• 제목/요약/키워드: kalman filter

검색결과 2,169건 처리시간 0.032초

Performance Analysis of Real-time Orbit Determination and Prediction for Navigation Message of Regional Navigation Satellite System

  • Jaeuk Park;Bu-Gyeom Kim;Changdon Kee;Donguk Kim
    • Journal of Positioning, Navigation, and Timing
    • /
    • 제12권2호
    • /
    • pp.167-176
    • /
    • 2023
  • This study presents the performance analysis of real-time orbit determination and prediction for navigation message generation of Regional Navigation Satellite System (RNSS). Since the accuracy of ephemeris and clock correction in navigation message affects the positioning accuracy of the user, it is essential to construct a ground segment that can generate this information precisely when designing a new navigation satellite system. Based on a real-time architecture by an extended Kalman filter, we simulated orbit determination and prediction of RNSS satellites in order to assess the accuracy of orbit and clock prediction and signal-in-space ranging errors (SISRE). As a result of the simulation, the orbit and clock accuracy was at 0.5 m and 2 m levels for 24 hour determination and six hour prediction after the determination, respectively. From the prediction result, we verified that the SISRE of RNSS for six hour prediction was at a 1 m level.

수중함 자유항주모형 개발 및 기본 성능 분석 (Submarine Free Running Model Development and Basic Performance Analysis)

  • 이주호;김선홍;신지환;안진형
    • 대한조선학회논문집
    • /
    • 제60권4호
    • /
    • pp.256-265
    • /
    • 2023
  • This paper describes the results of the development of the submarine Free Running Model (FRM). First, the goal of development was set based on the test conditions and the test environment, and the system was obtained accordingly. The target submarine, Joubert BB2 submarine, was selected with a scale of 18.35 in accordance with the development goal. In order to conduct a submarine FRM test underwater, where communication is impossible, the FRM must operate at least semi-autonomously. For this purpose, an Extended Kalman Filter (EKF) based underwater integrated navigation system and control system using a sailplane and an X-shaped sternplane were designed respectively. In addition, a ballast system was designed to enable the model to float to the water surface in case of an emergency. To verify its propulsion, navigation, and control performance, the FRM tests were conducted in both indoor and outdoor basins. As a result, the relationship between propeller RPM and vehicle speed was derived, and it was confirmed that the navigation and control performance met the target value.

Impact of assimilating the terrestrial water storage on the water and carbon cycles in CLM5-BGC

  • Chi, Heawon;Seo, Hocheol;Kim, Yeonjoo
    • 한국수자원학회:학술대회논문집
    • /
    • 한국수자원학회 2021년도 학술발표회
    • /
    • pp.204-204
    • /
    • 2021
  • Terrestrial water storage (TWS) includes all components of water (e.g., surface water, groundwater, snow and ice) over the land. So accurately predicting and estimating TWS is important in water resource management. Although many land surface models are used to predict the TWS, model output has errors and biases in comparison to the observation data due to the model deficiencies in the model structure, atmospheric forcing datasets, and parameters. In this study, Gravity Recovery And Climate Experiment (GRACE) satelite TWS data is assimilated in the Community Land Model version 5 with a biogeochemistry module (CLM5.0-BGC) over East Asia from 2003 to 2010 by employing the Ensemble Adjustment Kalman Filter (EAKF). Results showed that TWS over East Asia continued to decrease during the study period, and the ability to simulate the surface water storage, which is the component of the CLM derived TWS, was greatly improved. We further investigated the impact of assimilated TWS on the vegetated and carbon related variables, including the leaf area index and primary products of ecosystem. We also evaluated the simulated total ecosystem carbon and calculated its correlation with TWS. This study shows that how the better simulated TWS plays a role in capturing not only water but also carbon fluxes and states.

  • PDF

Improving streamflow prediction with assimilating the SMAP soil moisture data in WRF-Hydro

  • Kim, Yeri;Kim, Yeonjoo
    • 한국수자원학회:학술대회논문집
    • /
    • 한국수자원학회 2021년도 학술발표회
    • /
    • pp.205-205
    • /
    • 2021
  • Surface soil moisture, which governs the partitioning of precipitation into infiltration and runoff, plays an important role in the hydrological cycle. The assimilation of satellite soil moisture retrievals into a land surface model or hydrological model has been shown to improve the predictive skill of hydrological variables. This study aims to improve streamflow prediction with Weather Research and Forecasting model-Hydrological modeling system (WRF-Hydro) by assimilating Soil Moisture Active and Passive (SMAP) data at 3 km and analyze its impacts on hydrological components. We applied Cumulative Distribution Function (CDF) technique to remove the bias of SMAP data and assimilate SMAP data (April to July 2015-2019) into WRF-Hydro by using an Ensemble Kalman Filter (EnKF) with a total 12 ensembles. Daily inflow and soil moisture estimates of major dams (Soyanggang, Chungju, Sumjin dam) of South Korea were evaluated. We investigated how hydrologic variables such as runoff, evaporation and soil moisture were better simulated with the data assimilation than without the data assimilation. The result shows that the correlation coefficient of topsoil moisture can be improved, however a change of dam inflow was not outstanding. It may attribute to the fact that soil moisture memory and the respective memory of runoff play on different time scales. These findings demonstrate that the assimilation of satellite soil moisture retrievals can improve the predictive skill of hydrological variables for a better understanding of the water cycle.

  • PDF

Grey algorithmic control and identification for dynamic coupling composite structures

  • ZY Chen;Ruei-yuan Wang;Yahui Meng;Timothy Chen
    • Steel and Composite Structures
    • /
    • 제49권4호
    • /
    • pp.407-417
    • /
    • 2023
  • After a disaster like the catastrophic earthquake, the government have to use rapid assessment of the condition (or damage) of bridges, buildings and other infrastructures is mandatory for rapid feedbacks, rescue and post-event management. Many domain schemes based on the measured vibration computations, including least squares estimation and neural fuzzy logic control, have been studied and found to be effective for online/offline monitoring of structural damage. Traditional strategies require all external stimulus data (input data) which have been measured available, but this may not be the generalized for all structures. In this article, a new method with unknown inputs (excitations) is provided to identify structural matrix such as stiffness, mass, damping and other nonlinear parts, unknown disturbances for example. An analytical solution is thus constructed and presented because the solution in the existing literature has not been available. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results of linear and nonlinear structures show that the proposed method is able to identify structural parameters and their changes due to damage and unknown excitations. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and control theory.

Brain Tumor Detection Based on Amended Convolution Neural Network Using MRI Images

  • Mohanasundari M;Chandrasekaran V;Anitha S
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • 제17권10호
    • /
    • pp.2788-2808
    • /
    • 2023
  • Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.

모바일 로봇을 위한 학습 기반 관성-바퀴 오도메트리 (Learning-based Inertial-wheel Odometry for a Mobile Robot)

  • 김명수;장근우;박재흥
    • 로봇학회논문지
    • /
    • 제18권4호
    • /
    • pp.427-435
    • /
    • 2023
  • This paper proposes a method of estimating the pose of a mobile robot by using a learning model. When estimating the pose of a mobile robot, wheel encoder and inertial measurement unit (IMU) data are generally utilized. However, depending on the condition of the ground surface, slip occurs due to interaction between the wheel and the floor. In this case, it is hard to predict pose accurately by using only encoder and IMU. Thus, in order to reduce pose error even in such conditions, this paper introduces a pose estimation method based on a learning model using data of the wheel encoder and IMU. As the learning model, long short-term memory (LSTM) network is adopted. The inputs to LSTM are velocity and acceleration data from the wheel encoder and IMU. Outputs from network are corrected linear and angular velocity. Estimated pose is calculated through numerically integrating output velocities. Dataset used as ground truth of learning model is collected in various ground conditions. Experimental results demonstrate that proposed learning model has higher accuracy of pose estimation than extended Kalman filter (EKF) and other learning models using the same data under various ground conditions.

IEEE 802.11 DCF 에서 칼만 필터를 통한 ATIM 창 크기의 동적 할당 기법 (Dynamic Allocation of ATIM Window Size using Kalman Filter in IEEE 802.11 DCF)

  • 이장수;유승환;김승욱;김성천
    • 한국정보처리학회:학술대회논문집
    • /
    • 한국정보처리학회 2007년도 추계학술발표대회
    • /
    • pp.995-998
    • /
    • 2007
  • 무선 네트워크에서 사용되는 단말기는 이동성이라는 특징상 한정된 에너지를 사용하여 동작하게 된다. 따라서 무선 호스트에 의해 소모되는 에너지의 양을 감소시키기 위한 기술은 대단히 중요하다. 이러한 기술적 지원을 위해 IEEE 802.11 에서는 DCF (Distributed Coordination Function) 전력 절감 메카니즘을 제안하고 있다. 그런데, DCF 를 위한 IEEE 802.11 전력 절감 메카니즘에서는 ATIM 창 동안 노드들은 비콘 기간 동안 깨어 있는 상태로 있을 것인지를 결정하기 위해서 control packet 을 교환 하는데, 이러한 ATIM 창 크기는 각각의 노드들의 전력 절감에 상당한 영향을 미친다. 그래서 ATIM 창 크기를 효율적으로 할당하기 위해 DPSM 과 같은 기법들이 개발되었다. 본 논문은 ATIM 창 크기를 동적으로 증감시켜서 ATIM 창 시간동안 소모되는 에너지를 줄이도록 하였다. ATIM 창 크기를 동적으로 할당하기 위하여 통계적 예측 기법인 칼만 필터를 도입하여 예측시스템을 구축하였으며, 이 예측 시스템을 통해 다음 상태에서 적용할 ATIM 창 크기를 예측하여 동적으로 할당하도록 하였다. 실험 결과 네트워크 생존 시간을 28.6% 증가시켰고, ATIM 창 크기 예측값의 오차는 4.42%로 나타났다.

Tuned Mass Damper(TMD)를 이용한 구조물의 Linear Quadratic Gaussian(LQG) 하이브리드 진동제어 (LQG Hybrid Vibration Control of a Structure Using TMD)

  • 이진호;이상범
    • 한국구조물진단유지관리공학회 논문집
    • /
    • 제10권4호
    • /
    • pp.108-118
    • /
    • 2006
  • 본 연구는 지진동을 받는 구조물의 응답을 제어하기 위한 하이브리드 LQG 기법의 효용성을 조사하는 것이 목적이다. 입력 기진력은 엘센트로 지진이며 지반 가속도을 적절히 조절하여 구조물이 탄성 범위내에서 거동하도록 하였다. 수동 제어 장치로서 최상층에 설계된 TMD는 LQG 제어 알고리즘에 의해 통제되며 능동 제어기와 함께 하이브리드 제어 시스템을 이룬다. 이 기법을 통해 제어된 변위 응답을 비교한 결과 순수하게 능동 제어를 한 경우에 비해 훨씬 작은 크기의 입력으로도 변위를 제어할 수 있었으며 센서의 위치는 최상층에 부착하는 것이 가장 효과적인 것으로 나타났다.

Dynamic Control Allocation for Shaping Spacecraft Attitude Control Command

  • Choi, Yoon-Hyuk;Bang, Hyo-Choong
    • International Journal of Aeronautical and Space Sciences
    • /
    • 제8권1호
    • /
    • pp.10-20
    • /
    • 2007
  • For spacecraft attitude control, reaction wheel (RW) steering laws with more than three wheels for three-axis attitude control can be derived by using a control allocation (CA) approach.1-2 The CA technique deals with a problem of distributing a given control demand to available sets of actuators.3-4 There are many references for CA with applications to aerospace systems. For spacecraft, the control torque command for three body-fixed reference frames can be constructed by a combination of multiple wheels, usually four-wheel pyramid sets. Multi-wheel configurations can be exploited to satisfy a body-axis control torque requirement while satisfying objectives such as minimum control energy.1-2 In general, the reaction wheel steering laws determine required torque command for each wheel in the form of matrix pseudo-inverse. In general, the attitude control command is generated in the form of a feedback control. The spacecraft body angular rate measured by gyros is used to estimate angular displacement also.⁵ Combination of the body angular rate and attitude parameters such as quaternion and MRPs(Modified Rodrigues Parameters) is typically used in synthesizing the control command which should be produced by RWs.¹ The attitude sensor signals are usually corrupted by noise; gyros tend to contain errors such as drift and random noise. The attitude determination system can estimate such errors, and provide best true signals for feedback control.⁶ Even if the attitude determination system, for instance, sophisticated algorithm such as the EKF(Extended Kalman Filter) algorithm⁶, can eliminate the errors efficiently, it is quite probable that the control command still contains noise sources. The noise and/or other high frequency components in the control command would cause the wheel speed to change in an undesirable manner. The closed-loop system, governed by the feedback control law, is also directly affected by the noise due to imperfect sensor characteristics. The noise components in the sensor signal should be mitigated so that the control command is isolated from the noise effect. This can be done by adding a filter to the sensor output or preventing rapid change in the control command. Dynamic control allocation(DCA), recently studied by Härkegård, is to distribute the control command in the sense of dynamics⁴: the allocation is made over a certain time interval, not a fixed time instant. The dynamic behavior of the control command is taken into account in the course of distributing the control command. Not only the control command requirement, but also variation of the control command over a sampling interval is included in the performance criterion to be optimized. The result is a control command in the form of a finite difference equation over the given time interval.⁴ It results in a filter dynamics by taking the previous control command into account for the synthesis of current control command. Stability of the proposed dynamic control allocation (CA) approach was proved to ensure the control command is bounded at the steady-state. In this study, we extended the results presented in Ref. 4 by adding a two-step dynamic CA term in deriving the control allocation law. Also, the strict equality constraint, between the virtual and actual control inputs, is relaxed in order to construct control command with a smooth profile. The proposed DCA technique is applied to a spacecraft attitude control problem. The sensor noise and/or irregular signals, which are existent in most of spacecraft attitude sensors, can be handled effectively by the proposed approach.