Integrated Navigation Scheme Research Articles

Autonomous optical navigation of Mars probe aided by one-way Doppler measurements in capture stage

The optical navigation errors of Mars probe in the capture stage depend closely on which targets are selected to be observed in the Mars system. As for this problem, an integrated navigation scheme is proposed wherein the optical observation is aided by one-way Doppler measurements. The errors are then analyzed respectively for the optical observation and one-way Doppler measurements. The real-time calculating scheme which exploits the extended Kalman filter (EKF) framework is designed for the integrated navigation. The simulation tests demonstrate that the errors of optical navigation, which select the Mars moon as the observation target, are relatively smaller than those in the Mars-orientation optical navigation case. On one hand, the integrated navigation errors do not depend on the selecting pattern of optical observation targets. On the other hand, the integrated navigation errors are significantly reduced as compared with those in the optical-alone autonomous navigation mode.

Relative Docking and Formation Control via Range and Odometry Measurements

This article studies the problem of distance-based relative docking of a single robot and formation control of multirobot systems. In particular, an integrated localization and navigation scheme is proposed for a robot to navigate itself to a desired relative position with respect to a fixed landmark at an unknown position, where only range and odometry measurements are used. By carefully embedding historical measurements into equilibrium conditions, we design an integrated estimation-control scheme to achieve the relative docking asymptotically. It is rigorously proved that the robot will converge to the desired docking position asymptotically provided that control gains are chosen to satisfy certain conditions. This scheme is further extended to multirobot systems to consider an integrated relative localization and formation control problem. Unlike widely used spatial cooperation in the existing literature, we propose to exploit both spatial and temporal cooperations for achieving formation control. It is proved that multirobot formation can be achieved with zero error for directed acyclic graphs. Several simulation examples are provided to validate our theoretical results.

Stellar spectrum-based relative velocimetry with spectrometer and its integrated navigation

To enhance the velocimetry’s accuracy for the Mars explorer formation flight, especially the relative one, we develop a relative velocimetry method. In this method, the spectrometers at two Mars explorers are adopted to measure the stellar spectrum shift and to get the velocity relative to the star. Unfortunately, the instantaneous stellar velocity cannot be predicted accurately, which results in a high error in this velocimetry. The difference of these two velocities, which does not include the stellar proper motion, is the relative velocity between a pair of Mars explorers at the stellar direction. However, this navigation method cannot operate independently because of unobservability. To make the navigation system observable and enhance the accuracy of both absolute and relative navigation for the Mars explorer formation flight, the relative velocimetry and the inter-satellite links assist X-ray pulsar navigation. And we propose an autonomous integrated navigation method with observability. This integrated navigation scheme makes use of the extended Kalman filter to deal with the relative velocity, the inter-satellite links and the pulse time-of-arrival. The extended Kalman filter estimates the absolute and relative navigation information for the Mars explorer formation flight. Simulation results manifest that both absolute and relative navigation error of our method are lower than that of X-ray pulsar navigation, especially the relative one.

Reliable Distributed Integrated Navigation Based on CI during Mars Entry

A reliable distributed covariance intersection (CI) fusion integrated navigation algorithm with information feedback during the Mars atmospheric entry is proposed to meet robust, reliable, and high-precision Mars atmospheric entry navigation strategy. A distributed integrated navigation scheme includes four independent subsystems consisting of an IMU and one radio beacon, but each subsystem is weakly observable under limited Mars entry measurements. The scalar weights are fast obtained by maximizing the information contribution of the corresponding estimate. The distributed framework based upon the CI fusion algorithm is designed by using dynamic information distribution coefficients and information feedback strategy. This distributed fusion approach could meet the lower computation cost and robust and reliable capacity and especially is beneficial to the weakly observable or unobservable subsystems during the Mars entry navigation scheme. Numerical simulations show that the proposed distributed CI fusion integrated navigation algorithm can provide consistent navigation accuracy for the Mars entry vehicle and improve the entry navigation robustness under the weakly observable navigation subsystems.

A high-accuracy autonomous navigation scheme for the Mars rover

High-accuracy, autonomous and reliable navigation systems are important foundations for Mars rovers to achieve exploration missions successfully. Based on the motion characteristics and working environment of the rover, the paper shows a high-accuracy strapdown inertial navigation system/visual navigation system/celestial navigation system (SINS/VNS/CNS) integrated navigation scheme suitable for the rover with long-time and long-distance motion. According to the feature point positions in the camera frame at adjacent time obtained by the binocular visual odometry, its velocity in the camera frame and the attitude are calculated, then a subsystem model of SINS/VNS integrated navigation is established. In addition, using the star vectors measured by the large field-of-view star sensor, a high-accuracy attitude matrix of the rover in the inertial frame can be obtained, and a SINS/CNS subsystem model is established. Furthermore, in order to make full use of the complementary advantages of the two subsystems in attitude and position estimation, an interacting multiple model filter is developed. By updating the model probabilities of the subsystems separately in real time, the filter can output accurate navigation information of the rover. Simulation results show that the proposed navigation scheme can significantly improve the estimation accuracy of attitude, position and velocity simultaneously.

Fault-Tolerant Optical Flow Sensor/SINS Integrated Navigation Scheme for MAV in a GPS-Denied Environment

An integrated navigation scheme based on multiple optical flow sensors and a strapdown inertial navigation system (SINS) are presented, instead of the global position system (GPS) aided. Multiple optical flow sensors are mounted on a micro air vehicle (MAV) at different positions with different viewing directions for detecting optical flow around the MAV. A fault-tolerant decentralized extended Kalman filter (EKF) is performed for estimating navigation errors by fusing the inertial and optical flow measurements, which can prevent the estimation divergence caused by the failure of the optical flow sensor. Then, the estimation of navigation error is inputted into the SINS settlement process for correcting the SINS measurements. The results verify that the navigation errors of SINS can be effectively reduced (even more than 9/10). Moreover, although the sensor is in a state of failure for 400 seconds, the fault-tolerant integrated navigation system can still work properly without divergence.

Comparative study on autonomous navigation for Mars cruise probe based on observability analysis

We address the issue of the observability and performance of three autonomous navigation schemes with different combinations of measurements for the terminal cruise phase of Mars exploration. First, the system dynamic model and navigation measurement models are established to facilitate subsequent design and analysis. Second, the detailed degree of observability analysis of three navigation schemes is conduced based on the observability analysis theory of the piece-wise constant system. Third, both extended Kalman filter and unscented Kalman filter are adopted to assess the performance of different navigation approaches. Finally, the performance of three navigation schemes with different measurements is comparatively studied by theoretical analysis and computer simulation. Meanwhile, the optimal integrated navigation scheme (Mars/Sun/one x-ray pulsar measurement) is suggested based on comprehensive consideration of scheme performance and engineering constraints.

A High-accuracy SINS/CNS Integrated Navigation Scheme Based on Overall Optimal Correction

In order to utilise the position and attitude information of a Celestial Navigation System (CNS) to aid a Strapdown Inertial Navigation System (SINS) and make it possible to achieve long-range and high-precision navigation, a new SINS/CNS integrated navigation scheme based on overall optimal correction is proposed. Firstly, the optimal installation angle of the star sensor is acquired according to the geometric relationship between the refraction stars area and the star sensor's visual field. Secondly, an analytical method to determine position and horizontal reference is introduced. Thirdly, the mathematical model of the SINS/CNS integrated navigation system is established. Finally, some simulations are carried out to compare the navigation performance of the proposed SINS/CNS integrated scheme with that of the traditional gyro-drift-corrected integration scheme. Simulation results indicate that in the proposed scheme, without the aid of SINS, CNS can provide attitude and position information and the errors of the SINS are able to be estimated and corrected efficiently. Therefore, the navigation performance of the proposed SINS/CNS scheme is superior to that of a more traditional scheme in long-range flight.

Optimal scheme of star observation of missile-borne inertial navigation system/stellar refraction integrated navigation.

To achieve accurate and completely autonomous navigation for spacecraft, inertial/celestial integrated navigation gets increasing attention. In this study, a missile-borne inertial/stellar refraction integrated navigation scheme is proposed. Position Dilution of Precision (PDOP) for stellar refraction is introduced and the corresponding equation is derived. Based on the condition when PDOP reaches the minimum value, an optimized observation scheme is proposed. To verify the feasibility of the proposed scheme, numerical simulation is conducted. The results of the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are compared and impact factors of navigation accuracy are studied in the simulation. The simulation results indicated that the proposed observation scheme has an accurate positioning performance, and the results of EKF and UKF are similar.

Radio/FADS/IMU integrated navigation for Mars entry

Supposing future orbiting and landing collaborative exploration mission as the potential project background, this paper addresses the issue of Mars entry integrated navigation using radio beacon, flush air data sensing system (FADS), and inertial measurement unit (IMU). The range and Doppler information sensed from an orbiting radio beacon, the dynamic pressure and heating data sensed from flush air data sensing system, and acceleration and attitude angular rate outputs from an inertial measurement unit are integrated in an unscented Kalman filter to perform state estimation and suppress the system and measurement noise. Computer simulations show that the proposed integrated navigation scheme can enhance the navigation accuracy, which enables precise entry guidance for the given Mars orbiting and landing collaborative exploration mission.

An innovative navigation scheme for Mars entry using dynamic pressure measurement

Complete observability of dynamic system is a major concern of navigation in Mars precision landing exploration missions. It is demonstrated that, however, the current measurements used for navigation during Mars entry cannot guarantee the complete observability of the dynamic system. This paper proposes an integrated navigation scheme for Mars entry phase using the dynamic pressure and accelerations from inertial measurement unit (IMU). The dynamic pressure derived from the Mars Entry Atmospheric Data System (MEADS), and the triaxle accelerations from IMU are integrated in a filter as navigation measurements to increase the dynamic system observability and perform state estimation on-board. Afterward, the perturbation of the dynamic caused by parameter uncertainties is built. In order to address the impact of perturbation on state estimation, an adaptive estimator based on modified mixture-of-expert framework is given. Numerical simulation results demonstrate that the proposed integrated navigation scheme can ensure the complete observability of the dynamic system, and the state estimation are converged with entry time after the dynamic pressure has built up.

Robust integrated navigation for Mars atmospheric entry with parameter uncertainties

Abstract Mars atmospheric entry is a key phase to actualize Mars pinpoint landing. In this phase, parameters including atmospheric density, ballistic coefficient, and lift-to-drag ratio are uncertain because of environmental complexity. Ignoring these uncertainties may probably cause negative effects on the navigation accuracy. Based on the desensitized unscented Kalman filter (DUKF), which obtains the state estimation by minimizing a cost function involving the trace of posterior covariance matrix and the weighted norm of the posterior state estimation error sensitivities, this paper further introduces parameter uncertainties into the radio beacons/inertial measurement unit integrated navigation scheme and establishes a robust integrated navigation for Mars atmospheric entry with parameter uncertainties. Numerical simulation results show that the robust navigation algorithm based on the DUKF effectively reduces the influence of parameter uncertainties and illustrates a better performance than traditional methods.

Design and Simulation of the Integrated Navigation System based on Extended Kalman Filter

AbstractThe integrated navigation system is used to estimate the position, velocity, and attitude of a vehicle with the output of inertial sensors. This paper concentrates on the problem of the INS/GPS integrated navigation system design and simulation. The structure of the INS/GPS integrated navigation system is made up of four parts: 1) GPS receiver, 2) Inertial Navigation System, 3) Extended Kalman filter, and 4) Integrated navigation scheme. Afterwards, we illustrate how to simulate the integrated navigation system with the extended Kalman filter by measuring position, velocity and attitude. Particularly, the extended Kalman filter can estimate states of the nonlinear system in the noisy environment. In extended Kalman filter, the estimation of the state vector and the error covariance matrix are computed by steps: 1) time update and 2) measurement update. Finally, the simulation process is implemented by Matlab, and simulation results prove that the error rate of statement measuring is lower when applying the extended Kalman filter in the INS/GPS integrated navigation system.

Self-calibration rank filter for unknown dynamic inputs mitigation during the Mars powered descent phase

Advanced navigation systems for pinpoint landing are required in the entry, descent and landing phase of future missions to Mars. To overcome the horizontal position estimation problem in the Mars powered descent phase, the inertial measurement unit, Doppler radar and surface beacon integrated navigation scheme is proposed, and a conventional filter such as an extended or unscented Kalman filter is adopted. However, in engineering practice, these conventional filters for nonlinear systems with unknown dynamic inputs may degrade or even diverge. The lack of navigation accuracy may result in a large growth of spurious navigations. To solve this problem, based on the rank filter, a self-calibration rank filter is proposed for state estimation of a nonlinear system to mitigate the effects of unknown dynamic inputs. Monte Carlo simulation results are presented to demonstrate the good performance of the self-calibration rank filter for the Mars powered descent navigation. The self-calibration rank filter not only prevents the divergence of the filtering but also significantly improves the state estimation accuracy.

MACV/Radio integrated navigation for Mars powered descent via robust desensitized central difference Kalman filter

An innovative integrated navigation scheme based on MCAV/Radio measurement information during Mars powered descent phase, and a robust desensitized central difference Kalman filter (DCDKF) for systems with uncertain parameters or biases are proposed to improve the navigation descent accuracy. Based on the altitude and velocity information of the Miniature Coherent Altimeter and Velocimeter (MCAV), the radio-range information is added into the integrated navigation system to correct the horizontal position error of the vehicle during the Mars powered descent phase. Based the central difference transform, the sensitivity propagation of the state estimate errors in the DCDKF is described, and a designed desensitized cost function is minimized to obtain the gain matrix of the DCDKF. The performances of the innovative navigation scheme and the proposed DCDKF are all demonstrated by two Monte Carlo simulations with the Inertial Measurement Unit biases during the Mars power descent phase.

Performance enhancement of X-ray pulsar navigation using autonomous optical sensor

This paper develops an integrated navigation method based on the X-ray pulsar navigation (XNAV) system and an autonomous optical navigation system for spacecrafts. The X-ray pulsar navigation is implemented by using the difference between the measured and predicated pulse arrival time, which is calculated by comparing an observed pulse profile with a standard pulse profile. A problem arises from the X-ray signal processing in that the spacecraft’s orbit information, which may be unknown, is required to construct the observed pulse profile. The effect of the spacecraft orbit error on the accuracy of the pulse TOA (time of arrival) difference determination is analyzed. It is specified that the performance of the XNAV system may be degraded in the presence of large orbit error. In order to improve the navigation accuracy, an integrated navigation scheme is presented by fusing the measurement information of a X-ray detector and an ultraviolet optical sensor. The XNAV/optical integrated navigation system is effective to mitigate the effect of the spacecraft orbit error. The superiority of the presented scheme is illustrated through numerical simulations.

XANV/GNSS-Based Integrated Navigation Using a Robust Filtering Algorithm

In order to improve the satellite orbit determination accuracy, an X-ray pulsar-based navigation (XNAV)/Global navigation satellite system (GNSS) integrated navigation scheme is proposed. A global optimal filtering algorithm called robust federated extended Kalman filter (RFEKF) is proposed to fulfill data fusion. In this algorithm, an adaptive extended Kalman filter (EKF) with measurement noise scale factor is derived as the local filters to reduce the influence of measurement malfunctions and enhance the robustness of orbit determination system. The statistical threshold detection scheme based on the residual chi-square test is used for the fault detection. The simulation results demonstrate the validity and feasibility of RFEKF algorithm in case of measurement malfunctions.

Optical Flow Sensor/INS/Magnetometer Integrated Navigation System for MAV in GPS-Denied Environment

The drift of inertial navigation system (INS) will lead to large navigation error when a low-cost INS is used in microaerial vehicles (MAV). To overcome the above problem, an INS/optical flow/magnetometer integrated navigation scheme is proposed for GPS-denied environment in this paper. The scheme, which is based on extended Kalman filter, combines INS and optical flow information to estimate the velocity and position of MAV. The gyro, accelerator, and magnetometer information are fused together to estimate the MAV attitude when the MAV is at static state or uniformly moving state; and the gyro only is used to estimate the MAV attitude when the MAV is accelerating or decelerating. The MAV flight data is used to verify the proposed integrated navigation scheme, and the verification results show that the proposed scheme can effectively reduce the errors of navigation parameters and improve navigation precision.

Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors

This paper presents a non-linear state observer-based integrated navigation scheme for estimating the attitude, position and velocity of micro aerial vehicles (MAV) operating in GPS-denied indoor environments, using the measurements from low-cost MEMS (micro electro-mechanical systems) inertial sensors and an RGB-D camera. A robust RGB-D visual odometry (VO) approach was developed to estimate the MAV’s relative motion by extracting and matching features captured by the RGB-D camera from the environment. The state observer of the RGB-D visual-aided inertial navigation was then designed based on the invariant observer theory for systems possessing symmetries. The motion estimates from the RGB-D VO were fused with inertial and magnetic measurements from the onboard MEMS sensors via the state observer, providing the MAV with accurate estimates of its full six degree-of-freedom states. Implementations on a quadrotor MAV and indoor flight test results demonstrate that the resulting state observer is effective in estimating the MAV’s states without relying on external navigation aids such as GPS. The properties of computational efficiency and simplicity in gain tuning make the proposed invariant observer-based navigation scheme appealing for actual MAV applications in indoor environments.

An innovative high-precision SINS/CNS deep integrated navigation scheme for the Mars rover

The Mars rovers are the key to Mars exploration missions. In order to achieve long-time autonomous roving and perform science operations, Mars rovers need to have the capability of high-precision autonomous navigation. According to the characteristics of the Martian environment, an innovative scheme of strap-down inertial navigation system/celestial navigation system (SINS/CNS) deep integration for the Mars rover and its implementation are proposed. First, the large field of view (FOV) star sensor is utilized to assist the attitude matrix of SINS by continually observing and correcting the misalignment angles and gyro drifts. Thus, the high-precision mathematic reference is obtained. Then, the star sensor makes use of the reference provided by SINS to obtain the local position vector. The rover's position and attitude can be determined by CNS. On the basis of the mathematics reference, the innovative scheme of SINS/CNS deep integration which takes full advantage of the navigation information of each subsystem is presented. Moreover, the errors of inertial measurement unit (IMU) and the navigation parameters in SINS are estimated and corrected. And the deep integration of SINS/CNS is performed. Compared to the optimal integration mode, this scheme improves navigation accuracy. In the end, the simulation results indicate that this scheme is steady and reliable.