A modified unscented Kalman filter for autonomous navigation of distributed satellite systems

2017 ◽  
Author(s):  
Ai Zhang ◽  
Yong Li
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Unscented Kalman Filter ◽  
Satellite Systems ◽  
Distributed Satellite

scholarly journals Performance Evaluation of GPS Auto-Surveying Techniques

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7374
Author(s):  
João Manito ◽  
José Sanguino
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Unscented Kalman Filter ◽  
Weighted Least Squares ◽  
Base Station ◽  
Global Navigation Satellite Systems ◽  
Precise Position ◽  
Satellite Systems ◽  
Global Navigation Satellite ◽  
Position Estimate

With the increase in the widespread use of Global Navigation Satellite Systems (GNSS), increasing numbers of applications require precise position data. Of all the GNSS positioning methods, the most precise are those that are based in differential systems, such as Differential GNSS (DGNSS) and Real-Time Kinematics (RTK). However, for absolute positioning, the precision of these methods is tied to their reference position estimates. With the goal of quickly auto-surveying the position of a base station receiver, four positioning methods are analyzed and compared, namely Least Squares (LS), Weighted Least Squares (WLS), Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), using only pseudorange measurements, as well as the Hatch Filter and position thresholding. The research results show that the EKF and UKF present much better mean errors than LS and WLS, with an attained precision below 1 m after about 4 h of auto-surveying. The methods that presented the best results are then tested against existing implementations, showing them to be very competitive, especially considering the differences between the used receivers. Finally, these results are used in a DGNSS test, which verifies a significant improvement in the position estimate as the base station position estimate improves.

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.

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.

Near Space Pseudolite Navigation System Design and High-Performance Filtering Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 931-935
Author(s):  
She Sheng Gao ◽  
Wen Hui Wei ◽  
Li Xue
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Particle Filtering ◽  
Autonomous Navigation ◽  
Unscented Kalman Filter ◽  
Navigation Systems ◽  
Filtering Algorithm ◽  
Near Space ◽  
Autonomous Navigation System

This paper analyzes the defects of satellite navigation systems that exist in positioning and precision-guided weapons and pointes out the advantages and military needs of pseudolite. The autonomous navigation nonlinear mathematical model of Near Space Pseudolite SINS/CNS/SAR autonomous navigation system is established. Based on the merits of fading filter, robust adaptive filtering and particle filter, we propose a fading adaptive Unscented Particle Filtering algorithm. The proposed filtering algorithm is applied to SINS/CNS/SAR autonomous navigation system and conducted simulation calculation with the Unscented Kalman filter and particle filter comparison. The results show that the new algorithm that is proposed meets the needs of pseudolite autonomous navigation, and the navigation accuracy is significantly higher than the Unscented Kalman filter and particle filter algorithm.

scholarly journals GNSS/LiDAR-Based Navigation of an Aerial Robot in Sparse Forests

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4061 ◽  
Author(s):  
Antonio C. B. Chiella ◽  
Henrique N. Machado ◽  
Bruno O. S. Teixeira ◽  
Guilherme A. S. Pereira
Keyword(s):  
Autonomous Navigation ◽  
Unscented Kalman Filter ◽  
Unmanned Vehicles ◽  
Global Navigation Satellite Systems ◽  
Fusion Algorithm ◽  
Forest Environment ◽  
Satellite Systems ◽  
Aerial Robot ◽  
Robust Adaptive ◽  
Global Navigation Satellite

Autonomous navigation of unmanned vehicles in forests is a challenging task. In such environments, due to the canopies of the trees, information from Global Navigation Satellite Systems (GNSS) can be degraded or even unavailable. Also, because of the large number of obstacles, a previous detailed map of the environment is not practical. In this paper, we solve the complete navigation problem of an aerial robot in a sparse forest, where there is enough space for the flight and the GNSS signals can be sporadically detected. For localization, we propose a state estimator that merges information from GNSS, Attitude and Heading Reference Systems (AHRS), and odometry based on Light Detection and Ranging (LiDAR) sensors. In our LiDAR-based odometry solution, the trunks of the trees are used in a feature-based scan matching algorithm to estimate the relative movement of the vehicle. Our method employs a robust adaptive fusion algorithm based on the unscented Kalman filter. For motion control, we adopt a strategy that integrates a vector field, used to impose the main direction of the movement for the robot, with an optimal probabilistic planner, which is responsible for obstacle avoidance. Experiments with a quadrotor equipped with a planar LiDAR in an actual forest environment is used to illustrate the effectiveness of our approach.

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