State Estimation Of Spacecraft Research Articles

Autonomous Optical-Only Spacecraft-to-Spacecraft Absolute Tracking and Maneuver Classification in Cislunar Space

Traditional concerns regarding spacecraft state estimation are exacerbated in cislunar space due to highly nonlinear dynamics, limited observational information, and increasingly overloaded Earth-based resources. These challenges motivate research into space-based navigation and observation capabilities that provide opportunities for autonomy. This paper demonstrates that relative optical measurements provide full inertial estimates of an agent and target spacecraft, while simultaneously enabling classification of unknown maneuvers in the cislunar environment. To achieve this objective two subtasks are demonstrated. First, the spacecraft-to-spacecraft absolute tracking method, which estimates the absolute state of two vehicles using relative measurements, is tested for observability and shown to produce consistent filter solutions given optical sensors. Second, maneuver classification is performed by labeling an estimated control profile generated from the optimal control based estimator. Collectively, this sequential methodology enables an agent craft to autonomously navigate itself while observing a target craft and classifying its maneuvers.

Compensation Filtering for Spacecraft Attitude Estimation Using Error-Covariance Reconstruction

Traditional spacecraft attitude estimation algorithms normally require the assumption that the true quaternion approximates to the estimated value. Otherwise, it may lead to a large truncation error. However, this assumption is difficult to be always satisfied during practical applications. To alleviate it, this article proposes a new scheme to reconstruct the error covariance matrix to minimize this truncation error. Specifically, the recursive formulas of measurement updates used in traditional algorithms are recalculated with the quaternion derived from the attitude update, which can reduce the influence caused by the initial conditional errors and inaccurate attitude estimations. This new scheme is then applied to the quaternion attitude estimators. It is shown that the error covariances of the proposed algorithms are theoretically bounded. Simulations for a low-earth-orbit spacecraft state estimation are performed. Through updating the estimated quaternion and then reconstructing the error covariance, the comparison results show that the proposed algorithms thus reduce the introduction of truncation error and thus achieve better performance than traditional algorithms in terms of robustness and convergence rate.

Centered Error Entropy-Based Sigma-Point Kalman Filter for Spacecraft State Estimation with Non-Gaussian Noise

The classical sigma-point Kalman filter (SPKF) is widely used in a spacecraft state estimation area with the Gaussian white noise hypothesis. The actual sensor noise is often disturbed by outliers in the harsh space environment, and the SPKF algorithm will reduce the filtering accuracy or even diverge. In this study, to enhance the robustness under non-Gaussian noise condition, the outlier-robust SPKF algorithm based on a centered error entropy (CEE) criterion is derived. Unscented Kalman filter (UKF) is typical of SPKF; combining the deterministic sampling criterion with the centered error entropy criterion, a robust centered error entropy UKF (CEEUKF) algorithm is proposed. The CEEUKF uses the unscented transformation (UT) method to perform time update step and then uses the robust regression model and CEE criterion to reconstruct the measurement update step. The effectiveness of the proposed CEEUKF is verified by a spacecraft attitude determination system.

Jet transport particle filter for attitude estimation of tumbling space objects

The particle filter (PF) is one of the most potential methods for nonlinear state estimation of spacecraft on account of its accuracy and stability. However, the heavy computational burden limits its application in real-time estimations. In this paper, we propose an improvement for the PF based on the Jet Transport (JT) theory, and apply it to the real-time attitude estimation of tumbling space objects. The main innovation of the Jet Transport Particle Filter (JTPF) is to employ the JT technique in the particle evolution process, rather than using the numerical integration as the classical PF, so as to reduce the computational burden of the algorithm. Furthermore, the proposed JTPF uses the multiplicative error quaternion to avoid further errors in the normalizing process, and the regularization technique to avoid the particle degeneracy. The JTPF is tested in three scenarios with different state and observation dimensions. Monte Carlo simulations demonstrate that the JTPF has a similar accuracy as the classical PF, and costs only 7% ∼ 13% of processing time as the latter. Moreover, some empirical principals are summarized about the optimal JT expansion order and particle number of the JTPF.

Coupled orbit–attitude dynamics and relative state estimation of spacecraft near small Solar System bodies

The effects of dynamical coupling between the rotational (attitude) and translational (orbital) motion of spacecraft near small Solar System bodies is investigated. This coupling arises due to the weak gravity of these bodies, as well as solar radiation pressure. The traditional approach assumes a point-mass spacecraft model to describe the translational motion of the spacecraft, while the attitude motion is considered to be completely decoupled from the translational motion. The model used here to describe the rigid-body spacecraft dynamics includes the non-uniform rotating gravity field of the small body up to second degree and order along with the attitude dependent terms, solar tide, and solar radiation pressure. This model shows that the second degree and order gravity terms due to the small body affect the dynamics of the spacecraft to the same extent as the orbit–attitude coupling due to the primary gravity (zeroth order) term. Variational integrators are used to simulate the dynamics of both the rigid spacecraft and the point mass. The small bodies considered here are modeled after Near-Earth Objects (NEO) 101955 Bennu, and 25143 Itokawa, and are assumed to be triaxial ellipsoids with uniform density. Differences in the numerically obtained trajectories of a rigid spacecraft and a point mass are then compared, to illustrate the impact of the orbit–attitude coupling on spacecraft dynamics in proximity of small bodies. Possible implications on the performance of model-based spacecraft control and on the station-keeping budget, if the orbit–attitude coupling is not accounted for in the model of the dynamics, are also discussed. An almost globally asymptotically stable motion estimation scheme based solely on visual/optical feedback that estimates the relative motion of the asteroid with respect to the spacecraft is also obtained. This estimation scheme does not require a model of the dynamics of the asteroid, which makes it perfectly suited for asteroids whose properties are not well known.

Mars Aerobraking Spacecraft State Estimation by Processing Inertial Measurement Unit Data

Aerobraking is an efficient technique for orbit adjustment of planetary spacecraft, such as Magellan (Venus), Mars Global Surveyor, and Mars Odyssey. Determination of the vehicle state during the aerobraking phase has conventionally been performed using only radiometric tracking data prior to and following the atmospheric drag pass. This approach is sufficiently accurate and timely to meet current mission operational requirements; however, it is expensive in terms of ground support and leads to delayed results because ofthe need for post-drag pass data. This research presents a new approach to estimation of the vehicle state during the atmospheric pass that sequentially incorporates observations from an Inertial Measurement Unit (IMU) and models of the vehicle and environment. The approach, called Inertial Measurements for Aerobraking Navigation (IMAN), is shown to perform at a level comparable to the conventional methods in terms of navigation accuracy and superior to them in terms of availability of the results immediately after completion ofthe pass. Furthermore, the research shows that IMAN can be used to reliably predict subsequent periapsis times and locations over all aerobraking regimes. IMAN also yields accurate peak dynamic pressure and heating rates, critical for a successful corridor control strategy, comparable to navigation team reconstructed values. This research also provides the first instance of the utilization of the Unscented Kalman Filter for the purpose of estimating an actual spacecraft trajectory arc about another planet.

High‐resolution gravity model of Venus

The anomalous gravity field of Venus shows high correlation with surface features revealed by radar. We extract gravity models from the Doppler tracking data from the Pioneer Venus Orbiter (PVO) by means of a two‐step process. In the first step, we solve the nonlinear spacecraft state estimation problem using a Kalman filter‐smoother. The Kalman filter has been evaluated through simulations. This evaluation and some unusual features of the filter are discussed. In the second step, we perform a geophysical inversion using a linear Bayesian estimator. To allow an unbiased comparison between gravity and topography, we use a simulation technique to smooth and distort the radar topographic data so as to yield maps having the same characteristics as our gravity maps. The maps presented cover 2/3 of the surface of Venus and display the strong topography‐gravity correlation previously reported. The topography‐gravity scatter plots show two distinct trends.

Automated Matching of Pairs of SIR-B Images for Elevation Mapping

During the SIR-B mission in October 1984, a significant number of overlapping synthetic aperture radar (SAR) images of various ground areas was collected. This has offered the first opportunity to perform stereo analyses on images from space that cover large ground areas to determine elevation information. This paper presents the preliminary results of an investigation to obtain elevation data from stereo pairs of SIR-B images. First, the accuracy with which elevation information can be derived from SIR-B image pairs is evaluated theoretically. It is shown that elevation accuracy is a function of the slant range resolution, the incidence angles with which the stereo pair is obtained, the accuracies in spacecraft state estimation, and determination of corresponding pixels in the stereo pair. Next, a hierarchical method is developed to match the corresponding pixels. This method involves iterative removal of local distortions and correlations of pairs of local neighborhoods in the two images. Since it is necessary to perform the matching at every pixel in the image, it is very computationally intensive. Therefore, it has been implemented on the Massively Parallel Processor (MPP) at the Goddard Space Flight Center (GSFC). The MPP's speed permits two iterations of this technique to operate on a pair of 512 × 512 images within 7 s. Results of applying this algorithm to SIR-B images of Mount Shasta, CA, are shown. The matching algorithm performs well in regions of the image with significant features.

Venus gravity west of Beta Regio

Doppler tracking data from the Pioneer Venus Orbiter (PVO) have been used to estimate the anomalous gravity field in the region of Venus west of Beta Regio. The analysis invokes a Kalman filter-smoother to solve the nonlinear spacecraft state estimation problem and a linear Bayesian estimator to perform the geophysical inversion. The topographic map for this region, derived from the PVO radar, has been filtered to have the same distortions and degree of smoothing as the gravity map. The undulations of the gravity are about 0.2 times as large as expected from the topography on the assumption that the latter is uncompensated. A comparison of the gravity and topography by means of the spectral admittance is consistent with Airy compensation at a depth of 50 km if the surface material has a density of 2.6 g/cm 3. However, this is not a unique interpretation.