Vision-based relative navigation for formation flying of spacecraft

Alonso, Roberto; Crassidis, John L.; Junkins, John L.

doi:10.2514/6.2000-4439

Cited by 67 publications

(36 citation statements)

References 4 publications

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“…Alonso et al (2000) proposed a method utilizing multiple line of sight (LOS) vectors to estimate the relative position and velocity between multiple satellites in formation. Bever et al (2002) compared the characteristics of three relative navigation approaches; independent separation measurement system, formation needles, and formation flight instrumentation system.…”

Section: Introductionmentioning

confidence: 99%

Adaptive GPS/INS integration for relative navigation

et al. 2015

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Relative navigation based on GPS receivers and inertial measurement units is required in many applications including formation flying, collision avoidance, cooperative positioning, and accident monitoring. Since sensors are mounted on different vehicles which are moving independently, sensor errors are more variable in relative navigation than in single-vehicle navigation due to different vehicle dynamics and signal environments. In order to improve the robustness against sensor error variability in relative navigation, we present an efficient adaptive GPS/INS integration method. In the proposed method, the covariances of GPS and inertial measurements are estimated separately by the innovations of two fundamentally different filters. One is the position-domain carrier-smoothed-code filter and the other is the velocity-aided Kalman filter. By the proposed two-filter adaptive estimation method, the covariance estimation of the two sensors can be isolated effectively since each filter estimates its own measurement noise. Simulation and experimental results demonstrate that the proposed method improves relative navigation accuracy by appropriate noise covariance estimation.

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Section: Introductionmentioning

confidence: 99%

Adaptive GPS/INS integration for relative navigation

et al. 2015

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“…In general, the known beacon locations are defined in the master's body frame, whereas the relative position vector between the master and slave spacecraft is expressed in its LVLH frame. In Alonso et al (2000), Kim et al (2007) and Xing et al (2010), a simplified assumption that the master body frame coincides with its LVLH frame is made to construct the LOS observations for convenience. Unfortunately, this assumption is not valid under all situations, or in some rigorous senses, it is only a special case.…”

Section: Introductionmentioning

confidence: 99%

Unscented Kalman Filtering for Relative Spacecraft Attitude and Position Estimation

Zhang

Yang

et al. 2014

J. Navigation

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A novel relative spacecraft attitude and position estimation approach based on an Unscented Kalman Filter (UKF) is derived. The integrated sensor suite comprises the gyro sensors on each spacecraft and a vision-based navigation system on the slave spacecraft. In the traditional algorithm, an assumption that the master's body frame coincides with its Local Vertical Local Horizontal (LVLH) frame is made to construct the line-of-sight observations for convenience. To solve this problem, two relative quaternions that map the master's LVLH frame to the slave and master body frames are involved. The general relative equations of motion for eccentric orbits are used to describe the positional dynamics. The implementation equations for the UKF are derived. A modified version of the UKF is presented based on the averaging-quaternion algorithm. Simulation results indicate that the proposed filters provide more accurate estimates of relative attitude and position than the Extended Kalman Filter (EKF).2. Relative attitude and position. 3. Eccentric orbit. 4. Quaternion.

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“…Further details on analog signal processing are provided in Ref. 10. These currents, which are in numeric form, are fed to the DSP processor for the six-degree-of-freedom solution using the algorithm described in the following discussion.…”

Section: Estimation Of Relative Positionsmentioning

confidence: 99%

“…The solution consists of six-degree-of-freedom sensor position and attitude data with respect to the target frame A and is provided at an update rate of 100 Hz (Refs. [10][11][12]. For the aerial refueling application, Fig.…”

Section: Vision-based Navigation Sensor and Systemmentioning

confidence: 99%

Vision-Based Sensor and Navigation System for Autonomous Air Refueling

Valasek

Gunnam

Kimmett

et al. 2005

Journal of Guidance, Control, and Dynamics

162

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Autonomous in-flight aerial refueling is an important capability for the future deployment of unmanned aerial vehicles, because they will likely be ferried in flight to overseas theaters of operation instead of being shipped unassembled in containers. A reliable sensor, capable of providing accurate relative position measurements of sufficient bandwidth, is key to such a capability. A vision-based sensor and navigation system is introduced that enables precise and reliable probe-and-drogue autonomous aerial refueling for non-micro-sized unmanned aerial vehicles. A performance robust controller is developed and integrated with the sensor system, and feasibility of the total system is demonstrated by simulated docking maneuvers with both a stationary drogue and a drogue subjected to light turbulence. An unmanned air vehicle model is used for controller design and simulation. Results indicate that the integrated sensor and controller enables precise aerial refueling, including consideration of realistic measurement errors and disturbances.

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Vision-based relative navigation for formation flying of spacecraft

Cited by 67 publications

References 4 publications

Adaptive GPS/INS integration for relative navigation

Adaptive GPS/INS integration for relative navigation

Unscented Kalman Filtering for Relative Spacecraft Attitude and Position Estimation

Vision-Based Sensor and Navigation System for Autonomous Air Refueling

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