A passive system for determining the attitude of a satellite

INTRODUCTIONAttitude is the term used to describe a rigid body's orientation in three-dimensional space. In a more general sense, it is the description of the relative orientation of two coordinate frames. In vehicle guidance, navigation, and control (GNC) applications near Earth's surface (e.g., applications involving airplanes, marine vessels, etc.) the two coordinate frames of interest are sometimes referred to as the body and navigation reference frames. The body frame is rigidly attached to and moves with the vehicle. The navigation frame is normally a locally level (or tangent) coordinate frame. That is, it has an origin attached to Earth's surface and located directly below the vehicle's current position. Its x-y-z axes are lined-up with North, East, and Down (along the local vertical) directions, respectively. Attitude determination systems are used to measure or estimate the relative orientation of these two frames. The information generated by attitude determination systems is indispensable in many GNC applications. A few examples of applications requiring attitude information include pilot-in-the-loop control of manned aircraft, accurate payload pointing on remote sensing platforms, and autonomous navigation and guidance of uninhabited aerial, ground, and marine vehicles.The recent interest in high performance, micro aerial vehicles (MAVs) has necessitated the design of compact, accurate, and inexpensive attitude determination systems. MAVs are designed to be disposable and are very small in size and weight (largest dimensions no greater than 15 cm) [1,2]. At these scales, the avionics and sensor payloads can represent a significant fraction of the overall vehicle dimension and weight. To address this need, many inexpensive rate-gyro based attitude determination systems have been developed [3][4][5]. However, inexpensive and miniature solid-state rate gyros (< $1000 per axis) tend to be low-performance sensors which have outputs subject to wideband noise and rate instabilities on the order of 10 to 100 ± /hr [6,7]. In order to determine attitude, the rate gyro outputs must be integrated to give attitude and this leads to unbounded attitude errors. Thus, successful implementation of an attitude determination system that relies solely on rate gyros requires the use of sensors with exceptionally accurate and stable outputs. These types of gyros would have output errors less than 0:1 ± /hr and tend to be 1) prohibitively expensive, 2) have high power consumption, and/or 3) be physically too large for many miniature vehicle applications.An alternative to relying on accurate and expensive rate gyros is to devise a system which fuses miniature, low-performance (and low-cost and low-power) gyros with a gyro-free aiding system using a complementary filter architecture as shown in Fig. 1. The gyro-free aiding system provides either one of two types of information. It can provide 1) a noisy but unbiased direct attitude measurement periodically (i.e., a low bandwidth attitude update), or 2) it can provide indire...

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“…Since n!b C (q e ) represents a small rotation between the n 0 and n frames, using (14) it can be written as…”

Section: Linearization In the Navigation Framementioning

confidence: 99%

“…In general, these algorithms can be classified into three groups. The first group consists of deterministic algorithms such as the ones described in [14]. The second group consists of algorithms which take a classical least squares approach to the problem as articulated by Wahba in [12].…”

Section: Introductionmentioning

confidence: 99%

MAV attitude determination by vector matching

Gebre-Egziabher

Elkaim

2008

IEEE Trans. Aerosp. Electron. Syst.

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“…In this subsection, we use the measurement for agent 2 (relative position information expressed in the coordinate basis of agent 2, i.e., [23], [24] such that R(n, φ) is reconstructed. The TRIAD algorithm is a classical deterministic algorithm to estimate an attitude, in which the attitude is directly calculated based on two pairs of nonparallel vectors.…”

Section: A Estimation Of R(n φ) Using the Triad Algorithmmentioning

confidence: 99%

On 3-D Formation Control With Mismatched Coordinates

Meng

Anderson

Hirche

2018

IEEE Trans. Control Netw. Syst.

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Abstract-A typical formation shape control problem involves point agents sensing relative positions, i.e. orientations and distances, of their neighbors and then moving so that these relative positions achieve some prespecified values. Such a procedure, requiring as it does sensing of orientations, implicitly presupposes that all agents have a shared understanding of the common orientations. On the other hand, there may be biases in sensors, variations in the earth's magnetic field interfering with compass-based sensing, or drift in inertial sensors, with the result that orientations are inconsistently measured or measured with error. In this paper, we investigate the formation control problem with mismatched coordinates in the threedimensional space, considering the consequences of this error. First, the situation of a two agent formation is first considered. We show that the agents converge to a fixed, but distorted formation exponentially fast. In contrast to the matched case, the formation is not asymptotically stationary, but rather instead translates with a certain constant velocity depending on the mismatches. The formation distortion between the actual one and the desired one is obtained, as well as the steady state velocity of the formation for small mismatch orientations. The case of agents with double integrator dynamics is then considered and similar phenomena are observed. Based on the results, an estimation algorithm is given to obtain the mismatch rotation matrix, which allows a compensation algorithm to be proposed such that the desired formation is achieved with zero steady-state velocity for the formation as a whole. The case of n-agent formations is finally considered, first with a star graph and then with a general graph. Simulations are provided to validate the theoretical results.

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“…Now, we have two vector observation that are known in the reference frame, as well as in the body frame. A simple algebraic method to transform such observations into attitude matrix is the TRIAD algorithm and its variants [17], [18]. Since the performance of these methods is well studied, the following sections focus on the performance of the proposed methods in estimating individual pointing vectors.…”

Section: A a Simplified Approachmentioning

confidence: 99%