Performance evaluation of Cubature Kalman filter in a GPS/IMU tightly-coupled navigation system

2016 ◽  
Vol 119 ◽  
pp. 67-79 ◽  
Author(s):  
Yingwei Zhao
Keyword(s):  
Performance Evaluation ◽  
Kalman Filter ◽  
Navigation System ◽  
Cubature Kalman Filter ◽  
Tightly Coupled

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 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.

Online cubature Kalman filter Rauch–Tung–Striebel smoothing for indoor inertial navigation system/ultrawideband integrated pedestrian navigation

2017 ◽  
Vol 232 (4) ◽  
pp. 390-398 ◽  
Author(s):  
Yuan Xu ◽  
Xiyuan Chen
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Position Error ◽  
Smoothing Algorithm ◽  
Position Information ◽  
The North ◽  
Proposed Model ◽  
Cubature Kalman Filter

Accurate position information of the pedestrians is required in many applications such as healthcare, entertainment industries, and military field. In this work, an online Cubature Kalman filter Rauch–Tung–Striebel smoothing algorithm for people’s location in indoor environment is proposed using inertial navigation system techniques with ultrawideband technology. In this algorithm, Cubature Kalman filter is employed to improve the filtering output accuracy; then, the Rauch–Tung–Striebel smoothing is used between the ultrawideband measurements updates; finally, the average value of the corrected inertial navigation system error estimation is output to compensate the inertial navigation system position error. Moreover, a real indoor test has been done for assessing the performance of the proposed model and algorithm. Test results show that the proposed model is able to reduce the sum of the absolute position error between the east direction and the north direction by about 32% compared with only the ultrawideband model, and the performance of the online Cubature Kalman filter Rauch–Tung–Striebel smoothing algorithm is slightly better than the off-line mode.

scholarly journals A Novel Design for the Ultra-Tightly Coupled GPS/INS Navigation System

2012 ◽  
Vol 65 (4) ◽  
pp. 717-747 ◽  
Author(s):  
Dah-Jing Jwo ◽  
Chi-Fan Yang ◽  
Chih-Hsun Chuang ◽  
Kun-Chieh Lin
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Doppler Frequency ◽  
Superior Performance ◽  
Gps Tracking ◽  
Doppler Velocity ◽  
Tightly Coupled ◽  
Tracking Loops ◽  
Number Of Particles

This paper presents a sensor fusion method for the Ultra-Tightly Coupled (UTC) Global Positioning System (GPS)/Inertial Navigation System (INS) integrated navigation. The UTC structure, also known as the deep integration, exhibits many advantages, e.g., disturbance and multipath rejection capability, improved tracking capability for dynamic scenarios and weak signals, and reduction of acquisition time. This architecture involves the integration of I (in-phase) and Q (quadrature) components from the correlator of a GPS receiver with the INS data. The Particle Filter (PF) exhibits superior performance as compared to an Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) in state estimation for the nonlinear, non-Gaussian system. To handle the problem of heavy-tailed probability distribution, one of the strategies is to incorporate the UKF into the PF as the proposal distribution, leading to the Unscented Particle Filter (UPF). The combination of an adaptive UPF and Fuzzy Logic Adaptive System (FLAS) is adopted for reducing the number of particles with sufficiently good results. The GPS tracking loops may lose lock due to the signals being weak, subjected to excessive dynamics or completely blocked. One of the principal advantages of the UTC structure is that a Doppler frequency derived from the INS is integrated with the tracking loops to improve the receiver tracking capability. The Doppler frequency shift is calculated and fed to the GPS tracking loops for elimination of the effect of stochastic errors caused by the Doppler frequency. In this paper, several nonlinear filtering approaches, including EKF, UKF, UPF and ‘FLAS assisted UPF’ (FUPF), are adopted for performance comparison for ultra-tight integration of GPS and INS. It is assumed that no outage occurs such that the inertial sensor errors can be properly corrected and accordingly the aiding information is working well. Two examples are provided for performance assessment for the various data fusion methods. The FUPF algorithm with Doppler velocity aiding demonstrates remarkable improvement, especially in the high dynamic environments, in navigation estimation accuracy with reduction of number of particles.

scholarly journals Application of MCC-based Robust High-degree Cubature Kalman Filter in Integrated Navigation System

2019 ◽  
Vol 1168 (6) ◽  
pp. 062029
Author(s):  
Ming-qi Xu ◽  
Guo-rong Huang ◽  
Hang Lu ◽  
Zhi-ying Peng ◽  
Shun-yi Hao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Cubature Kalman Filter ◽  
High Degree
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