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Artykuły w czasopismach na temat "Gaussian star filter"
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Kim, Taeho, Natnael S. Zewge, Hyochoong Bang, and Hyosang Yoon. "GMM-Based Adaptive Extended Kalman Filter Design for Satellite Attitude Estimation under Thruster-Induced Disturbances." Sensors 23, no. 9 (2023): 4212. http://dx.doi.org/10.3390/s23094212.
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Star images from star trackers are usually defocused to capture stars over an exposure time for better centroid measurements. While a satellite is maneuvering, the star point on the screen of the camera is affected by the satellite, which results in the degradation of centroid measurement accuracy. Additionally, this could result in a worse star vector outcome. For geostationary satellites, onboard thrusters are used to maintain or change orbit parameters under orbit disturbances. Since there is misalignment in the thruster and torque is generated by an impulsive shape signal from the torque command, it is difficult to generate target torque; in addition, it also impacts the star image because the impulsive torque creates a sudden change in the angular velocity in the satellite dynamics. This makes the noise of the star image non-Gaussian, which may require introducing a method for dealing with non-Gaussian measurement noise. To meet this goal, in this study, an adaptive extended Kalman filter is implemented to predict measurement vectors with predicted states. The GMM (Gaussian mixture model) is connected in this sequence, giving weighting parameters to each Gaussian density and resulting in the better prediction of measurement vectors. Simulation results show that the GMM-EKF exhibits a better performance than the EKF for attitude estimation, with 30% improvement in performance. Therefore, the GMM-EKF could be a more attractive approach for use with geostationary satellites during station-keeping maneuvers.
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Cui, Kai, Zhaohui Liu, Junfeng Han, Yuke Ma, Peng Liu, and Bingbing Gao. "Event-Driven Maximum Correntropy Filter Based on Cauchy Kernel for Spatial Orientation Using Gyros/Star Sensor Integration." Sensors 24, no. 22 (2024): 7164. http://dx.doi.org/10.3390/s24227164.
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Gyros/star sensor integration provides a potential method to obtain high-accuracy spatial orientation for turntable structures. However, it is subjected to the problem of accuracy loss when the measurement noises become non-Gaussian due to the complex spatial environment. This paper presents an event-driven maximum correntropy filter based on Cauchy kernel to handle the above problem. In this method, a direct installation mode of gyros/star sensor integration is established and the associated mathematical model is derived to improve the turntable’s control stability. Based on this, a Cauchy kernel-based maximum correntropy filter is developed to curb the influence of non-Gaussian measurement noise for enhancing the gyros/star sensor integration’s robustness. Subsequently, an event-driven mechanism is constructed based on the filter’s innovation information for further reducing the unnecessary computational cost to optimize the real-time performance. The effectiveness of the proposed method has been validated by simulations of the gyros/star sensor integration for spatial orientation. This shows that the proposed filtering methodology not only has strong robustness to deal with the influence of non-Gaussian measurement noise but can also achieve superior real-time spatial applications with a small computational cost, leading to enhanced performance for the turntable’s spatial orientation using gyros/star sensor integration.
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Chi, Luo-jia, Xin-xi Feng, and Lu Miao. "Generalized Labeled Multi-Bernoulli Extended Target Tracking Based on Gaussian Process Regression." MATEC Web of Conferences 176 (2018): 01017. http://dx.doi.org/10.1051/matecconf/201817601017.
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For the problems that Gamma Gaussian Inverse Wishart Cardinalized Probability Hypothesis Density (GGIW-CPHD) filter cannot accurately estimate the extended target shape and has a bad tracking performance under the condition of low SNR, a new generalized labeled multi-Bernoulli algorithm based on Gaussian process regression is proposed. The algorithm adopts the star convex to model the extended target, and realizes the online learning of the Gaussian process by constructing the state space model to complete the estimation of the extended target shape. At the same time, in the low SNR environment, the target motion state is tracked by the good tracking performance of the generalized label Bernoulli filter. Simulation results show that for any target with unknown shape, the proposed algorithm can well offer its extended shape and in the low SNR environment it can greatly improve the accuracy and stability of target tracking.
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KETENCİ, Seniha, and Ali GANGAL. "Automatic reduction of periodic noise in images using adaptive Gaussian star filter." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 25 (2017): 2336–48. http://dx.doi.org/10.3906/elk-1506-78.
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Sun, Lifan, Haofang Yu, Jian Lan, Zhumu Fu, Zishu He, and Jiexin Pu. "Tracking of Multiple Maneuvering Random Hypersurface Extended Objects Using High Resolution Sensors." Remote Sensing 13, no. 15 (2021): 2963. http://dx.doi.org/10.3390/rs13152963.
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With the increased resolution capability of modern sensors, an object should be considered as extended if the target extent is larger than the sensor resolution. Multiple maneuvering extended object tracking (MMEOT) uses not only measurements of the target centroid but also high-resolution sensor measurements which may resolve individual features or measurement sources. MMEOT aims to jointly estimate object number, centroid states, and extension states. However, unknown and time-varying maneuvers of multiple objects produce difficulties in terms of accurate estimation. For multiple maneuvering star-convex extended objects using random hypersurface models (RHMs) in particular, their complex maneuvering behaviors are difficult to be described accurately and handled effectively. To deal with these problems, this paper proposes an interacting multiple model Gaussian mixture probability hypothesis density (IMM-GMPHD) filter for multiple maneuvering extended object tracking. In this filter, linear maneuver models derived from RHMs are utilized to describe different turn maneuvers of star-convex extended objects accurately. Based on these, an IMM-GMPHD filtering recursive form is given by deriving new update and merging formulas of model probabilities for extended objects. Gaussian mixture components of different posterior intensities are also pruned and merged accurately. More importantly, the geometrical significance of object extension states is fully considered and exploited in this filter. This contributes to the accurate estimation of object extensions. Simulation results demonstrate the effectiveness of the proposed tracking approach—it can obtain the joint estimation of object number, kinematic states, and object extensions in complex maneuvering scenarios.
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Roell, Tristan, Andreas Seifahrt, and Ralph Neuhäuser. "Search for extrasolar planets with high-precision relative astrometry by ground-based and single-aperture observations." Proceedings of the International Astronomical Union 3, S249 (2007): 57–60. http://dx.doi.org/10.1017/s1743921308016372.
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AbstractWe present our search program for substellar companions using high-precision relative astronomy. Due to its orbital motion around the star, an unseen substellar companion would produce a periodic “wobble” of the host star, which is the astrometric signal of the unseen companion. By measuring the separation between the components of stellar double and triple systems, we want to measure this astrometric signal of a possible unseen companion indirectly as a relative and periodic change of these separations. Using a new observation mode (the “cube-mode”) where the frames were directly saved in cubes with nearly no loss of time during the readout, an adaptive optics system to correct for atmospheric noise and an infrared narrow band filter in the near infrared to suppress differential chromatic refraction (DCR) effects we achieve for our first target (the double star HD 19994) a relative precision for the separation measurements of about 100. . . 150μas per epoch. To reach a precision in the μas-regime, we use a statistical approach. We take several thousand frames per target and epoche and after a verification of a Gaussian distribution the measurement precision can be calculated as the standard deviation of our measurements divided by the square root of the number of Gaussian distributed measurements. Our first observed target is the stellar binary HD 19994 A & B, where the A component has a known radial velocity planet candidate.
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Taha, A. S., L. Labadie, E. Pantin, et al. "The spatial extent of polycyclic aromatic hydrocarbons emission in the Herbig star HD 179218." Astronomy & Astrophysics 612 (April 2018): A15. http://dx.doi.org/10.1051/0004-6361/201732008.
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Aim. We investigate, in the mid-infrared, the spatial properties of the polycyclic aromatic hydrocarbons (PAHs) emission in the disk of HD 179218, an intermediate-mass Herbig star at ~300 pc. Methods. We obtained mid-infrared images in the PAH-1, PAH-2 and Si-6 filters centered at 8.6, 11.3, and 12.5 μm, and N-band low-resolution spectra using CanariCam on the 10-m Gran Telescopio Canarias (GTC). We compared the point spread function (PSF) profiles measured in the PAH filters to the profile derived in the Si-6 filter, where the thermal continuum emission dominates. We performed radiative transfer modeling of the spectral energy distribution (SED) and produced synthetic images in the three filters to investigate different spatial scenarios. Results. Our data show that the disk emission is spatially resolved in the PAH-1 and PAH-2 filters, while unresolved in the Si-6 filter. Thanks to very good observing conditions, an average full width at half maximum (FWHM) of 0.232′′, 0.280′′ and 0.293′′ is measured in the three filters, respectively. Gaussian disk fitting and quadratic subtraction of the science and calibrator PSFs suggests a lower-limit characteristic angular diameter of the emission of ~100 mas, or ~30 au. The photometric and spectroscopic results are compatible with previous findings. Our radiative transfer (RT) modeling of the continuum suggests that the resolved emission should result from PAH molecules on the disk atmosphere being UV-excited by the central star. Simple geometrical models of the PAH component compared to the underlying continuum point to a PAH emission uniformly extended out to the physical limits of the disk model. Furthermore, our RT best model of the continuum requires a negative exponent of the surface density power-law, in contrast with earlier modeling pointing to a positive exponent. Conclusions. We have spatially resolved – for the first time to our knowledge – the PAHs emission in the disk of HD 179218 and set constraints on its spatial extent. Based on spatial and spectroscopic considerations as well as on qualitative comparison with IRS 48 and HD 97048, we favor a scenario in which PAHs extend out to large radii across the flared disk surface and are at the same time predominantly in an ionized charge state due to the strong UV radiation field of the 180 L⊙ central star.
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Huang, Shuo, Hideki Umehata, Ryohei Kawabe, et al. "Characterizing CO Emitters in the SSA22-AzTEC26 Field." Astrophysical Journal 953, no. 1 (2023): 75. http://dx.doi.org/10.3847/1538-4357/ace0c0.
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Abstract We report the physical characterization of four CO emitters detected near the bright submillimeter galaxy (SMG) SSA22-AzTEC26. We analyze the data from Atacama Large Millimeter/submillimeter Array band 3, 4, and 7 observations of the SSA22-AzTEC26 field. In addition to the targeted SMG, we detect four line emitters with signal-to-noise ratio >5.2 in the cube smoothed with a 300 km s−1 FWHM Gaussian filter. All four sources have NIR counterparts within 1″. We perform UV-to-FIR spectral energy distribution modeling to derive the photometric redshifts and physical properties. Based on the photometric redshifts, we reveal that two of them are CO(2–1) at redshifts of 1.113 and 1.146 and one is CO(3–2) at z = 2.124. The three sources are massive galaxies with a stellar mass ≳1010.5 M ⊙, but have different levels of star formation. Two lie within the scatter of the main sequence (MS) of star-forming galaxies at z ∼ 1–2, and the most massive galaxy lies significantly below the MS. However, all three sources have a gas fraction within the scatter of the MS scaling relation. This shows that a blind CO line search can detect massive galaxies with low specific star formation rates that still host large gas reservoirs and that it also complements targeted surveys, suggesting later gas acquisition and the need for other mechanisms in addition to gas consumption to suppress star formation.
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Zari, E., H. Hashemi, A. G. A. Brown, K. Jardine, and P. T. de Zeeuw. "3D mapping of young stars in the solar neighbourhood with Gaia DR2." Astronomy & Astrophysics 620 (December 2018): A172. http://dx.doi.org/10.1051/0004-6361/201834150.
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We study the three dimensional arrangement of young stars in the solar neighbourhood using the second release of the Gaia mission (Gaia DR2) and we provide a new, original view of the spatial configuration of the star-forming regions within 500 pc of the Sun. By smoothing the star distribution through a Gaussian filter, we construct three dimensional (3D) density maps for early-type stars (upper-main sequence, UMS) and pre-main sequence (PMS) sources. The PMS and the UMS samples are selected through a combination of photometric and astrometric criteria. A side product of the analysis is a 3D, G-band extinction map, which we use to correct our colour-magnitude diagram for extinction and reddening. Both density maps show three prominent structures, Scorpius-Centaurus, Orion, and Vela. The PMS map shows a plethora of lower-mass star-forming regions, such as Taurus, Perseus, Cepheus, Cassiopeia, and Lacerta, which are less visible in the UMS map due to the lack of large numbers of bright, early-type stars. We report the finding of a candidate new open cluster towards l, b ∼ 218.5° , − 2°, which could be related to the Orion star-forming complex. We estimate ages for the PMS sample and we study the distribution of PMS stars as a function of their age. We find that younger stars cluster in dense, compact clumps, and are surrounded by older sources, whose distribution is instead more diffuse. The youngest groups that we find are mainly located in Scorpius-Centaurus, Orion, Vela, and Taurus. Cepheus, Cassiopeia, and Lacerta are instead more evolved and less numerous. Finally, we find that the 3D density maps show no evidence for the existence of the ring-like structure which is usually referred to as the Gould Belt.
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Songai, Gao, Zhang Ningning, and Zheng Xiang. "Design of Precise Estimation Algorithm for Sports Pose Based on Multifeature Fusion." Mobile Information Systems 2022 (October 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/1224939.
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The estimation time and estimation precision of motion pose samples are problematic for the pose estimation algorithm of sports movements. This paper proposes a multifeature fusion-based algorithm for accurate posture estimation. The human rod model is constructed after analyzing the human pose estimation technology. Using the Kalman filter method, the degree of freedom and range of motion of the major joints of the human body were determined. The eight-star model was used to extract the sports posture features, and the weighted average method was used to process the grayscale images of sports. Using the multifeature fusion method, the extracted multisource feature vector information is thoroughly analyzed and processed, and a new group of fusion feature vectors is created. Using a mixture Gaussian distribution model, the posture estimation of an athlete’s body is accomplished. Experimental results indicate that when the amount of sports pose sample data is 900 GB, the accurate estimation time of the proposed method is 5.3 s, and its accuracy is 100 percent. Improve the estimation accuracy of samples of sports posture.
Rozprawy doktorskie na temat "Gaussian star filter"
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Petitjean, Julien. "Contributions au traitement spatio-temporel fondé sur un modèle autorégressif vectoriel des interférences pour améliorer la détection de petites cibles lentes dans un environnement de fouillis hétérogène Gaussien et non Gaussien." Thesis, Bordeaux 1, 2010. http://www.theses.fr/2010BOR14157/document.
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Cette thèse traite du traitement adaptatif spatio-temporel dans le domaine radar. Pour augmenter les performances en détection, cette approche consiste à maximiser le rapport entre la puissance de la cible et celle des interférences, à savoir le bruit thermique et le fouillis. De nombreuses variantes de cet algorithme existent, une d’entre elles est fondée sur une modélisation autorégressive vectorielle des interférences. Sa principale difficulté réside dans l’estimation des matrices autorégressives à partir des données d’entrainement ; ce point constitue l’axe de notre travail de recherche. En particulier, notre contribution porte sur deux aspects. D’une part, dans le cas où l’on suppose que le bruit thermique est négligeable devant le fouillis non gaussien, les matrices autorégressives sont estimées en utilisant la méthode du point fixe. Ainsi, l’algorithme est robuste à la distribution non gaussienne du fouillis.D’autre part, nous proposons une nouvelle modélisation des interférences différenciant le bruit thermique et le fouillis : le fouillis est considéré comme un processus autorégressif vectoriel, gaussien et perturbé par le bruit blanc thermique. Ainsi, de nouvelles techniques d'estimation des matrices autorégressives sont proposées. La première est une estimation aveugle par bloc reposant sur la technique à erreurs dans les variables. Ainsi, l’estimation des matrices autorégressives reste robuste pour un rapport faible entre la puissance de la cible et celle du fouillis (< 5 dB). Ensuite, des méthodes récursives ont été développées. Elles sont fondées sur des approches du type Kalman : filtrage de Kalman étendu et filtrage par sigma point (UKF et CDKF), ainsi que sur le filtre H∞.Une étude comparative sur des données synthétiques et réelles, avec un fouillis gaussien ou non gaussien, est menée pour révéler la pertinence des différents estimateurs en terme de probabilité de détection<br>This dissertation deals with space-time adaptive processing in the radar’s field. To improve the detection’s performances, this approach consists in maximizing the ratio between the target’s power and the interference’s one, i.e. the thermal noise and the clutter. Several variants of its algorithm exist, one of them is based on multichannel autoregressive modelling of interferences. Its main problem lies in the estimation of autoregressive matrices with training data and guides our research’s work. Especially, our contribution is twofold.On the one hand, when thermal noise is considered negligible, autoregressive matrices are estimated with fixed point method. Thus, the algorithm is robust against non-gaussian clutter.On the other hand, a new modelling of interferences is proposed. The clutter and thermal noise are separated : the clutter is considered as a multichannel autoregressive process which is Gaussian and disturbed by the white thermal noise. Thus, new estimation’s algorithms are developed. The first one is a blind estimation based on errors in variable methods. Then, recursive approaches are proposed and used extension of Kalman filter : the extended Kalman filter and the Sigma Point Kalman filter (UKF and CDKF), and the H∞ filter. A comparative study on synthetic and real data with Gausian and non Gaussian clutter is carried out to show the relevance of the different algorithms about detection’s probability
Części książek na temat "Gaussian star filter"
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Mohammadi M., Petkov N., Peletier R.F., Bibiloni P., and Bunte K. "Detection of Globular Clusters in the Halo of Milky Way." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-929-4-70.
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Finding interesting stellar structures inside large astronomical datasets is a challenging task. Depending on the type of data different techniques are needed to tackle this challenge. In this paper, we focus on the GAIA survey DR1, which provides the sky positions and G-band magnitudes for more than 109 stars. We compare the use of the magnitude distribution (or luminosity function) and Difference of Gaussian filtering extracted from small patches of the sky to detect globular clusters (GC). By using a Nearest Neighbor Retrieval strategy we find windows which depict similar magnitude distribution or filter responses compared to the ones extracted from known GCs. Our first results show that the Difference of Gaussian filters are advantageous to find spherical structures such as GC if only limited information, such as sky position and G-band magnitudes, are available.
Streszczenia konferencji na temat "Gaussian star filter"
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Ketenci, Seniha, and Ali Gangal. "Design of Gaussian star filter for reduction of periodic noise and quasi-periodic noise in gray level images." In 2012 International Symposium on Innovations in Intelligent Systems and Applications (INISTA). IEEE, 2012. http://dx.doi.org/10.1109/inista.2012.6246937.
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Ginolhac, G., and P. Forster. "Performance analysis of a Robust Low-Rank STAP filter in low-rank Gaussian clutter." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5496218.
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Ito, Hiroshi. "Identification of Metal Temperature Distribution in Steam Turbine During Start-Up Operation Using Particle Filter and Model Order Reduction." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90727.
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Abstract In steam turbines (STs), evaluation technology of thermal deformations during transient operations is increasingly important, because of a demand for improvement of operability such as shortening startup time. However, it is still difficult to predict temperatures of metal parts with sufficient accuracy due to complexity of heat transfer phenomena. The present study puts its focus on developing a method to identify metal temperatures of STs during start-up operations using particle filter (PF), a kind of sequential Bayesian filter utilizing ensemble approximation, and model order reduction (MOR) technique. In this method, a self-organizing state-space model is used to calculate time evaluations of metal temperatures, and heat transfer coefficient (HTC) parameters and steam bulk temperature parameters. And an optimal estimate of these is obtained through iterations of a short-time prediction and a correction of variables using measured temperatures. In this state-space model, state variables including HTC and bulk temperature parameters are treated as random variables, and the time evolution of each variable is modeled as follows; (1) Metal temperature is modeled using a reduced order model (ROM) in order to reduce computational time of PF. The ROM is constructed from a finite element model for unsteady heat transfer analysis using MOR technique. (2) HTC is modeled as a random walk model where its changes during one time step is randomly determined using Gaussian noise. Here, the magnitude of this noise is adjusted automatically. (3) Bulk temperature is modeled using prediction formulas with Gaussian noise added. As a validation problem, a cold start-up operation of a ST unit of a gas turbine combined cycle (GTCC) is considered. The proposed method is applied to the problem, and boundary conditions (BCs) are identified using measured temperatures at 68 measurement points. Then, the heat transfer analysis based on finite element analysis (FEA) is performed using the identified BCs. As a result of this FEA, it is confirmed that the calculated metal temperature tends to agree better with measurements compared with that of initial FEA, and that the errors of the calculated temperature at measurement points reduce by 41% on average compared with initial FEA. From this result, it is concluded that this proposed method is effective for metal temperature identification during start-up operations.
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Grisell, Andrea, and Murali Sundaram. "Creation of Functionally Graded Glass Channels by Electrochemical Discharge Machining Process: A Feasibility Study." In ASME 2021 16th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/msec2021-63916.
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Abstract Functionally graded surfaces — surfaces with properties that are engineered to have spatial variations — have numerous applications such as micropumps, auto-mixers, and flow control for lab-on-chip devices. Manufacturing of functionally graded surfaces is an increasingly important topic of research. This study investigates the feasibility of creating a functionally graded surface during channeling of borosilicate glass by the electrochemical discharge machining (ECDM) process. The ability to create surface roughness gradients in microchannels during the machining process was demonstrated by modifying the input voltage, tool feed rate, and tool rotation speed. Microchannels with graded surface roughness having Ra values ranging from 0.35 to 4.07 μm were successfully machined on borosilicate glass by ECDM. Surface profiles were obtained via a stylus profilometer, and roughness values were calculated after detrending and applying a Gaussian filter. To demonstrate the process versatility, micro channels with increasing and decreasing Ra values were machined, one increasing from 1.43 μm to 4.07 μm, another decreasing from 3.29 μm to 1.10 μm. To demonstrate the process repeatability, a micro channel with similar surface roughness on both ends and a lower Ra value in the center was created. In this channel, the Ra value at the start is 0.35 μm, reducing to 0.24 μm, then rising again to 0.38 μm in the final section.