scholarly journals Irnss Orbit and Clock Bias Estimation using Navic Ground Receiver Data: Extended Kalman Filter

2019 ◽  
Vol 9 (2) ◽  
pp. 713-717
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Orbit Determination ◽  
Sequential Estimation ◽  
Estimation Algorithm ◽  
Time Instant ◽  
Tropospheric Delay ◽  
Bias Estimation ◽  
Satellite Clock ◽  
Clock Error

The aim of this work is to precisely estimate the IRNSS satellite’s orbit and clock errors using NavIC receiver data. Orbit determination is required to precisely calculate the user/receiver position on the Earth. In this study, Bengaluru, Surat, Kolkata, and Hyderabad’s NavIC ground receivers’ data is considered for orbit estimation. The pseudo-range measurements received by the ground receivers have multiple errors added due to ionospheric delay, tropospheric delay, multipath delays, satellite clock errors, and some unmodeled effects. But, the major factor accounting for errors is the satellite clock error. Hence, along with position and velocity of the satellite, even the clock correction is estimated using Extended Kalman Filter (EKF). EKF is a sequential estimation algorithm which estimates satellite position, velocity and clock error at each time instant. In this paper, results of all seven IRNSS satellite’s orbit determination are discussed.

scholarly journals Research on the Effectiveness of Different Estimation Algorithm on the Autonomous Orbit Determination of Lagrangian Navigation Constellation

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Youtao Gao ◽  
Junkang Chen ◽  
Bo Xu ◽  
Jianhua Zhou
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Orbit Determination ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Fading Memory ◽  
Variance Matrix ◽  
Autonomous Orbit Determination ◽  
Navigation Constellation

The accuracy of autonomous orbit determination of Lagrangian navigation constellation will affect the navigation accuracy for the deep space probes. Because of the special dynamical characteristics of Lagrangian navigation satellite, the error caused by different estimation algorithm will cause totally different autonomous orbit determination accuracy. We apply the extended Kalman filter and the fading–memory filter to determinate the orbits of Lagrangian navigation satellites. The autonomous orbit determination errors are compared. The accuracy of autonomous orbit determination using fading-memory filter can improve 50% compared to the autonomous orbit determination accuracy using extended Kalman filter. We proposed an integrated Kalman fading filter to smooth the process of autonomous orbit determination and improve the accuracy of autonomous orbit determination. The square root extended Kalman filter is introduced to deal with the case of inaccurate initial error variance matrix. The simulations proved that the estimation method can affect the accuracy of autonomous orbit determination greatly.

scholarly journals A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Heikki Hyyti ◽  
Arto Visala
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Direction Cosine ◽  
Estimation Algorithm ◽  
Calibration Method ◽  
Attitude Estimation ◽  
Mems Sensors ◽  
Acceleration Sensors ◽  
Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

scholarly journals A Fuzzy State-of-Charge Estimation Algorithm Combining Ampere-Hour and an Extended Kalman Filter for Li-Ion Batteries Based on Multi-Model Global Identification

2018 ◽  
Vol 8 (11) ◽  
pp. 2028 ◽  
Author(s):  
Xin Lai ◽  
Dongdong Qiao ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
Distribution Area ◽  
Model Parameters ◽  
Model Accuracy ◽  
Soc Estimation ◽  
Fuzzy Fusion ◽  
Fuzzy State

The popular and widely reported lithium-ion battery model is the equivalent circuit model (ECM). The suitable ECM structure and matched model parameters are equally important for the state-of-charge (SOC) estimation algorithm. This paper focuses on high-accuracy models and the estimation algorithm with high robustness and accuracy in practical application. Firstly, five ECMs and five parameter identification approaches are compared under the New European Driving Cycle (NEDC) working condition in the whole SOC area, and the most appropriate model structure and its parameters are determined to improve model accuracy. Based on this, a multi-model and multi-algorithm (MM-MA) method, considering the SOC distribution area, is proposed. The experimental results show that this method can effectively improve the model accuracy. Secondly, a fuzzy fusion SOC estimation algorithm, based on the extended Kalman filter (EKF) and ampere-hour counting (AH) method, is proposed. The fuzzy fusion algorithm takes advantage of the advantages of EKF, and AH avoids the weaknesses. Six case studies show that the SOC estimation result can hold the satisfactory accuracy even when large sensor and model errors exist.

A time-efficient implementation of Extended Kalman Filter for sequential orbit determination and a case study for onboard application

2018 ◽  
Vol 62 (2) ◽  
pp. 343-358
Author(s):  
Jingshi Tang ◽  
Haihong Wang ◽  
Qiuli Chen ◽  
Zhonggui Chen ◽  
Jinjun Zheng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Orbit Determination ◽  
Efficient Implementation

scholarly journals Distributed Orbit Determination for Global Navigation Satellite System with Inter-Satellite Link

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1031 ◽  
Author(s):  
Yuanlan Wen ◽  
Jun Zhu ◽  
Youxing Gong ◽  
Qian Wang ◽  
Xiufeng He
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Global Navigation Satellite System ◽  
Orbit Determination ◽  
Measurement Data ◽  
Satellite System ◽  
Navigation Satellite ◽  
Satellite Link ◽  
Global Navigation ◽  
Global Navigation Satellite

To keep the global navigation satellite system functional during extreme conditions, it is a trend to employ autonomous navigation technology with inter-satellite link. As in the newly built BeiDou system (BDS-3) equipped with Ka-band inter-satellite links, every individual satellite has the ability of communicating and measuring distances among each other. The system also has less dependence on the ground stations and improved navigation performance. Because of the huge amount of measurement data, the centralized data processing algorithm for orbit determination is suggested to be replaced by a distributed one in which each satellite in the constellation is required to finish a partial computation task. In the present paper, the balanced extended Kalman filter algorithm for distributed orbit determination is proposed and compared with the whole-constellation centralized extended Kalman filter, the iterative cascade extended Kalman filter, and the increasing measurement covariance extended Kalman filter. The proposed method demands a lower computation power; however, it yields results with a relatively good accuracy.

Fault-tolerant pose and inertial parameters estimation of an uncooperative spacecraft based on dual vector quaternions

2018 ◽  
Vol 233 (4) ◽  
pp. 1250-1269 ◽  
Author(s):  
Jianping Yuan ◽  
Xianghao Hou ◽  
Chong Sun ◽  
Yu Cheng
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Formation Flying ◽  
Fault Tolerant ◽  
Estimation Algorithm ◽  
Parameters Estimation ◽  
Small Satellites ◽  
Dual Vector ◽  
Inertial Parameters ◽  
Space Target

Estimating the parameters of an unknown free-floating tumbling spacecraft is an essential task for the on-orbit servicing missions. This paper proposes a dual vector quaternion based fault-tolerant pose and inertial parameters estimation algorithm of an uncooperative space target using two formation flying small satellites. Firstly, by utilizing the dual vector quaternions to model the kinematics and dynamics of the system, not only the representation of the model is concise and compacted, but also the translational and rotational coupled effects are considered. By using this modeling technique along with the measurements from the on-board vision-based sensors, a dual vector quaternion based extended Kalman filter for each of the two small satellites is designed. Secondly, both of the estimations from each small satellite will be used as inputs of the fault-tolerant algorithm. This algorithm is based on the fault-tolerant federal extended Kalman filter strategy to overcome the estimation errors caused by the faulty measurements, the unknown space environment and the computing errors by setting the appropriate ratios of the two estimations from the first step dual vector quaternions extended Kalman filter. Together with the first and second steps, a novel fault-tolerant dual vector quaternions federal extended Kalman filter using two formation flying small satellites is proposed by this paper to estimate the pose and inertial parameters of a free-floating tumbling space target. By utilizing the estimation algorithm, a good prior knowledge of the unknown space target can be achieved. Finally, the proposed dual vector quaternion federal extended Kalman filter is validated by mathematical simulations to show its robust performances.

scholarly journals A Discussion Related to Orbit Determination Using Nonlinear Sigma Point Kalman Filter

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
Paula Cristiane Pinto Mesquita Pardal ◽  
Helio Koiti Kuga ◽  
Rodolpho Vilhena de Moraes
Keyword(s):  
Kalman Filter ◽  
Dynamic Model ◽  
Extended Kalman Filter ◽  
Orbit Determination ◽  
Unscented Kalman Filter ◽  
Satellite Orbit ◽  
Observation Model ◽  
Gps Measurements ◽  
Unscented Transformation ◽  
Sigma Point

Herein, the purpose is to present a Kalman filter based on the sigma point unscented transformation development, aiming at real-time satellite orbit determination using GPS measurements. First, a brief review of the extended Kalman filter will be done. After, the sigma point Kalman filter will be introduced as well as the basic idea of the unscented transformation, in which this filter is based. Following, the unscented Kalman filter applied to orbit determination will be explained. Such explanation encloses formulations about the orbit determination through GPS; the dynamic model; the observation model; the unmodeled acceleration estimation; also an application of this new filter approaches on orbit determination using GPS measurements discussion.

An Optimizing Evaluation of Estimation Algorithm about the Fault Detection and Exclusion in the WAAS

2013 ◽  
Vol 397-400 ◽  
pp. 1611-1614
Author(s):  
Rui Bin Zhao ◽  
Rui Li ◽  
Zhi Gang Huang ◽  
Bo Shao
Keyword(s):  
Fault Detection ◽  
Estimation Method ◽  
Engineering Application ◽  
Estimation Algorithm ◽  
Satellite Clock ◽  
Clock Error ◽  
Wide Area Augmentation System ◽  
Integrity Risk ◽  
Position Solution ◽  
Satellite Clock Error

The fault detection and exclusion (FDE) algorithm played a very important role in the Wide Area Augmentation System (WAAS). When the WAAS was not available, the FDE algorithm could provide the monitored information for the navigation position solution. It could autonomously provide the integrity monitoring for the position solution, which it had a capability to detect and prevent a positioning failure that affected the navigation. An optimizing evaluation of estimation method was employed in detecting and isolating the outlying measurements for the satellite ephemeris error and the satellite clock error, which could assess the integrity risk in the WAAS. Therefore this algorithm could provide more precise integrity parameters of the satellite ephemeris error and clock error, which the method is more suitable at the master station for engineering application about the satellite.

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