Ellipticity pivot star method for autonomous star identification

An algorithm for fast extraction and identification of star target

Grid algorithm is a classical star identification algorithm based on star pattern. A three-dimensional grid algorithm for all-sky autonomous star identification is proposed, which is associated with the information of star view magnitude. In contrast with traditional grid algorithm that constructs the grid cells on two-dimensional plane (e.g. x-y coordinate plane), the proposed approach makes use of the star view magnitudes of the neighboring stars as the third dimension (e.g. z-axis). A pattern is generated for each of its three-dimensional grid cells that contain neighboring star are 1, and those without are 0. The progress of star identification is to determine which pattern in the database is associated with the particular sensor pattern. Simulation shows that this method can achieve identification rate of 98.0% while the standard deviation of star position error and star view magnitudes are 1 pixel and 0.3Mv respectively. Compared with the traditional grid algorithm, the identification rate is higher, and the average runtime is 50 percent shorter.

A robust star identification algorithm with star shortlisting

Robot vision is essential to aid in performing robotic tasks. Existing robotic path tracking methods used in the industry such as line following are simple but not capable to cater for continuous floor plan changes. This paper proposes a new vision based pattern design and detection algorithm that can be implemented on an existing mobile robot structure to provide path tracking and routing. A unique pattern that is capable to embed information is successfully designed. The detection algorithm is tested at different lighting conditions, pattern size and orientation. A larger pattern size and good lighting conditions has produced favorable high detection of the pattern. Subsequently, after pattern detection, a unique identifier of the pattern is generated in the form of a binary matrix. The generated binary matrix is proposed to be used as a location identifier to aid in mobile robot routing.

Typical GN&C solutions for precision pointing spacecraft require a pair of orthogonally oriented star sensors for attitude estimation and three-axis gyroscopes to provide angular rate information. The combined sensor suite however, leads to large weight, volume and power consumption. The SG100 solution seeks to eliminate the need for a separate gyroscope sensor by deriving angular velocities based on star measurements alone. The SG100 is capable of acquiring images of stars as faint as visual magnitude of 10.0 at exposure times less than 10ms and contains proprietary robust star identification, centroiding, attitude estimation and filtering algorithms to estimate precision attitude and drift-free angular rates at 100Hz. In this paper we report the development and testing of the SG100 engineering model. Results from the radiation testing (total ionizing dose) of critical components, night sky sensitivity, noise and angular rate measurement tests are presented. Test results show that the SG100 exceeds the requirements on the star sensitivity and noise equivalent angles while providing an accurate estimation of the angular rates.

No-reference light field image quality assessment based on depth, structural and angular information

Star trackers are currently the most accurate spacecraft attitude sensors. As a result, they are widely used in remote sensing satellites. Since traditional charge-coupled device (CCD)-based star trackers have a limited sensitivity range and dynamic range, the matching process for a star tracker is typically not very sensitive to star brightness. For active pixel sensor (APS) star trackers, the intensity of an imaged star is valuable information that can be used in star identification process. In this paper an improved brightness referenced star identification algorithm is presented. This algorithm utilizes the k-vector search theory and adds imaged stars' intensities to narrow the search scope and therefore increase the efficiency of the matching process. Based on different imaging conditions (slew, bright bodies, etc.) the developed matching algorithm operates in one of two identification modes: a three-star mode, and a four-star mode. If the reference bright stars (the stars brighter than three magnitude) show up, the algorithm runs the three-star mode and efficiency is further improved. The proposed method was compared with other two distinctive methods the pyramid and geometric voting methods. All three methods were tested with simulation data and actual in orbit data from the APS star tracker of ZY-3. Using a catalog composed of 1500 stars, the results show that without false stars the efficiency of this new method is 4∼5 times that of the pyramid method and 35∼37 times that of the geometric method.

This paper aims to design a stabilized Adaptive Model Predictive Control (MPC) for nonlinear continuous systems with unknown dynamics at the start time. The proposed method is based on the Lyapunov MPC (LMPC) where the derivative of the Lyapunov function of the closed-loop system is limited to be less than that under an auxiliary Lyapunov controller. Different from the existing LMPC methods, this paper provides an online constraint updating strategy, which gradually reduces the conservation of the constraint. Specifically, the piece-wise linear (PWL) model updated by the Set-Membership (SM) identification method is adopted in the constraint and the auxiliary controller design, which can result in a non-increasing boundary of the model error. Then, a sufficient condition that guarantees the stabilization of the closed-loop system is deduced, which takes the effect of sample-and-hold implementation and the error boundary of the PWL model into account. By this condition, an optimization problem to update the Lyapunov controller for relaxing the constraints of MPC is proposed. We prove that, by the proposed method, the states will eventually converge to a small region around the equilibrium, both this small region and the conservatism of MPC decrease with the increasing of the accuracy of the PWL model. The application of the proposed method to a chemical process demonstrates the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The control problem of nonlinear continuous systems with unknown dynamics has attracted the attention of researchers. This article develops an Adaptive MPC for this kind of system, which updates the boundary of uncertainties according to the measurement, and updates the constraints of MPC according to the updated uncertainty boundary. It provides a guaranteed stabilization and continuously relaxed constraint design for MPC where machine learning-based models are employed to predict the future trajectory. In the process of implementation, the method solves two optimization problems: one is to get the parameters of constraints which is based on the PWL model, and the other is used to optimize the future trajectory with the updated constraints. To proceed, the initial boundary of the uncertainty should be given at the initial instant, the initialization of PWL models and constraints can be configured based on it. The proposed method can be used for tracking control of chemical processes, power systems, robots, etc., where the accurate dynamics are not provided in advance and an improved performance is required.

Fast ℓ1-recursive total least squares algorithm for sparse system identification

The reliability of star sensor in a harsh environment has recently become a research hotspot. In some harsh environment such as plume interference, a large number of false stars can be observed, leading to failure in star identification. In this paper, we propose a false star filtering algorithm, which can be used as a preprocessing algorithm for any existing star identification algorithm. By utilizing the difference between the motion of false stars and true stars, the algorithm performs angular distance tracking and star voting on multiple consecutive frames of star images and achieves false star filtering. The software simulation results show that for the star images containing more than 700 false stars, the algorithm is able to find out all true stars in less than 10 frames, and the success rate of the algorithm remains high when the star sensor rotates at up to 1°/s. The algorithm is also implemented on an existing star senor and evaluated with star images generated by a dynamic star simulator. The experimental results indicate that with the help of the proposed algorithm, the robustness of a normal star identification algorithm can be significantly improved.

This paper investigates the identification of Continuous-Time models based on binary observations. Currently, no identification algorithm has been proposed in this field. The reason is twofold: first the time-domain differentiation operator of a such model structure prevents the use of existing identification methods based on binary observations, second the simple knowledge of binary observations on the output prevents the use of existing Continuous-Time identification methods. In this paper a two steps identification algorithm is derived. It is based on the combined use of a specific input signal and Support Vector Machines for the reconstruction of a high resolution output signal. This high resolution output signal is then used for the estimation of a Continuous-Time model. An extension for the identification of a MISO system is also proposed. Some simulation results are given in order to illustrate the validity of the proposed method.

Acoustical damage detection of wind turbine blade using the improved incremental support vector data description

A Fast Approximation Algorithm for Set-Membership System Identification

sEMG Pattern Recognition of Muscle Force of Upper Arm for Intelligent Bionic Limb Control

A star pattern identification algorithm, called modified grid algorithm, for attitude determination of spacecraft is addressed. The proposed algorithm is closely connected to a general pattern recognition technique. Each star is characterized by a well-defined pattern that can be determined by the surrounding stars. The so-called grid algorithm is one of the star identification algorithms based upon the pattern recognition approach. The proposed algorithm, is motivated by the conventional grid algorithm. To enhance the performance of the conventional grid algorithm, a modified method using polar grid, virtual grid, and multi-references is proposed. Simulation study is conducted for the demonstration of the new algorithm. The proposed modified grid approach turns out to make the grid algorithm more robust and reliable.