Modified grid algorithm for star pattern identification by using star trackers

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.

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.

In this paper, a laboratory model is designed to evaluate the performance of star tracker algorithms for attitude determination of a satellite. Star tracker is the most accurate attitude sensor that determines satellite direction by applying centroiding algorithm, star identification and attitude determination. To utilize such algorithms, first, high quality of star images are needed which should be provided through the star tracker camera. Then, such images are given to processors and it determines the attitude of camera and satellite in three axes based on mentioned algorithms. First, in preliminary design, we define important star tracker parameters, like accuracy, detector, processor and field of view. In this paper, we have considered the range accuracy in yaw/pitch axis less than 20 arcsec and for roll axis less than 100 arcsec. To improve the attitude determination accuracy, we have applied an adaptive structure in centroiding algorithm, as only brightness stars are selected and identification algorithm is done based on them. Another important parameter is speed of identification algorithm which is handled by an ARM processor and improving pyramid algorithm, we reached less than 25 ms. Duo to this time, updating rate would be desired. Also knowing the coordination of bore sight direction that is distance between focal lens and imager is another important parameter that affects the accuracy of attitude and by ground calibration of camera, this parameter could be estimated carefully. Finally, implementation results on real images that are captured by a star tracker demonstrate optimum performance of algorithms.

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.

A full-sky autonomous star identification is presented. Star pattern is generated in the form of radial and cyclic features. Since radial pattern is a reliable feature, it is used for initial match, and cyclic pattern is then used in re-match step. Simulation is done to verify the performance and the result shows this method is more robust against location error compared with grid algorithm under the same condition.

A robust star identification algorithm with star shortlisting

Star identification can be accomplished by several different available algorithms that identify the stars observed by a star tracker. However, efficiency and reliability remain key issues and the availability of new active pixel cameras requires new approaches. Two novel algorithms for recursive mode star identification are presented here. The first approach is derived by the spherical polygon search (SP-search) algorithm, it was used to access all the cataloged stars observed by the sensor field-of-view (FOV) and recursively add/remove candidate cataloged stars according to the predicted image motion induced by camera attitude dynamics. Star identification is then accomplished by a star pattern matching technique which identifies the observed stars in the reference catalog. The second method uses star neighborhood information and a catalog neighborhood pointer matrix to access the star catalog. In the recursive star identification process, and under the assumption of "slow" attitude dynamics, only the stars in the neighborhood of previously identified stars are considered for star identification in the succeeding frames. Numerical tests are performed to validate the absolute and relative efficiency of the proposed methods.

This paper presents a novel approach to the attitude determination problem of space vehicles. The proposed algorithm utilizes a modified Genetic Algorithm (GA) to solve the “lost in space” star pattern recognition problem associated with star tracker attitude determination systems. Characteristics of the stars that are visible within the Field of View (FOV) – reflected on an image taken by the onboard star tracker – are formulated using simple geometric descriptions. The proposed GA minimizes the discrepancy between the characteristics of the stars inside the actual FOV and a candidate FOV selected from the on board stored star map. The global minimum of the discrepancy represents the inertial coordinates of the FOV bore-sight. The concept of a Spiral Genetic Algorithms (SGA) is proposed where the search area decreases for consecutive GA, with consequently tighter constraints, making it converge to the desired location. Also the algorithm presented has the capability of determining the rotational angle between the spacecraft’s coordinate system and that of a real star map. Simulation results indicate competitive results to current star trackers in terms of accuracy.

There are many devices used today on modern spacecraft to accurately determine their position. Star trackers (space cameras) are the most accurate device in use for determining a spacecraft's position. The star tracker operates automatically, getting images of star patterns within its Field of View (FOV). The stars observed by the camera can then be identified and the orientation of the spacecraft can be calculated. A star tracker is composed of a baffle for removing stray lights, optical system, image sensor, microprocessor and memory. In this paper, basic principles for designing a baffle system, such as determination of baffle dimensions, arrangement of vanes inside the baffle, and coating of surface have been reviewed. We first determined dimensions of a two-stage conical baffle. Then, we calculated positions and height of vane to prohibit diffusely reflected light inside the baffle from entering into the photometer. In order to evaluate performance of the designed baffle system, we have used a ray tracing program using a Monte Carlo method and calculated attenuation factor.

Star trackers are state-of-the-art attitude estimation devices which function by recognising and tracking star patterns. Most commercial star trackers use conventional optical sensors. A recent alternative is to use event sensors, which could enable more energy efficient and faster star trackers. However, this demands new algorithms that can efficiently cope with high-speed asynchronous data, and are feasible on resource-constrained computing platforms. To this end, we propose an event-based processing approach for star tracking. Our technique operates on the event stream from a star field, by using multiresolution Hough Transforms to time-progressively integrate event data and produce accurate relative rotations. Optimisation via rotation averaging is then used to fuse the relative rotations and jointly refine the absolute orientations. Our technique is designed to be feasible for asynchronous operation on standard hardware. Moreover, compared to state-of-the-art event-based motion estimation schemes, our technique is much more efficient and accurate.

This paper uses a radial basis function (RBF) neural network for star identification. A feature vector consisting of distance and angle cosine values is constructed as a library of recognition feature stars and input to the neural network. Then, the network’s output is the number of the corresponding primary star. Comparing the training efficiency and robustness of the grid algorithm with the BP neural network and radial basis function neural network for star identification, the experimental results show that the radial basis function neural network has a strong anti-interference ability against noise and pseudo-star interference. The following conclusions are drawn from the simulation tests: the sample recognition rate of radial basis function neural network algorithm can reach 96% for star position and star addition noise; when the proportion of pseudo-star reaches 20%, the sample recognition rate of radial basis function neural network algorithm is still higher than grid algorithm and BP neural network, which indicates that the radial basis function neural network algorithm has the solid anti-interference ability to both noise and pseudo-star. Therefore, the radial basis function neural network algorithm based on star Identification has specific application prospects.

Herbal medicine for inflammatory bowel diseases: development of pattern identification algorithms by retrospective analysis of case series data

The autonomous star trackers, using charge couple device CCD camera, or active pixel sensor APS, as natural sensors for optical input, has assumed a permanent position. This is due to their highly accurate attitude determination, small size, light weight and simple functionality, making them devices of choice in nearly all modern space vehicles especially for Nano-satellites. In all star trackers, star centroiding is a fundamental process necessary for pattern recognition. In this paper, a new method is presented, which predicts centers of regions of star locations, in successive image frames, from the current knowledge of boresight direction, which is updated successively. This method is very attractive for hardware implementation using APS imagers known for their random accessibility feature lacked in CCD's. The big advantage of this method is that it does not need pattern recognition, thus making it fast. This reduction of computational budget, power consumption and time, added to the capability of incorporating them in APS imagers, is appealing to Nano-satellites navigational instrumentation.

Transient-based fault identification algorithm using parametric models for meshed HVDC grids

Spacecraft would be expected to encounter diverse extreme environmental (EE) conditions throughout their mission phases. These EE conditions are often coupled. Star tracker is a high accurate 3-axis attitude measuring instrument used in various spacecrafts. In this paper, an effective scheme to the thermal/optical analysis in optical system of star sensor was described and the concept of thermal optical analysis of star tracker optical system was introduced in detail. Using finite element analysis (FEA) and ray tracing, we can study the relationship of optical properties of optical systems and optical system's temperature distribution . A lens system configuration having six uncemented elements was discussed. The lens system was a 56mm EFL, which was different from common lens used in imaging system that this lens system was required to have a high resolving power in design thoughts. It was designed to determine the attitude of space platform by detecting and mapping the geometric pattern of stars. Based on this system, the FEA models of the optical system were established for temperature distribution calculation and for thermal-elastic structural deformation analysis respectively. Using the models, the steady-state temperature distributions of the tracker were simulated. The rigid body displacements of the optical components under homogeneous temperature changes and certain temperature distributions were derived out. It is convenient to use Zernike polynomials as the data transmission between optical and structural analysis programs. Here, Zernike polynomials and their fitting method are used as an example to determine the thermal induced optical degradations of the optical system.