scholarly journals A Robust Adaptive Cubature Kalman Filter Based on SVD for Dual-Antenna GNSS/MIMU Tightly Coupled Integration

2021 ◽  
Vol 13 (10) ◽  
pp. 1943
Author(s):  
Cheng Pan ◽  
Nijia Qian ◽  
Zengke Li ◽  
Jingxiang Gao ◽  
Zhenbin Liu ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Satellite System ◽  
Integrated System ◽  
Measurement Unit ◽  
Attitude Angle ◽  
Cubature Kalman Filter ◽  
Tightly Coupled ◽  
Heading Angle ◽  
Robust Adaptive

In complex urban environments, a single Global Navigation Satellite System (GNSS) is often not ideal for navigation due to a lack of sufficient visible satellites. Additionally, the heading angle error of a GNSS/micro-electro-mechanical system–grade inertial measurement unit (MIMU) tightly coupled integration based on the single antenna is large, and the attitude angle, velocity, and position calculated therein all have large errors. Considering the above problems, this paper designs a tightly coupled integration of GNSS/MIMU based on two GNSS antennas and proposes a singular value decomposition (SVD)-based robust adaptive cubature Kalman filter (SVD-RACKF) according to the model characteristics of the integration. In this integration, the high-accuracy heading angle of the carrier is obtained through two antennas, and the existing attitude angle information is used as the observation to constrain the filtering estimation. The proposed SVD-RACKF uses SVD to stabilize the numerical accuracy of the recursive filtering. Furthermore, the three-stage equivalent weight function and the adaptive adjustment factor are constructed to suppress the influence of the gross error and the abnormal state on the parameter estimation, respectively. A set of real measured data was employed for testing and analysis. The results show that dual antennas and dual systems can improve the positioning performance of the integrated system to a certain extent, and the proposed SVD-RACKF can accurately detect the gross errors of the observations and effectively suppress them. Compared with the cubature Kalman filter, the proposed filtering algorithm is more robust, with higher accuracy and reliability of parameter estimation.

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.

Performance evaluation of real-time tightly-coupled GNSS PPP/MEMS-based inertial integration using an improved robust adaptive Kalman filter

2020 ◽  
Vol 14 (4) ◽  
pp. 413-430
Author(s):  
Abdelsatar Elmezayen ◽  
Ahmed El-Rabbany
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Integrated System ◽  
Adaptive Kalman Filter ◽  
Integration Algorithm ◽  
Positioning Accuracy ◽  
Precise Positioning ◽  
The Real ◽  
Tightly Coupled ◽  
Robust Adaptive

AbstractTypically, the extended Kalman filter (EKF) is used for tightly-coupled (TC) integration of multi-constellation GNSS PPP and micro-electro-mechanical system (MEMS) inertial navigation system (INS) to provide precise positioning, velocity, and attitude solutions for ground vehicles. However, the obtained solution will generally be affected by both of the GNSS measurement outliers and the inaccurate modeling of the system dynamic. In this paper, an improved robust adaptive Kalman filter (IRKF) is adopted and used to overcome the effect of the measurement outliers and dynamic model errors on the obtained integrated solution. A real-time IRKF-based TC GPS+Galileo PPP/MEMS-based INS integration algorithm is developed to provide precise positioning and attitude solutions. The pre-saved real-time orbit and clock products from the Centre National d’Etudes Spatials (CNES) are used to simulate the real-time scenario. The performance of the real-time IRKF-based TC GNSS PPP/INS integrated system is assessed under open sky environment, and both of simulated partial and complete GNSS outages through two ground vehicular field trials. It is shown that the real-time TC GNSS PPP/INS integration through the IRKF achieves centimeter-level positioning accuracy under open sky environments and decimeter-level positioning accuracy under GNSS outages that range from 10 to 60 seconds. In addition, the use of IRKF improves the positioning accuracy and enhances the convergence of the integrated solution in comparison with the EKF. Furthermore, the IRKF-based integrated system achieves attitude accuracy of 0.052°, 0.048°, and 0.165° for pitch, roll, and azimuth angles, respectively. This represents improvement of 44 %, 48 %, and 36 % for the pitch, roll, and azimuth angles, respectively, in comparison with the EKF-based counterpart.

scholarly journals Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform

Aerospace ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 280
Author(s):  
Farzan Farhangian ◽  
Hamza Benzerrouk ◽  
Rene Landry
Keyword(s):  
Kalman Filter ◽  
State Space Model ◽  
Flight Test ◽  
Satellite System ◽  
Low Earth Orbit ◽  
Measurement Unit ◽  
Starting Point ◽  
Leo Satellites ◽  
Positioning Method ◽  
Global Navigation Satellite

With the emergence of numerous low Earth orbit (LEO) satellite constellations such as Iridium-Next, Globalstar, Orbcomm, Starlink, and OneWeb, the idea of considering their downlink signals as a source of pseudorange and pseudorange rate measurements has become incredibly attractive to the community. LEO satellites could be a reliable alternative for environments or situations in which the global navigation satellite system (GNSS) is blocked or inaccessible. In this article, we present a novel in-flight alignment method for a strapdown inertial navigation system (SINS) using Doppler shift measurements obtained from single or multi-constellation LEO satellites and a rotation technique applied on the inertial measurement unit (IMU). Firstly, a regular Doppler positioning algorithm based on the extended Kalman filter (EKF) calculates states of the receiver. This system is considered as a slave block. In parallel, a master INS estimates the position, velocity, and attitude of the system. Secondly, the linearized state space model of the INS errors is formulated. The alignment model accounts for obtaining the errors of the INS by a Kalman filter. The measurements of this system are the difference in the outputs from the master and slave systems. Thirdly, as the observability rank of the system is not sufficient for estimating all the parameters, a discrete dual-axis IMU rotation sequence was simulated. By increasing the observability rank of the system, all the states were estimated. Two experiments were performed with different overhead satellites and numbers of constellations: one for a ground vehicle and another for a small flight vehicle. Finally, the results showed a significant improvement compared to stand-alone INS and the regular Doppler positioning method. The error of the ground test reached around 26 m. This error for the flight test was demonstrated in different time intervals from the starting point of the trajectory. The proposed method showed a 180% accuracy improvement compared to the Doppler positioning method for up to 4.5 min after blocking the GNSS.

scholarly journals Comparative Analysis between Error-State and Full-State Error Estimation for KF-Based IMU/GNSS Integration against IMU Faults

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4912 ◽  
Author(s):  
Wei Liu ◽  
Dan Song ◽  
Zhipeng Wang ◽  
Kun Fang
Keyword(s):  
Satellite System ◽  
Position Estimation ◽  
Measurement Unit ◽  
Multisensor Systems ◽  
System Integrity ◽  
Full State ◽  
Tightly Coupled ◽  
Integrity Assurance ◽  
Error State ◽  
State Error

Considering the inertial measurement unit (IMU) faults risk of an unmanned aerial vehicle (UAV), this paper provides an analysis of the error overboundings of position estimation in a tightly coupled IMU/global navigation satellite system (GNSS) integrated architecture under the IMU fault conditions using an error-state EKF-based approach and provides a comparison to a recently published EKF-based full state method. Simulation results show that both the error overboundings of the error-state and full-state EKFs can fit the state error against the IMU faults, but the error-state EKF is more suitable for UAV navigation system integrity assurance due to its higher calculation effciency. This study will be extended to the integrity monitoring of multisensor systems.

scholarly journals Robust INS/SFPPP-BDS tightly coupled navigation algorithm with adaptive cycle slip compensation model

2019 ◽  
Vol 13 ◽  
pp. 174830181983304
Author(s):  
Hangshuai Ma ◽  
Rong Wang ◽  
Zhi Xiong ◽  
Jianye Liu ◽  
Chuanyi Li
Keyword(s):  
Navigation System ◽  
Carrier Phase ◽  
Low Cost ◽  
Integrated System ◽  
Single Frequency ◽  
Cycle Slip ◽  
Phase Measurements ◽  
Satellite Navigation System ◽  
Tightly Coupled ◽  
Robust Adaptive

The application of Beidou Satellite Navigation System (BDS) is developing rapidly. To satisfy the increasing demand for positioning performance, single-frequency precise point positioning (SFPPP) has been a focus in recent years. By introducing the SFPPP technique into the INS/BDS integrated system, higher navigation accuracy can be obtained. Cycle slip, which is caused by signal blockage during the measurement of the carrier phase, is a challenge for SFPPP application. In the INS/SFPPP-BDS integrated system, cycle slip can cause serious bias in BDS carrier phase measurements. In this paper, a new INS/SFBDS-PPP tightly coupled navigation system and a robust adaptive filtering method are proposed. Using a low-cost single-frequency receiver integrated with INS, an observation model was built based on the pseudo range and carrier phase by PPP preprocessing. The cycle slip was introduced into the state vector to improve the estimation precision. The test statistics, comprising the innovation and its covariance, were used to estimate the time at which cycle slip occurred and its amplitude to compensate for its effect on the observation. Finally, the proposed system model and algorithm are validated by simulation.

The Two-Wheeled Self-Balancing Mobile Robot Based on MEMS

2012 ◽  
Vol 468-471 ◽  
pp. 2678-2681
Author(s):  
Hu Sun ◽  
Yun Guo Li ◽  
Xin Biao Li ◽  
Pei Cheng
Keyword(s):  
Kalman Filter ◽  
Inertial Measurement Unit ◽  
Optimal Estimation ◽  
Measurement Unit ◽  
Robot System ◽  
Multiple Sensors ◽  
Attitude Angle ◽  
Wheeled Robot ◽  
Kalman Filter Algorithm ◽  
Sensors Fusion

In this paper, the MIMU( MEMS Inertial Measurement Unit) was used to detect the attitude angle of the two-wheeled robot. By Kalman filter, the optimal estimation of attitude angle was gotten, and which was applied to the balance controlling. In this system, FPGA is chosen as processor, and the embedded kernel was built up based on SOPC. Furthermore, the software of multiple sensors fusion has been developed. The experiment indicates that the design of this robot system is reasonable, and the Kalman filter algorithm can improve the precision of controlling effectively.

scholarly journals Moving Horizon Estimation for GNSS-IMU sensor fusion

2020 ◽  
pp. 112-122
Author(s):  
Guido Sánchez ◽  
Marina Murillo ◽  
Lucas Genzelis ◽  
Nahuel Deniz ◽  
Leonardo Giovanini
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Satellite System ◽  
Measurement Unit ◽  
Moving Horizon Estimation ◽  
Ground Vehicle ◽  
Global Navigation Satellite ◽  
Time Systems ◽  
True Values

The aim of this work is to develop a Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensor fusion system. To achieve this objective, we introduce a Moving Horizon Estimation (MHE) algorithm to estimate the position, velocity orientation and also the accelerometer and gyroscope bias of a simulated unmanned ground vehicle. The obtained results are compared with the true values of the system and with an Extended Kalman filter (EKF). The use of CasADi and Ipopt provide efficient numerical solvers that can obtain fast solutions. The quality of MHE estimated values enable us to consider MHE as a viable replacement for the popular Kalman Filter, even on real time systems.

scholarly journals Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8180
Author(s):  
Jijun Geng ◽  
Linyuan Xia ◽  
Jingchao Xia ◽  
Qianxia Li ◽  
Hongyu Zhu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Step Length ◽  
Indoor Positioning ◽  
Actual Measurement ◽  
Location Service ◽  
Cubature Kalman Filter ◽  
Robust Adaptive ◽  
Pedestrian Dead Reckoning

Indoor localization based on pedestrian dead reckoning (PDR) is drawing more and more attention of researchers in location-based services (LBS). The demand for indoor localization has grown rapidly using a smartphone. This paper proposes a 3D indoor positioning method based on the micro-electro-mechanical systems (MEMS) sensors of the smartphone. A quaternion-based robust adaptive cubature Kalman filter (RACKF) algorithm is proposed to estimate the heading of pedestrians based on magnetic, angular rate, and gravity (MARG) sensors. Then, the pedestrian behavior patterns are distinguished by detecting the changes of pitch angle, total accelerometer and barometer values of the smartphone in the duration of effective step frequency. According to the geometric information of the building stairs, the step length of pedestrians and the height difference of each step can be obtained when pedestrians go up and downstairs. Combined with the differential barometric altimetry method, the optimal height can be computed by the robust adaptive Kalman filter (RAKF) algorithm. Moreover, the heading and step length of each step are optimized by the Kalman filter to reduce positioning error. In addition, based on the indoor map vector information, this paper proposes a heading calculation strategy of the 16-wind rose map to improve the pedestrian positioning accuracy and reduce the accumulation error. Pedestrian plane coordinates can be solved based on the Pedestrian Dead-Reckoning (PDR). Finally, combining pedestrian plane coordinates and height, the three-dimensional positioning coordinates of indoor pedestrians are obtained. The proposed algorithm is verified by actual measurement examples. The experimental verification was carried out in a multi-story indoor environment. The results show that the Root Mean Squared Error (RMSE) of location errors is 1.04–1.65 m by using the proposed algorithm for three participants. Furthermore, the RMSE of height estimation errors is 0.17–0.27 m for three participants, which meets the demand of personal intelligent user terminal for location service. Moreover, the height parameter enables users to perceive the floor information.

scholarly journals Integration of GNSS-IMU for increasing the observation accuracy in Condensed Areas (Infrastructure and Forest Canopies)

2019 ◽  
Vol 94 ◽  
pp. 03015
Author(s):  
Mokhamad Nur Cahyadi ◽  
Irene Rwabudandi
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Kalman Filters ◽  
Measurement Unit ◽  
Gps Receiver ◽  
Satellite Navigation System ◽  
Loosely Coupled ◽  
Advantages And Disadvantages ◽  
Gps Satellites ◽  
Tightly Coupled

Position determination using satellite navigation system has grown significantly. It provides geospatial with global coverage called GNSS (Global Navigation System Satellite). GNSS satellites consists of GLONASS, GPS (Global positioning system) and Galileo.GPS is the most commonly used system and it is known to its capability to determine 3D position on the surface of the earth. In order to determine the position, a GPS receiver must be able to receive signals from at least four GPS satellites. However, the determination of position in condensed areas such as tunnels, area surrounded by high rise buildings, highly forested and in other closely-knit areas is not achieved because satellite signals cannot reach the receiver in the above-mentioned areas and also others where the signals are reflected before being received by a GPS receiver. In this paper, we present the algorithm to fuse GPS and the inertial measurement unit (IMU) to enable positioning in the above-mentioned Condensed Areas. The standard deviations of the two measurements show that GPS-IMU is better than GPS alone, the standard deviation when satellite outages occurred is - 4.57475 for GPS-IMU measurements and 0.218675 for GPS observations. We presented the results in graphics and it shows that GPS measurements are easily disturbed by external influence such as multipath but GPS-IMU graphic is continuous and robust. The advantages and disadvantages of GPS and INS are complementary and make them work together to enable the accurate measurements in the areas mentioned above. Integration of INS and GNSS can be classified into three types, loosely coupled Kalman filter, tightly coupled Kalman filters and ultratight coupled Kalman filter. In this research we used loosely coupled Kalman filter and tightly coupled Kalman filters to combine GPS and INS in one system.

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