Hardware Implementation of Fast and Robust Star Centroid Extraction With Low Resource Cost
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Star trackers measure the attitude of a spacecraft by matching the stars captured by the camera and those stored in the onboard database, whose directions are already known. The information (i.e., location and brightness) on the stars in the captured image must be correctly and timely provided for star recognition. This process is called star centroid extraction. The hardware implementation of the star centroid extraction algorithm using parallel and pipelined architecture is a proper solution to ensuring higher accuracy as well as lower time cost. However, some limits restrict the performance of these kinds of algorithms. For example, faint stars, disturbing objects (e.g., the moon, bright planets, and so on), and noise pixels are not valid stars but resume a large amount of resource. Some irregularly shaped star spots may cause the algorithms to obtain inaccurate results. To solve these problems, this paper proposes a star centroid extraction method implemented on field programmable gate arrays (FPGAs) with a dynamic rooted tree architecture. In contrast to the traditional connected domain segmentation method, this method merges the equivalence table in the process of scanning, such that only one scan of the image is needed. Moreover, this method profits from a strict equivalence merging logic and can deal with various irregularly shaped star spots. Experiments are performed both on PC simulations and FPGA platforms, and results show that this method achieves good performance at a very low resource cost.Keywords:
Centroid
Star (game theory)
Abstract In high dynamic conditions, star spot exhibits a tailing phenomenon, which makes it impossible to accurately extract the star centroid. Adjusting the exposure time can shorten the tail length but it will reduce the energy received by the detector, which also makes it difficult to extract the star target. Aiming at the above two problems, this paper proposes a star spot centroid extraction method under high dynamic conditions based on different hash algorithm. The method is divided into three steps. Firstly, establish a mathematical model of dynamic star spots. Then using difference hash algorithm and Hamming distance to realize coarse positioning of star targets in star tracking window. Finally, adopt threshold segmentation and connected domain method to extract star spot centroid in the coarse location area. The experimental results show that the proposed method can adapt to various exposure time, making the star sensor realize the stable tracking at the angular velocity of 3°/s. When the exposure time is 50ms and the angular velocity is 3°/s, the angular distance error is 13 arcseconds, the average extraction rate is 96%, which is 29.6% and 29.7% better than the traditional method.
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Star (game theory)
Hamming distance
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Star centroiding results no longer denote initial locations of star centroid because of the smearing effect. This locational error increases significantly when star sensors work in non-uniform rotation cases. Herein, a novel method is proposed to compensate this error. By taking angular acceleration into consideration, a more accurate stellar trajectory model is deduced first, followed by the derivation of compensation formula. Simulation results show that the proposed method can effectively compensate the extracted centroids to their initial locations. The systematic error caused by the proposed method is less than 0.05 pixels when the angular acceleration is 6.0 °/s 2 , which is negligible from engineering perspective.
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Star centroiding accuracy decreases significantly when star sensor works under highly dynamic conditions or star images are corrupted by severe noise, reducing the output attitude precision. Herein, an adaptive iteration method is proposed to solve this problem. Firstly, initial star centroids are predicted by traditional method, and then based on initial reported star centroids and angular velocities of the star sensor, adaptive centroiding windows are generated to cover the star area and then an iterative method optimizing the location of centroiding window is used to obtain the final star spot extraction results. Simulation results shows that, compared with traditional star image restoration method and Iteratively Weighted Center of Gravity method, AWI algorithm maintains higher extraction accuracy when rotation velocities or noise level increases.
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Star centroid extraction is an important basis of star sensor.As for the complicated noise condition of offshore star image,a new star extraction method based on gravity method was proposed,which included four steps:block scan threshold segmentation,noise rejection based on star continuity,star extraction considering gray information and gravity method for centroid coordinate calculation.The algorithm was proved in experiments using 30offshore star images that it could extract at least 10stars centroids correctly with a success rate of 100%and an accuracy of 1/20pixel while achieving real-time processing for video.Application requirements of shipboard star sensor could be satisfied.
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A* search algorithm
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The characteristic of single star image was introduced. Some useful subdivision methods for improving the precision of star locating were expounded and analyzed. These methods were applied into star locating of simulative single star image. A detailed comparison was carried out for the precision of various subdivision methods when the star center of simulative single star image was located anywhere between the CCD pixels. The simulation results show that squared centroid and Gauss curve fitting methods are better, we can make a choice according to the condition of application.
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A novel method was presented for increasing the accuracy of subpixel centroid estimation for smearing star image. Model of the smearing trajectory of smearing star was built. It helped to study the analytical form of the errors, caused by image smearing, for centroid estimation. In the algorithm, the errors were estimated with accuracy and used to revise the centroid processed by CoM (centre of mass). Simulations have been run to study the effect of angular rates, integration time, and actual position of star on the accuracy of centroid estimation. Results were presented which suggested that the proposed algorithm had a precision better than 1/10 of a pixel when the angular rate was up to 3.0 deg/s.
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Fast and accurately centroid locating can makes the star image recognition more efficient. The Gauss curve fitting method has higher accurate than traditional centroid locating. The process of centroid locating was investigated with the parallel character of FPGA. First, the star image was divided with the method of average threshold. Secondly, the star pixel was recognized by moving a 3×3 template. Finally, used the Gauss curve fitting method to located star centroid. The algorithm for image divided, star pixel recognition and Gauss curve fitting methods was realized in the same time by FPGA. A simulation was done by combining the computer with the hardware circuit. The results show that it spend less than 6ms to process a 512×512 star image and the locating precision less than 1/20 pixel when signal-to-ratio is less than 0.05. It indicates that centroid can be located accurately and fast by FPGA with Gauss curve fitting method.
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Most studies of star image location of star sensors are limited in static state conditions at present.However,the star spot is moving during exposure as a result of satellite rotation,thus influencing the location accuracy of the star image center.In this paper,the accuracy of star image center location in dynamic state is analyzed for active pixel sensor(APS) based star sensors.First of all,sources of the location errors produced by the centroid algorithm are analyzed,a dynamic accuracy estimation method is proposed,and corresponding formulas are deduced.Then choice of the calculation window and the exposure time influencing the dynamic accuracy is analyzed based on given APS star sensor parameters.Finally,corresponding simulations are made to validate the conclusions.
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The ultimate goal of intrusion detection system (IDS) development is to accomplish the best possible accuracy for detection attacks. Various hybrid machine learning techniques were developed for IDS. The centroid-based classification method is a particular hybrid learning approach that highly efficient in the training and classification stages. This paper studies 60 associated papers in the period between 2010 and 2016 concentrating on developing IDS using hybrid classifiers, which 11 papers used centroid-based classification. Similar studies are compared by the algorithm used in hybrid machine learning, the dataset used, the establishment of the representative feature, the stages of pre-processing data, and evaluation methods considered. The accomplishments and limitations in developing IDSs using hybrid machine learning and centroid-based classification were presented and discussed. Several future research opportunities were provided that may encourage interested researchers to work in this area.
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