Implicit UKF and its observability analysis of satellite stellar refraction navigation system

The stellar refraction navigation is a type of autonomous celestial navigation method for satellites that uses high-accuracy star sensors to obtain the stellar refraction. The apparent height and the stellar refraction angle are two kinds of measurements for the satellite stellar refraction navigation system. A better navigation performance can be achieved by the stellar refraction angle rather than the refraction apparent height. However, the measurement model of the stellar refraction angle is an implicit function, which needs an implicit filter method. A new implicit unscented Kalman filter (UKF) method is proposed and applied to the satellite stellar refraction navigation system in this paper. The main difference between the classical UKF and the implicit UKF lies in the measurement update. First, since the implicit measurement model can be rearranged into an equation whose one side is zero, the vector of zeros is regarded as the equivalent measurement, and the difference between the predicted measurement and its true value is utilized for updating the predicted state and its covariance matrix. Second, the measurement noise and its covariance matrix are transformed based on the unscented transformation principle. In addition, the implicit UKF is compared with the implicit extended Kalman filter (IEKF), the iterative IEKF, and the UKF. Simulations show that the proposed implicit UKF method can obtain about 19.0% and 17.5% improvements in three-dimensional root mean square position estimation compared to the IEKF and the iterative IEKF, respectively. Although the navigation performance of the UKF is close to the implicit UKFs, the computational time of the implicit UKF is much shorter than the UKFs. In addition, the sensitivities of the four different Kalman filters to the star sensor's faults and the atmosphere perturbation are compared, respectively.

Autonomous satellite navigation using starlight refraction angle measurements

A starlight refraction scheme with single star sensor used in autonomous satellite navigation system

Autonomous navigation is one of the key technologies to ensure the successful implementation of deep space exploration mission. Aiming at the requirement of celestial autonomous navigation ability of asteroid probe in practical engineering tasks, the integrated autonomous navigation method combined celestial spectral velocity measurement with celestial image angle measurement is proposed to realize continuous autonomous, real-time and high-precision navigation of asteroid probe. Considering the limitation of traditional radio navigation on the ground and the high requirement for autonomous survivability of asteroid probe, the task of asteroid explorer has an urgent need for real-time and autonomous navigation ability. The current autonomous navigation method mainly extracts the angle measurement information of the probe relative to the target celestial planet from the optical image information of the target celestial planet, and then calculates and determines the navigation state of the probe. However the accuracy of velocity estimation by this navigation method is limited, which still cannot fully meet the current demand of asteroid probe. In view of the shortcomings of the main existing autonomous navigation methods, this paper proposes a direct velocity measurement method based on astronomical spectrum, and combines it with the astronomical angle navigation method to form the integrated autonomous navigation method. Based on the integrated autonomous navigation method, the navigation system model and filtering algorithm are derived, and the observability and navigation accuracy performance of the integrated navigation system are analyzed. Taking the Ceres exploration mission as an engineering background, the simulation results show that the stellar velocity information measured by astronomical spectroscopy can greatly improve the integrated navigation performance. And the accuracy of position and velocity estimation results of the probe can restrain the influence of errors more effectively and enhance the reliability of navigation system. Compared with the traditional terrestrial radio navigation or angle measurement navigation methods, the integrated navigation results have higher accuracy and better real-time performance, which can provide accurate navigation information for asteroid probe orbit modification. The integrated navigation method derived in this paper is effective, reliable and easy to implement. It provides a reference for the implementation of the asteroid exploration project and the subsequent major deep space missions in China.

This paper presents a novel satellite autonomous navigation and orbit determination method in spatial data chains and network, using wireless measurements of the. distances from each other. The satellite position and velocity in ECEF Cartesian coordinates, as a function of the six Keplerian elements, are used to estimate the instantaneous orbital elements by the extended Kalman filter algorithm. The problems confronted in the spatial network are simply analyzed, such as high data transmission between satellites, accurate wireless distance measurement and network management. In the case of the low Earth orbit satellite (LEO), the estimated position error was controlled to around ten meters. Two important problems to be studied further are discussed.

Collaborative navigation allows a group of mobile networked users to improve their navigation performance. Each node in the network has a means of its own to navigate (e.g. INS/GPS). However, individual navigation performance is limited due to, for instance, inertial sensor drifts when GPS signal is lost. By exchanging navigation states and in particular obtaining relative measurements (range, range rate or angular) via radio communications, the distributed nodes collaborate and serve as anchors for one another. In this paper, we investigate spatial and temporal effects of collaborative navigation. The improvement in navigation performance is affected by the number of collaborating nodes and inter-node measurement quality in relation to the uncertainty of individual navigation states (spatial effect). Navigation errors after inter-node measurement exchanges become correlated, which limits further reduction in errors by subsequent measurements (temporal effect). Understanding of such fundamental effects provides practical guidelines for optimal design of communication protocols and estimation algorithms. Copyright © 2014 Institute of Navigation

GPS and Inertial Navigation Systems are used for positioning and attitude determination in a wide range of applications. Over the last few years, a number of low cost inertial sensors have become available. Although they exhibit large errors, GPS measurements can be used correct the INS and sensor errors to provide high accuracy real-time navigation. The integration of GPS and INS measurements is usually achieved using a Kalman filter. The measurement and process noise matrices used in the Kalman filter represent the stochastic properties of the GPS and INS systems respectively. Traditionally they are defined a priori and remain constant throughout a processing run. In reality, the stochastic properties of the system vary depending on factors such as vehicle dynamics and environmental conditions. This is particularly an issue for low cost inertial sensors where the initial sensor errors can be large, and experience significant temporal variation. This paper investigates three adaptive Kalman filtering algorithms that can be used to improve the estimation of the stochastic properties of a low cost INS. The algorithms are tested using a low cost Crossbow MEMS IMU integrated with carrier phase GPS for a marine application. The adaptive Kalman filtering algorithms are shown to reduce the dependence on the a priori information used in the filter. This results in a reduction in the time required to initialise the sensor errors and align the INS, and results in an improvement in navigation performance.

The solar differential rotation results in different velocities on the solar disk. The Doppler velocities of different points on the solar disk observed by the spacecraft are also different, which are functions of the current position of the spacecraft. Thus, using the velocities' information or their differences, the position of the spacecraft can be estimated. The advantage of velocity difference measurement is that the influence of some systematic error, such as the spectrometer error and the periodic variation of the sun's velocity on the navigation performance, can be eliminated. In this article, a new autonomous navigation method using the Doppler velocity differences of different points on the solar disk is proposed. Taking solar explorer as an example, simulations show that the proposed method can effectively restrain systematic errors compared with the traditional Doppler velocity method.

Developing an autonomous indoor mobile robot navigation system from scratch is very difficult and it takes a long time. It requires a series of complex tasks such as handling given sensor inputs, calculating all the information needed for autonomous navigation, and controlling actuators required for movement. In this paper, an autonomous navigation system for indoor mobile robots is introduced mainly based on open source provided by the robot operating system. The presented system is capable of autonomously navigating an unstructured indoor environment avoiding collision with static or dynamic objects. To this end, the system consists of three main modules: mapping, localization, and planning. The mapping module builds a global map for an unknown environment by means of a simultaneous localization and mapping algorithm based on laser scanner data. The localization module estimates the mobile robot’s pose within the prebuilt map by way of an adaptive Monte Carlo localization approach. The planning module builds a local cost map for collision avoidance, generates collision-free trajectories to reach a goal pose based on the cost map, and produces control commands to follow the trajectories. The presented system has been tested not only in simulation environments built in the Gazebo simulator but also in real environments utilizing the Jackal mobile robot, to validate its performance for autonomous navigation including collision avoidance.

Satellite navigation by stellar refraction

A number of applications of autonomous satellite navigation are discussed. They include the onboard annotation of all experimental data and the onboard correction of imaging data. The accuracy levels required for each function are presented. Two alternative techniques for achieving autonomous satellite navigation are considered. One technique utilizes data from the Global Positioning System (GPS). The other approach utilizes data from a geosynchronous relay satellite system such as the Tracking and Data Relay Satellite System (TDRSS). The TDRSS approach makes use of one-way range and Doppler data along with a time transfer mechanism. Comparative accuracy levels for the onboard determination of position, velocity, and time based upon the GPS and TDRSS approaches are presented.

This paper presents methods of mode transition in the integrated navigation based on selective sensor configurations to ensure continuous and reliable navigation performance in urban flight environments such as high-rise buildings. First, this paper characterizes candidate techniques using conventional DOP and error covariance for quantitative analysis of the navigation mode transition. Then, this paper proposes a new hybrid logic that reflects the deployment of heterogeneous sensor measurements considering characteristics of the urban flight environment. Through the proposed mode transition algorithm, enhanced estimation performance is achieved between GPS/INS integrated navigation and map-aided Range/INS integrated navigation. Finally, simulation and practical flight experiments under the urban flight environment demonstrated the suggested improvement of navigation performance.

This paper addresses the issue of autonomous navigation method based on MEMS sensor optimization for mobile robot in Global Urban System (GUS). A new GPS/INS (Inertial Navigation System) navigation scheme is proposed, based on MEMS gyro north-finding approach. Existing research in the area of GUS focuses on the issues related to SLAM (Simultaneous Localization and Mapping) and planned trajectory tracking, while our work will specially consider about the robot's effectiveness and adaptability based on MEMS gyroscope technology and application in prescribed urban area. And we apply the urban map and GPS information into the outdoor experiment to observe the mobile robot's trajectory. Compared with usual integrated navigation, the system's structure is simplified; targeting precision and navigation performance are improved. From the simulation and experimental results, the MEMS gyro north finding accuracy, effectiveness and usefulness of the proposed autonomous navigation scheme are confirmed.

: Several autonomous satellite navigation configurations were studies on a common low-altitude basis with the emphasis on accuracy and convergence. Analysis was performed by simulations using recursive filtering techniques. Configurations investigated included horizon sensors, unknown landmark tracking, known landmark tracking, and one-way doppler systems. Relevant sensor combinations were analyzed to determine possible performance augmentation.

: The primary objective of this study was to generate some indication of the autonomous navigation accuracy that can be achieved with the SHAD concept for various representative satellite orbits. This was accomplished by conducting a covariance analysis utilizing a Kalman filter to sequentially process the measurements as they occur during orbit operation.