scholarly journals A Distributed Radio Beacon/IMU/Altimeter Integrated Localization Scheme with Uncertain Initial Beacon Locations for Lunar Pinpoint Landing

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5643
Author(s):  
Rongjun Mu ◽  
Yuntian Li ◽  
Rubin Luo ◽  
Bingzhi Su ◽  
Yongzhi Shan
Keyword(s):  
Estimation Accuracy ◽  
Measurement Unit ◽  
Iteration Algorithm ◽  
Navigation Systems ◽  
Potential Candidate ◽  
Hybrid Filter ◽  
Hybrid Form ◽  
Localization Scheme ◽  
Information Filter ◽  
Radio Beacon

As a growing number of exploration missions have successfully landed on the Moon in recent decades, ground infrastructures, such as radio beacons, have attracted a great deal of attention in the design of navigation systems. None of the available studies regarding integrating beacon measurements for pinpoint landing have considered uncertain initial beacon locations, which are quite common in practice. In this paper, we propose a radio beacon/inertial measurement unit (IMU)/altimeter localization scheme that is sufficiently robust regarding uncertain initial beacon locations. This scheme was designed based on the sparse extended information filter (SEIF) to locate the lander and update the beacon configuration at the same time. Then, an adaptive iterated sparse extended hybrid filter (AISEHF) was devised by modifying the prediction and update stage of SEIF with a hybrid-form propagation and a damping iteration algorithm, respectively. The simulation results indicated that the proposed method effectively reduced the error in the position estimations caused by uncertain beacon locations and made an effective trade-off between the estimation accuracy and the computational efficiency. Thus, this method is a potential candidate for future lunar exploration activities.

scholarly journals Specialized Algorithm for Navigation of a Micro Hopping Air Vehicle Using Only Inertial Sensors

2012 ◽  
Vol 135 (2) ◽  
Author(s):  
Edward Scheuermann ◽  
Mark Costello
Keyword(s):  
Data Stream ◽  
Inertial Sensors ◽  
Micro Air Vehicles ◽  
Positional Information ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Navigation Solution ◽  
Air Vehicle ◽  
Vehicle Position

The need for accurate and reliable navigation techniques for micro air vehicles plays an important part in enabling autonomous operation. Traditional navigation systems typically rely on periodic global positioning system updates and provide little benefit when operating indoors or in other similarly shielded environments. Moreover, direct integration of the onboard inertial measurement unit data stream often results in substantial drift errors yielding virtually unusable positional information. This paper presents a new strategy for obtaining an accurate navigation solution for the special case of a micro hopping air vehicle, beginning from some known location and heading, using only one triaxial accelerometer and one triaxial gyroscope. Utilizing the unique dynamics of the hopping vehicle, a piece-wise navigation solution is constructed by selectively integrating the inertial data stream for only those short periods of time while the vehicle is airborne. Interhop data post processing and sensor bias recalibration are also used to further improve estimation accuracy. To assess the performance of the proposed algorithm, a series of tests were conducted in which the estimated vehicle position following a sequence of 10 consecutive hops was compared with measurements from an optical motion-capture system. On average, the final estimated vehicle position was within 0.70 m or just over 6% from its actual location based on a total traveled distance of approximately 11 m.

scholarly journals Mixed-Degree Cubature H∞ Information Filter-Based Visual-Inertial Odometry

2018 ◽  
Vol 9 (1) ◽  
pp. 56 ◽  
Author(s):  
Chunlin Song ◽  
Xiaogang Wang ◽  
Naigang Cui
Keyword(s):  
Estimation Method ◽  
Visual Navigation ◽  
Mobile Robot Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Cubature Rule ◽  
Information Filter ◽  
Dual Sensor ◽  
Tightly Coupled ◽  
Non Gaussian

Visual–inertial odometry is an effective system for mobile robot navigation. This article presents an egomotion estimation method for a dual-sensor system consisting of a camera and an inertial measurement unit (IMU) based on the cubature information filter and H∞ filter. The intensity of the image was used as the measurement directly. The measurements from the two sensors were fused with a hybrid information filter in a tightly coupled way. The hybrid filter used the third-degree spherical-radial cubature rule in the time-update phase and the fifth-degree spherical simplex-radial cubature rule in the measurement-update phase for numerical stability. The robust H∞ filter was combined into the measurement-update phase of the cubature information filter framework for robustness toward non-Gaussian noises in the intensity measurements. The algorithm was evaluated on a common public dataset and compared to other visual navigation systems in terms of absolute and relative accuracy.

scholarly journals Vehicle Sideslip Angle Estimation Using Two Single-Antenna GPS Receivers

2010 ◽  
Author(s):  
Jong-Hwa Yoon ◽  
Huei Peng
Keyword(s):  
Low Cost ◽  
Longitudinal Velocity ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Sideslip Angle ◽  
Gps Receivers ◽  
Single Antenna ◽  
Active Safety Systems ◽  
Safety Systems

Knowing vehicle sideslip angle accurately is critical for active safety systems such as Electronic Stability Control (ESC). Vehicle sideslip angle can be measured through optical speed sensors, or dual-antenna GPS. These measurement systems are costly (∼$5k to $100k), which prohibits wide adoption of such systems. This paper demonstrates that the vehicle sideslip angle can be estimated in real-time by using two low-cost single-antenna GPS receivers. Fast sampled signals from an Inertial Measurement Unit (IMU) compensate for the slow update rate of the GPS receivers through an Extended Kalman Filter (EKF). Bias errors of the IMU measurements are estimated through an EKF to improve the sideslip estimation accuracy. A key challenge of the proposed method lies in the synchronization of the two GPS receivers, which is achieved through an extrapolated update method. Analysis reveals that the estimation accuracy of the proposed method relies mainly on vehicle yaw rate and longitudinal velocity. Experimental results confirm the feasibility of the proposed method.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

INS/GNSS Integration with Account for Timing Skew and Displacement of GNSS Antenna. Experience of Practical Realization

2021 ◽  
Vol 29 (3) ◽  
pp. 52-68
Author(s):  
N.B. Vavilova ◽  
◽  
A.A. Golovan ◽  
A.V. Kozlov ◽  
I.A. Papusha ◽  
...  
Keyword(s):  
Spatial Separation ◽  
Measurement Unit ◽  
Complex Data ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Positioning Systems ◽  
Global Positioning ◽  
Antenna Phase ◽  
Antenna Phase Center ◽  
Timing Skew

We examine two aspects specific to complex data fusion algorithms in integrated strapdown inertial navigation systems aided by global positioning systems, with their inherent spatial separation between the GNSS antenna phase center and the inertial measurement unit, as well as with the timing skew between their measurements. The first aspect refers to modifications of mathematical models used in INS/GNSS integration. The second one relates to our experience in their application in onboard airborne navigation algorithms developed by Moscow Institute of Electromechanics and Automatics.

scholarly journals Scalar Method of Fault Diagnosis of Inertial Measurement Unit

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Vadym Avrutov
Keyword(s):  
Fault Diagnosis ◽  
Inertial Measurement Unit ◽  
Calibration Method ◽  
Quality Monitoring ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Working Capacity ◽  
Element Determination ◽  
Inertial Measurement ◽  
Algorithm Verification

The scalar method of fault diagnosis systems of the inertial measurement unit (IMU) is described. All inertial navigation systems consist of such IMU. The scalar calibration method is a base of the scalar method for quality monitoring and diagnostics. In accordance with scalar calibration method algorithms of fault diagnosis systems are developed. As a result of quality monitoring algorithm verification is implemented in the working capacity monitoring of IMU. A failure element determination is based on diagnostics algorithm verification and after that the reason for such failure is cleared. The process of verifications consists of comparison of the calculated estimations of biases, scale factor errors, and misalignments angles of sensors to their data sheet certificate, kept in internal memory of computer. As a result of such comparison the conclusion for working capacity of each IMU sensor can be made and also the failure sensor can be determined.

scholarly journals Thermal Modeling and Calibration Method in Complex Temperature Field for Single-Axis Rotational Inertial Navigation System

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 384 ◽  
Author(s):  
Zihui Wang ◽  
Xianghong Cheng ◽  
Jingjing Du
Keyword(s):  
Temperature Field ◽  
Inertial Navigation ◽  
Calibration Method ◽  
Measurement Unit ◽  
Calibration Model ◽  
Navigation Systems ◽  
Moving Vehicle ◽  
Temperature Change Rate ◽  
Environment Temperature ◽  
Complex Temperature

Single-axis rotational inertial navigation systems (single-axis RINSs) are widely used in high-accuracy navigation because of their ability to restrain the horizontal axis errors of the inertial measurement unit (IMU). The IMU errors, especially the biases, should be constant during each rotation cycle that is to be modulated and restrained. However, the temperature field, consisting of the environment temperature and the power heating of single-axis RINS, affects the IMU performance and changes the biases over time. To improve the precision of single-axis RINS, the change of IMU biases caused by the temperature should be calibrated accurately. The traditional thermal calibration model consists of the temperature and temperature change rate, which does not reflect the complex temperature field of single-axis RINS. This paper proposed a multiple regression method with a temperature gradient in the model, and in order to describe the complex temperature field thoroughly, a BP neural network method is proposed with consideration of the coupled items of the temperature variables. Experiments show that the proposed methods outperform the traditional calibration method. The navigation accuracy of single-axis RINS can be improved by up to 47.41% in lab conditions and 65.11% in the moving vehicle experiment, respectively.

Robust high-degree cubature information filter and its application to trajectory estimation for ballistic missile

2017 ◽  
Vol 232 (12) ◽  
pp. 2364-2377 ◽  
Author(s):  
Xiaogang Wang ◽  
Wutao Qin ◽  
Naigang Cui ◽  
Yu Wang
Keyword(s):  
State Estimation ◽  
Estimation Algorithm ◽  
Ballistic Missile ◽  
Measurement Noise ◽  
Estimation Accuracy ◽  
Trajectory Estimation ◽  
Information Filter ◽  
Non Gaussian ◽  
Time Update ◽  
High Degree

This paper presents a new recursive filter algorithm, the robust high-degree cubature information filter, which can provide reliable state estimation in the presence of non-Gaussian measurement noise. The novel algorithm is developed in the framework of the conventional information filter. The fifth-degree Cubature rule is utilized to improve the estimation accuracy and numerical stability during the time update, while the Huber technique is adopted in the measurements update stage. As the Huber technique is a combined minimum l1 and l2 norm estimation algorithm, the proposed algorithm could exhibit robustness to the non-Gaussian measurement noise, especially the glint noise. In addition, Monte Carlo simulation and the trajectory estimation for ballistic missile experiments demonstrate that the robust high-degree cubature information filter can provide improved state estimation performance over extended information filter and high-degree cubature information filter.

Precision navigation system design for Moon penetrator

2016 ◽  
Vol 88 (6) ◽  
pp. 791-798
Author(s):  
Xiaogang Wang ◽  
Wutao Qin ◽  
Yuliang Bai ◽  
Naigang Cui
Keyword(s):  
System Design ◽  
Kalman Filtering ◽  
Navigation System ◽  
Estimation Accuracy ◽  
Line Of Sight ◽  
Accurate Estimation ◽  
Navigation Systems ◽  
Vision Sensor ◽  
Content Type ◽  
Sigma Point

Purpose Penetrator plays an important role in the exploration of Moon and Mars. The navigation method is a key technology during the development of penetrator. To meet the high accuracy requirements of Moon penetrator, this paper aims to propose two kinds of navigation systems. Design/methodology/approach The line of sight of vision sensor between the penetrator and Moon orbiter could be utilized as the measurement during the navigation system design. However, the analysis of observability shows that the navigation system cannot estimate the position and velocity of penetrator, when the line of sight measurement is the only resource of information. Therefore, the Doppler measurement due to the relative motion between penetrator and the orbiter is used as the supplement. The other option is the relative range measurement between penetrator and the orbiter. The sigma-point Kalman Filtering is implemented to fuse the information from the vision sensor and Doppler or rangefinder. The observability of two navigation system is analyzed. Findings The sigma-point Kalman filtering could be used based on vision sensor and Doppler radar or laser rangefinder to give an accurate estimation of Moon penetrator position and velocity without increasing the payload of Moon penetrator or decreasing the estimation accuracy. However, the simulation result shows that the last method is better. The observability analysis also proves this conclusion. Practical implications Two navigation systems are proposed, and the simulations show that both systems can provide accurate estimation of states of penetrator. Originality/value Two navigation methods are proposed, and the observability of these navigation systems is analyzed. The sigma-point Kalman filtering is first introduced to the vision-based navigation system for Moon penetrator to provide precision navigation during the descent phase of Moon penetrator.

scholarly journals Obtaining World Coordinate Information of UAV in GNSS Denied Environments

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2241 ◽  
Author(s):  
Chengbin Chen ◽  
YaoYuan Tian ◽  
Liang Lin ◽  
SiFan Chen ◽  
HanWen Li ◽  
...  
Keyword(s):  
Visual Servoing ◽  
Field Tests ◽  
Visual Navigation ◽  
Sensor Data ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Complex Environments ◽  
External Interference ◽  
Position Information ◽  
Self Powered

GNSS information is vulnerable to external interference and causes failure when unmanned aerial vehicles (UAVs) are in a fully autonomous flight in complex environments such as high-rise parks and dense forests. This paper presents a pan-tilt-based visual servoing (PBVS) method for obtaining world coordinate information. The system is equipped with an inertial measurement unit (IMU), an air pressure sensor, a magnetometer, and a pan-tilt-zoom (PTZ) camera. In this paper, we explain the physical model and the application method of the PBVS system, which can be briefly summarized as follows. We track the operation target with a UAV carrying a camera and output the information about the UAV’s position and the angle between the PTZ and the anchor point. In this way, we can obtain the current absolute position information of the UAV with its absolute altitude collected by the height sensing unit and absolute geographic coordinate information and altitude information of the tracked target. We set up an actual UAV experimental environment. To meet the calculation requirements, some sensor data will be sent to the cloud through the network. Through the field tests, it can be concluded that the systematic deviation of the overall solution is less than the error of GNSS sensor equipment, and it can provide navigation coordinate information for the UAV in complex environments. Compared with traditional visual navigation systems, our scheme has the advantage of obtaining absolute, continuous, accurate, and efficient navigation information at a short distance (within 15 m from the target). This system can be used in scenarios that require autonomous cruise, such as self-powered inspections of UAVs, patrols in parks, etc.

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