through a Monte Carlo simulation, it is shown that, under typical conditions, sub-0.1-degree attitude accuracy is available when using multiple antenna GNSS data only, but that this accuracy can degrade to degree level in some environments warranting the inclusion of additional attitude sensors to maintain the desired level of accuracy. Further, we show that integrating inertial sensors is more valuable whenever accurate pitch and roll estimates are critical.

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Section: Gnss Observable Simulationmentioning

confidence: 99%

Performance Trades for Multiantenna GNSS Multisensor Attitude Determination Systems

Tehrani

Gross

2018

International Journal of Aerospace Engineering

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“…The EKF works by linearizing the non-linear system about it's nominal trajectory (i.e., taking a first order Taylor series expansion of the systems about the trajectory [6]). It should be noted that other non-linear filter techniques have been proposed (i.e., the unscented Kalman filter [32], or the particle filter [33]); however, in practice, the EKF seems to be the most widely utilized filter technique, with applications spanning domains from aerospace navigation [34] to ecological modelling [35].…”

Section: The Filtering Paradigmmentioning

confidence: 99%

“…4.4.1. To tailor the toolbox for an evaluation of kinematic PPP airborne positioning, several minor modifications were made, as discussed in [34]. For example, toolbox was modified to include attitude dependent satellite masking and carrier-phase breaks.…”

Section: Evaluation On Simulated Datamentioning

confidence: 99%

“…This error model is constructed by differencing JPL's International GNSS Service (IGS) submission with European Center for Orbit Determination (CODE) submission. For a more detailed discussion on the simulation environment, the reader is directed to [34].…”

Section: Evaluation On Simulated Datamentioning

confidence: 99%

“…Specifically, one hundred datasets were generated where several parameters, which are known to adversely affect GNSS positioning performance, were ran- domly initialized for each flight -see Table 4.4.1 for additional information on varied parameters. To provide a reference positioning solution, a traditional extended Kalman filter (EKF) was utilized, where the specific EKF formulation details are provided in [34]. To provide a fair comparison, the same stochastic models were implemented for both estimators.…”

Section: Evaluation On Simulated Datamentioning

confidence: 99%

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Enabling Robust State Estimation through Covariance Adaptation

Watson¹

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Several robust state estimation frameworks have been proposed over the previous decades. Underpinning all of these robust frameworks is one dubious assumption. Specifically, the assumption that an accurate a priori measurement uncertainty model can be provided. As systems become more autonomous, this assumption becomes less valid (i.e., as systems start operating in novel environments, there is no guarantee that the assumed a priori measurement uncertainty model characterizes the sensors current observation uncertainty).In an attempt to relax this assumption, a novel robust state estimation framework is proposed. The proposed framework enables robust state estimation through the iterative adaptation of the measurement uncertainty model. The adaptation of the measurement uncertainty model is granted through non-parametric clustering of the estimator' s residuals, which enables the characterization of the measurement uncertainty via a Gaussian mixture model. This Gaussian mixture model based measurement uncertainty characterization can be incorporated into any non-linear least square optimization routine.Within this dissertation, the proposed framework is instantiated into three novel robust state estimation algorithms: batch covariance estimation (BCE), batch covariance estimation over an augmented data space (BCE-AD), and incremental covariance estimation (ICE). To verify the proposed framework, three global navigation satellite system (GNSS) data sets were collected. The collected data sets provide varying levels of observation degradation to enable the characterization of the proposed algorithm on a diverse data set. Utilizing these data sets, it is shown that the proposed framework exhibits improved state estimation accuracy when compared to other robust estimation techniques when confronted with degraded data quality.

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Tightly coupled integrated inertial and real-time-kinematic positioning approach using nonlinear observer

Hansen

Johansen

Fossen

2016

2016 American Control Conference (ACC)

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Performance Characterization of Tightly-Coupled GNSS Precise Point Positioning Inertial Navigation within a Simulation Environment

Cited by 18 publications

References 7 publications

Performance Trades for Multiantenna GNSS Multisensor Attitude Determination Systems

Performance Trades for Multiantenna GNSS Multisensor Attitude Determination Systems

Enabling Robust State Estimation through Covariance Adaptation

Tightly coupled integrated inertial and real-time-kinematic positioning approach using nonlinear observer

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