scholarly journals Performance Analysis and Architectures for a MEMS-SINS/GPS Ultratight Integration System

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jianxin Ren ◽  
Junlin Zi ◽  
Hao Yang ◽  
Jin Li
Keyword(s):  
Navigation System ◽  
Dynamic Performance ◽  
Measurement Model ◽  
Integration Scheme ◽  
Integration System ◽  
Microelectromechanical System ◽  
Time Space ◽  
Assisted Gps ◽  
Integration Navigation ◽  
High Dynamics

In order to analyze the performance of strapdown inertial navigation system/global position system (SINS/GPS) ultratight integration system with low-precision microelectromechanical system (MEMS) under challenging environments, a new MEMS-SINS/GPS ultratight integration scheme is designed. The time-space difference carrier phase velocity (TSDCP-v) is used to assist the carrier tracking loop, the measurement model including nonlinear term is established, and the corresponding filtering algorithm is designed. A simulation and verification platform is established to analyze and verify the performance of the MEMS-SINS/GPS ultratight integration system designed in this paper. Compared with the SINS/GPS tight integration navigation system, the MEMS-SINS/GPS ultratight integration system has higher dynamic performance, anti-interference capability, and navigation performance. At the same time, the MEMS-SINS/GPS ultratight integration system improves the carrier tracking performance of SINS-assisted GPS ultratight integration system when using low-precision MEMS and in high dynamics, strong interference environments.

scholarly journals A New Efficient Filtering Model for GPS/SINS Ultratight Integration System

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chaochen Wang ◽  
Yuming Bo ◽  
Changhui Jiang
Keyword(s):  
Navigation System ◽  
The State ◽  
Integrated System ◽  
State Model ◽  
Integration System ◽  
Filter Model ◽  
Integration Mode ◽  
Measurement Vector ◽  
Computation Load ◽  
Vector Dimension

Global Positioning System (GPS) and strap-down inertial navigation system (SINS) are recognized as highly complementary and widely employed in the community. The GPS has the advantage of providing precise navigation solutions without divergence, but the GPS signals might be blocked and attenuated. The SINS is a totally self-contained navigation system which is hardly disturbed. The GPS/SINS integration system could utilize the advantages of both the GPS and SINS and provide more reliable navigation solutions. According to the data fusion strategies, the GPS/SINS integrated system could be divided into three different modes: loose, tight, and ultratight integration (LI, TI, and UTC). In the loose integration mode, position and velocity difference from the GPS and SINS are employed to compose measurement vector, in which the vector dimension has nothing to do with the amount of the available satellites. However, in the tight and ultratight modes, difference of pseudoranges and pseudorange rates from the GPS and SINS are employed to compose the measurement vector, in which the measurement vector dimension increases with the amount of available satellites. In addition, compared with the loose integration mode, clock bias and drift are included in the integration state model. The two characteristics magnify the computation load of the tight and ultratight modes. In this paper, a new efficient filter model was proposed and evaluated. Two schemes were included in this design for reducing the computation load. Firstly, a difference between pseudorange measurements was determined, by which clock bias and drift were excluded from the integration state model. This step reduced the dimension of the state vector. Secondly, the integration filter was divided into two subfilters: pseudorange subfilter and pseudorange rate subfilter. A federated filter was utilized to estimate the state errors optimally. In the second step, the two subfilters could run in parallel and the measurement vector was divided into two subvectors with lower dimension. A simulation implemented in MATLAB software was conducted to evaluate the performance of the new efficient integration method in UTC. The simulation results showed that the method could reduce the computation load with the navigation solutions almost unchanged.

scholarly journals Robust Adaptive Cubature Kalman Filter and Its Application to Ultra-Tightly Coupled SINS/GPS Navigation System

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2352 ◽  
Author(s):  
Xin Zhao ◽  
Jianli Li ◽  
Xunliang Yan ◽  
Shaowen Ji
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Kinematic Model ◽  
Tracking Performance ◽  
Typical Application ◽  
Gross Error ◽  
Cubature Kalman Filter ◽  
Tightly Coupled ◽  
Robust Adaptive ◽  
High Dynamics

In this paper, we propose a robust adaptive cubature Kalman filter (CKF) to deal with the problem of an inaccurately known system model and noise statistics. In order to overcome the kinematic model error, we introduce an adaptive factor to adjust the covariance matrix of state prediction, and process the influence introduced by dynamic disturbance error. Aiming at overcoming the abnormality error, we propose the robust estimation theory to adjust the CKF algorithm online. The proposed adaptive CKF can detect the degree of gross error and subsequently process it, so the influence produced by the abnormality error can be solved. The paper also studies a typical application system for the proposed method, which is the ultra-tightly coupled navigation system of a hypersonic vehicle. Highly dynamical scene experimental results show that the proposed method can effectively process errors aroused by the abnormality data and inaccurate model, and has better tracking performance than UKF and CKF tracking methods. Simultaneously, the proposed method is superior to the tracing method based on a single-modulating loop in the tracking performance. Thus, the stable and high-precision tracking for GPS satellite signals are preferably achieved and the applicability of the system is promoted under the circumstance of high dynamics and weak signals. The effectiveness of the proposed method is verified by a highly dynamical scene experiment.

scholarly journals Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas

2020 ◽  
Vol 12 (10) ◽  
pp. 1686 ◽  
Author(s):  
Xiwei Bai ◽  
Weisong Wen ◽  
Li-Ta Hsu
Keyword(s):  
Navigation System ◽  
Urban Areas ◽  
Feature Tracking ◽  
Measurement Model ◽  
Integrated Navigation ◽  
Dynamic Features ◽  
Integrated Navigation System ◽  
M Estimator ◽  
Dynamic Objects

The visual-inertial integrated navigation system (VINS) has been extensively studied over the past decades to provide accurate and low-cost positioning solutions for autonomous systems. Satisfactory performance can be obtained in an ideal scenario with sufficient and static environment features. However, there are usually numerous dynamic objects in deep urban areas, and these moving objects can severely distort the feature-tracking process which is critical to the feature-based VINS. One well-known method that mitigates the effects of dynamic objects is to detect vehicles using deep neural networks and remove the features belonging to surrounding vehicles. However, excessive feature exclusion can severely distort the geometry of feature distribution, leading to limited visual measurements. Instead of directly eliminating the features from dynamic objects, this study proposes to adopt the visual measurement model based on the quality of feature tracking to improve the performance of the VINS. First, a self-tuning covariance estimation approach is proposed to model the uncertainty of each feature measurement by integrating two parts: (1) the geometry of feature distribution (GFD); (2) the quality of feature tracking. Second, an adaptive M-estimator is proposed to correct the measurement residual model to further mitigate the effects of outlier measurements, like the dynamic features. Different from the conventional M-estimator, the proposed method effectively alleviates the reliance on the excessive parameterization of the M-estimator. Experiments were conducted in typical urban areas of Hong Kong with numerous dynamic objects. The results show that the proposed method could effectively mitigate the effects of dynamic objects and improved accuracy of the VINS is obtained when compared with the conventional VINS method.

scholarly journals An Integration Method of Inertial Navigation System and Three-Beam Lidar for the Precision Landing

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyue Zhang ◽  
Pengbo Liu ◽  
Chunxi Zhang
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Integration Method ◽  
Inertial Navigation ◽  
Measurement Model ◽  
Reconstruction Method ◽  
Integrated Navigation ◽  
Fault Resistance ◽  
Vehicle Test

To ensure the high accuracy, independence, and reliability of the measurement system in the unmanned aerial vehicle (UAV) landing process, an integration method of inertial navigation system (INS) and the three-beam Lidar is proposed. The three beams of Lidar are, respectively, regarded as an independent sensor to integrate with INS according to the conception of multisensor fusion. Simultaneously, the fault-detection and reconstruction method is adopted to enhance the reliability and fault resistance. First the integration method is described. Then the strapdown inertial navigation system (SINS) error model is introduced and the measurement model of SINS/Lidar integrated navigation is deduced under Lidar reference coordinate. The fault-detection and reconstruction method is introduced. Finally, numerical simulation and vehicle test are carried out to demonstrate the validity and utility of the proposed method. The results indicate that the integration can obtain high precision navigation information and the system can effectively distinguish the faults and accomplish the reconstruction to guarantee the normal navigation when one or two beams of the Lidar malfunction.

MEMS IMU and GPS/Beidou Integration Navigation System Using Interval Kalman Filter

2014 ◽  
Vol 568-570 ◽  
pp. 970-975 ◽  
Author(s):  
Yang Le ◽  
Xiu Feng He ◽  
Ru Ya Xiao
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Integrated System ◽  
Integrated Navigation ◽  
Dynamics Modeling ◽  
Integrated Navigation System ◽  
Uncertain Dynamics ◽  
Integration Navigation ◽  
Mems Imu

This paper describes an integrated MEMS IMU and GNSS system for vehicles. The GNSS system is developed to accurately estimate the vehicle azimuth, and the integrated GNSS/IMU provides attitude, position and velocity. This research is aimed at producing a low-cost integrated navigation system for vehicles. Thus, an inexpensive solid-state MEMS IMU is used to smooth the noise and to provide a high bandwidth response. The integration system with uncertain dynamics modeling and uncertain measurement noise is also studied. An interval adaptive Kalman filter is developed for such an uncertain integrated system, since the standard extended Kalman filter (SKF) is no longer applicable, and a method of adaptive factor construction with uncertain parameter is proposed for the nonlinear integrated GNSS/IMU system. The results from the actual GNSS/IMU integrated system indicate that the filtering method proposed is effective.

scholarly journals Design and Analysis of Avionic Structures For Aircraft Applications

2020 ◽  
Vol 6 (5) ◽  
pp. 78-83
Author(s):  
P H J Venkatesh ◽  
M S R Viswanath
Keyword(s):  
Experimental Analysis ◽  
Navigation System ◽  
System Performance ◽  
Random Vibration ◽  
Inertial Navigation System ◽  
Mechanical Support ◽  
Static And Dynamic Analysis ◽  
Solid Works ◽  
High Dynamics ◽  
Gps System

The Avionic enclosure is a electronic packed setup which are used in aircrafts and spacecrafts. Avionic enclosure is used for mechanical support to all the system elements and this is mechanically interfaced with the aircrafts. The avionic enclosures is a key role for the system performance. The avionic package has to be designed to withstand high dynamics. FINGS(FIBRE OPTIC GYRO BASED INERTIAL NAVIGATION SYSTEM) is a unit of aircraft for finding the navigation. In this paper the FINGS + GPS SYSTEM (FINGS) unit designed using SOLID WORKS and The Modal Analysis on these parts was carried out using ABAQUS FEA software and a random vibration experimental analysis is tested under both static and dynamic analysis. The obtained results are compared other for the design optimization of FINGS package.

scholarly journals ADAPTIVE STRATEGY-BASED TIGHTLY-COUPLED INS/GNSS INTEGRATION SYSTEM AIDED BY ODOMETER AND BAROMETER

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 881-888 ◽  
Author(s):  
Y. C. Tien ◽  
Y. L. Chen ◽  
K. W. Chiang
Keyword(s):  
Experimental Validation ◽  
Fundamental System ◽  
Adaptive Strategy ◽  
Measurement Model ◽  
Satellite System ◽  
Integration System ◽  
Loosely Coupled ◽  
Navigation Algorithm ◽  
Tightly Coupled ◽  
Global Navigation Satellite

<p><strong>Abstract.</strong> Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) integration system have been widely applied in recent years. Unfortunately, it sometimes malfunctions and the performance heavily deteriorates, especially in urban area where signals from satellites may be blocked or reflected by modern buildings. In multipath or Non Light-of-sight (NLOS) environment, incorrect signal results in poor observability of GNSS measurement model in Kalman Filter (KF). For purpose of addressing the issue, we proposed an adaptive strategy-based tightly-coupled INS/GNSS integration system aided by odometer and barometer, targeting to mitigate the error from poor observability. In this method, tightly-coupled (TC) scheme is implemented as the fundamental system in order to increase the reliability and stability. TC is more suitable than Loosely-coupled (LC), the traditional scheme, in urban navigation because it requires less visible GNSS measurement and it overcomes the disadvantage of LC, and further enhances the navigation result. Furthermore, aiding sensors such as odometer and barometer are integrated in this system as well, serving as velocity and height constraints respectively. Since the precision of GNSS positioning depends on the properties of the environment, measurement model of KF must work adaptively. Thus, innovation-based Adaptive Scaled Estimation (IASE) and Residual-based Adaptive Scaled Estimation (RASE), are also implemented to improve navigation performance in this paper. Finally, from the experimental validation, the proposed adaptive sensor-fusion navigation algorithm significantly enhanced the performance. The improvement was approximate 80% compared with the pure TC scheme; the RMSE can reach 6m in 3D and 2.5&amp;thinsp;m in vertical.</p>

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