An implementation of the cubature Kalman filter for estimating trajectory parameters and air data of a hypersonic vehicle

2019 ◽  
Vol 233 (12) ◽  
pp. 4554-4570
Author(s):  
Zhijian Ding ◽  
Huan Zhou ◽  
Feng Wang ◽  
Dongsheng Wu ◽  
Yingchuan Wu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Inertial Navigation System ◽  
Hypersonic Vehicle ◽  
Positioning System ◽  
System State ◽  
Sensing System ◽  
Cubature Kalman Filter ◽  
Global Positioning ◽  
Kalman Filter Algorithm

Trajectory parameters (including the position, velocity, and attitude angles of a vehicle) and air data (consisting of the flow angles, the Mach number, and the freestream static pressure) are vital data for the analysis and evaluation process in the hypersonic flight tests. This paper describes a data fusion estimation algorithm for a flush air data sensing system/inertial navigation system/global positioning system integrated system, which is used to estimate the trajectory parameters and air data for an unpowered hypersonic vehicle. In the approach, the raw outputs of flush air data sensing system (i.e. the surface pressure measurements) are integrated with global positioning system results (the vehicle’s position and velocity) and inertial navigation system measurements (including the acceleration and the angular velocity measurements) by using a nonlinear Kalman filter algorithm. Firstly, the system state vector is defined with the trajectory parameters, the biases of the inertial sensors and the winds. Then, the system dynamic models are built based on the motion equations of an unpowered hypersonic vehicle, the inertial sensor error models and the wind model. Besides, the system measurement vector is designed with the global positioning system results and the flush air data sensing system raw outputs. Based on these works, the system state is directly estimated by using the cubature Kalman filter algorithm. After that, the air data is calculated based on the estimated values and a high-fidelity model of atmosphere. Simulation cases are implemented to assess the performance of the proposed algorithm. The results show that the proposed method could estimate the trajectory parameters and air data for hypersonic vehicle with a high precision.

scholarly journals A Robust Method of Vehicle Stability Accurate Measurement Using GPS and INS

2015 ◽  
Vol 15 (6) ◽  
pp. 294-303 ◽  
Author(s):  
Zhibin Miao ◽  
Hongtian Zhang ◽  
Jinzhu Zhang
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Vehicle Stability ◽  
Positioning System ◽  
Robust Method ◽  
Sideslip Angle ◽  
Yaw Rate ◽  
Global Positioning

Abstract With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) is a very practical method to get high-precision measurement data. Usually, the Kalman filter is used to fuse the data from GPS and INS. In this paper, a robust method is used to measure vehicle sideslip angle and yaw rate, which are two important parameters for vehicle stability. First, a four-wheel vehicle dynamic model is introduced, based on sideslip angle and yaw rate. Second, a double level Kalman filter is established to fuse the data from Global Positioning System and Inertial Navigation System. Then, this method is simulated on a sample vehicle, using Carsim software to test the sideslip angle and yaw rate. Finally, a real experiment is made to verify the advantage of this approach. The experimental results showed the merits of this method of measurement and estimation, and the approach can meet the design requirements of the vehicle stability controller.

scholarly journals MODEL SISTEM NAVIGASI INERSIAL: SEBUAH TINJAUAN

Oseanika ◽  
2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Muhamad Irfan ◽  
Dwi Haryanto
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Global Navigation Satellite System ◽  
Inertial Navigation System ◽  
Satellite System ◽  
Positioning System ◽  
Navigation Satellite ◽  
Navigation Systems ◽  
Global Positioning ◽  
Global Navigation Satellite

Sistem navigasi merupakan sistem yang memandu wahana gerak dari satu tempat ke tempat lainnya. Ada banyak sistem navigasi yang digunakan baik untuk kepentingan survei maupun untuk kepentingan umum. Sistem navigasi yang sudah dikenal luas adalah sistem navigasi berbasis satelit menggunakan global navigation satellite system (GNSS) atau global positioning system (GPS). GPS mempunyai kelemahan akibat faktor eksternal yakni sangat tergantung pada perambatan sinyal gelombang elektromagnetik dari satelit GPS ke receiver GPS. Sistem navigasi yang lainnya dan belum banyak dikenal namun sudah banyak digunakan adalah sistem navigasi inersial atau INS (inertial navigation system). INS ini merupakan sistem navigasi yang tidak terpengaruh oleh faktor eksternal, karena dibuat dengan mengikuti hukum gerak Newton, dan terdiri dari sensor accelerometer dan gyroscope. Biasanya INS ini dikombinasikan dengan sistem navigasi GPS untuk mendapatkan informasi navigasi yang lengkap dan akurat, yaitu posisi absolut, percepatan, kecepatan, arah, dan kelabilan (attitude) dengan frekuensi pengambilan data yang tinggi. Tulisan ini membahas tentang model dasar INS dari buku “Inertial Navigation Systems with Geodetic Applications” [Jekeli].Kata kunci:navigasi, accelerometer, gyroscope, inersial, GPS, Kalman filter

GPS/INS Integration During GPS Outages Using Machine Learning Augmented with Kalman Filter

2021 ◽  
Vol 16 ◽  
pp. 294-301
Author(s):  
Reshma Verma ◽  
Lakshmi Shrinivasan ◽  
K Shreedarshan
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Global Positioning System ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Supervised Machine Learning ◽  
Positioning System ◽  
Position Information ◽  
Global Positioning

Nowadays a tremendous progress has been witnessed in Global Positioning System (GPS) and Inertial Navigation System (INS). The Global Positioning System provides information as long as there is an unobstructed line of sight and it suffers from multipath effect. To enhance the performance of an integrated Global Positioning System and Inertial Navigation System (GPS/INS) during GPS outages, a novel hybrid fusion algorithm is proposed to provide a pseudo position information to assist the integrated navigation system. A new model that directly relates the velocity, angular rate and specific force of INS to the increments of the GPS position is established. Combined with a Kalman filter the hybrid system is able to predict and estimate a pseud GPS position when GPS signal is unavailable. Field test data are collected to experimentally evaluate the proposed model. In this paper, the obtained GPS/INS datasets are pre-processed and semi-supervised machine learning technique has been used. These datasets are then passed into Kalman filtering for the estimation/prediction of GPS positions which were lost due to GPS outages. Hence, to bridge out the gaps of GPS outages Kalman Filter plays a major role in prediction. The comparative results of Kaman filter and extended Kalman filter are computed. The simulation results show that the GPS positions have been predicted taking into account some factors/measurements of a vehicle, the trajectory of the vehicle, the entire simulation was done using Anaconda (Jupyter Notebook).

scholarly journals In-motion Alignment of a Low-cost GPS/INS under Large Heading Error

2014 ◽  
Vol 68 (2) ◽  
pp. 355-366 ◽  
Author(s):  
Burak H. Kaygısız ◽  
Bekir Şen
Keyword(s):  
Global Positioning System ◽  
Inertial Navigation System ◽  
Low Cost ◽  
Positioning System ◽  
Performance Tests ◽  
Alignment Procedure ◽  
Attitude Error ◽  
Global Positioning ◽  
New Type ◽  
Navigation Accuracy

This paper presents a new type of Global Positioning System/Inertial Navigation System (GPS/INS) providing higher navigation accuracy under large initial heading error. The mechanization introduced is applicable to low cost GPS/INS systems and enhances the performance when the heading error is large. The proposed approach has the capability to decrease large heading errors very quickly and can start the strapdown navigation computations under poor heading accuracy without any special alignment procedure. Although the design is applicable to land, sea and aerial vehicles, a land vehicle is used for the performance tests. The test is conducted around a closed path and the proposed system is compared to a GPS/INS system based on small attitude error assumption. The performance of both systems is given in this paper.

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