scholarly journals Mars Cruise Orbit Determination from Combined Optical Celestial Techniques and X-ray Pulsars

2017 ◽  
Vol 70 (4) ◽  
pp. 719-734 ◽  
Author(s):  
Jiandong Liu ◽  
Erhu Wei ◽  
Shuanggen Jin
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Unscented Kalman Filter ◽  
Integrated System ◽  
Observation Data ◽  
X Ray ◽  
Celestial Navigation ◽  
Federated Kalman Filter ◽  
Mars Pathfinder ◽  
Navigation Accuracy

The precise autonomous navigation for deep space exploration by combination of multi-source observation data is a key issue for probe control and scientific applications. In this paper, the performance of an integrated Optical Celestial Navigation (OCN) and X-ray Pulsars Autonomous Navigation (XNAV) system is investigated for the orbit of Mars Pathfinder. Firstly, OCN and XNAV single systems are realised by an Unscented Kalman Filter (UKF). Secondly, the integrated system is simulated with a Federated Kalman Filter (FKF), which can do the information fusion of the two subsystems of UKF and inherits the advantages of each subsystem. Thirdly, the performance of our system is evaluated by analysing the relationship between observation errors and navigation accuracy. The results of the simulation experiments show that the biases between the nominal and our calculated orbit are within 5 km in all three axes under complex error conditions. This accuracy is also better than current ground-based techniques.

scholarly journals Autonomous Navigation of Mars Probes by Single X-ray Pulsar Measurement and Optical Data of Viewing Martian Moons

2016 ◽  
Vol 70 (1) ◽  
pp. 18-32 ◽  
Author(s):  
Pengbin Ma ◽  
Tianshu Wang ◽  
Fanghua Jiang ◽  
Junshan Mu ◽  
Hexi Baoyin
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Unscented Kalman Filter ◽  
Optical Data ◽  
Optical Navigation ◽  
Integrated Navigation ◽  
X Ray ◽  
Navigation Accuracy ◽  
Navigation Information ◽  
Navigation Method

In order to achieve high accuracy of autonomous navigation for Mars probes, an integrated navigation method using X-ray pulsar measurement and optical data of viewing Martian moons is proposed. For single X-ray pulsar measurement on board a Mars probe, navigation accuracy is low due to its poor observability. On the other hand, Phobos and Deimos, two natural moons of Mars, are important optical navigation information sources available for Mars missions. However, the Martian moons ephemeris bias and the differences between barycentre and centre of brightness of Martian moons will result in low navigation accuracy. The method of integrated navigation using X-ray pulsar measurement and optical data of viewing Martian moons can overcome the defect and achieve accurate navigation. Two sequential orbit determination algorithms, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), are compared. The simulation results show this method can obtain high autonomous navigation accuracy during the phase of a probe orbiting Mars.

Autonomous Navigation of Mars Probes by Combining Optical Data of Viewing Martian Moons and SST Data

2015 ◽  
Vol 68 (6) ◽  
pp. 1019-1040 ◽  
Author(s):  
Pengbin Ma ◽  
Fanghua Jiang ◽  
Hexi Baoyin
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Unscented Kalman Filter ◽  
Optical Measurement ◽  
Optical Data ◽  
Deep Space Exploration ◽  
Rank Defect ◽  
The Difference ◽  
Navigation Accuracy ◽  
Navigation Information

Autonomous navigation has become a key technology for deep space exploration missions. Phobos and Deimos, the two natural moons of Mars, are important optical navigation information sources available for Mars missions. However, during the phase of the probe orbiting close to Mars, the ephemeris bias and the difference between the barycentre and the centre of brightness of a Martian moon will result in low navigation accuracy. On the other hand, Satellite-to-Satellite Tracking (SST) can achieve convenient and high accuracy observation for autonomous navigation. However, this cannot apply for a Mars mission during the Mars orbit phase only by SST data because of a rank defect problem of the Jacobian matrix. To improve the autonomous navigation accuracy of Mars probes, this paper presents a new autonomous navigation method that combines SST radio data provided by two probes and optical measurement by viewing the natural Martian moons. Two sequential orbit determination algorithms, an Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are compared. Simulation results show this method can obtain high autonomous navigation accuracy during the probe's Mars Orbit phase.

Integrated celestial navigation for spacecraft using interferometer and Earth sensor

2020 ◽  
Vol 234 (16) ◽  
pp. 2248-2262
Author(s):  
Kai Xiong ◽  
Chunling Wei
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Optical Path ◽  
Path Delay ◽  
Optical Interferometer ◽  
Q Learning ◽  
Celestial Navigation ◽  
Navigation Accuracy

An integrated celestial navigation scheme for spacecrafts based on an optical interferometer and an ultraviolet Earth sensor is presented in this paper. The optical interferometer is adopted to measure the change in inter-star angles due to stellar aberration, which provides information on the velocity of the spacecraft in the plane perpendicular to the direction of the observed star. In order to enhance the navigation performance, the measurements obtained from the ultraviolet Earth sensor is used to eliminate the unfavorable effect caused by the gravitational deflection of starlight. As the prior knowledge about the optical path delay bias of the optical interferometer may be ambiguous, a Q-learning extended Kalman filter is derived to fuse the two types of measurements, and estimate the kinematic state together with the optical path delay bias. The solution of the autonomous navigation system consists of position, velocity and attitude of the spacecraft. Numerical simulation shows that an evident improvement in navigation accuracy can be achieved by introducing the ultraviolet Earth sensor into the navigation system. In addition, it is shown that the Q-learning extended Kalman filter performs better than the traditional extended Kalman filter.

New fault-tolerant federated Kalman filters for integrated navigation

2018 ◽  
Vol 90 (1) ◽  
pp. 65-73
Author(s):  
Yueqian Liang ◽  
Yingmin Jia
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Fault Tolerant ◽  
Distribution Coefficients ◽  
Integrated Navigation ◽  
Content Type ◽  
Vector Distribution ◽  
Federated Kalman Filter ◽  
Aerial Vehicle ◽  
Navigation Accuracy

Purpose The purpose of this paper is to achieve accurate integrated navigation results for the unmanned aerial vehicle (UAV) systems even in the presence of possible navigation faults in the subsystems of the federated Kalman filter. Design/methodology/approach The federated Kalman filter is modified from two aspects to get accurate navigation results under abnormity. First, time-variant vector distribution coefficients trading off the navigation accuracy and the observability degree of each state component are computed to replace the traditional scalar coefficients. Second, a fault-tolerant filter is proposed as the local navigation filter. Findings Simulations for the navigation of a UAV system show that the proposed method can be applied for accurate navigation purpose even in the presence of subsystem navigation faults. Originality/value New fault-tolerant federated Kalman filters for integrated navigation are presented to achieve accurate navigation solutions.

Federated Particle Filter Technology Based on JIDS/SINS/GPS Integrated Navigation System

2013 ◽  
Vol 347-350 ◽  
pp. 1544-1548
Author(s):  
Zi Yu Li ◽  
Yan Liu ◽  
Ping Zhu ◽  
Cheng Ying
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Integrated System ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System ◽  
Non Linear ◽  
Federated Kalman Filter

In multi-sensor integrated navigation systems, when sub-systems are non-linear and with Gaussian noise, the federated Kalman filter commonly used generates large error or even failure when estimating the global fusion state. This paper, taking JIDS/SINS/GPS integrated navigation system as example, proposes a federated particle filter technology to solve problems above. This technology, combining the particle filter with the federated Kalman filter, can be applied to non-linear non-Gaussian integrated system. It is proved effective in information fusion algorithm by simulated application, where the navigation information gets well fused.

Ultra-tight GPS/IMU Integration based Long-Range Rocket Projectile Navigation

2016 ◽  
Vol 66 (1) ◽  
pp. 64 ◽  
Author(s):  
Handong Zhao ◽  
Zhipeng Li
Keyword(s):  
Kalman Filter ◽  
Long Range ◽  
High Performance ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Position Information ◽  
Cubature Kalman Filter ◽  
Carrier Phase Tracking ◽  
Output Information ◽  
Navigation Accuracy

<p>Accurate navigation is important for long-range rocket projectile’s precise striking. For getting a stable and high-performance navigation result, a ultra-tight global position system (GPS), inertial measuring unit integration (IMU)-based navigation approach is proposed. In this study, high-accuracy position information output from IMU in a short time to assist the carrier phase tracking in the GPS receiver, and then fused the output information of IMU and GPS based on federated filter. Meanwhile, introduced the cubature kalman filter as the local filter to replace the unscented kalman filter, and improved it with strong tracking principle, then, improved the federated filter with vector sharing theory. Lastly simulation was carried out based on the real ballistic data, from the estimation error statistic figure. The navigation accuracy of the proposed method is higher than traditional method.</p><p><strong>Defence Science Journal, Vol. 66, No. 1, January 2016, pp. 64-70, DOI: http://dx.doi.org/10.14429/dsj.66.8326</strong></p>

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