Covariance matrix transformation method in all-earth integrated navigation considering coordinate frame conversion

2022 ◽  
Author(s):  
yongjian zhang ◽  
Lin Wang ◽  
Guo Wei ◽  
Xudong Yu ◽  
Chunfeng Gao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Navigation System ◽  
Transformation Method ◽  
Satellite System ◽  
Open Loop ◽  
Matrix Transformation ◽  
Coordinate Frame ◽  
Navigation Systems ◽  
Integrated Navigation

Abstract In the exploration of polar region, navigation is one of the most important issues to be resolved. To avoid the limitations of single navigation coordinate frame, the navigation systems usually use different navigation coordinate frames in polar and nonpolar region, such as the north-oriented geographic frame and the grid frame. However, the error states and covariance matrix are related with the definition of navigation coordinate frame, since the coordinate frame conversion will cause the integrated navigation Kalman filter overshoot and error discontinuity. To solve this problem, the transformation relationship of error states defined in different frames is deduced, whereby the covariance matrix transformation relationship is also analyzed. On this basis, covariance transformation-based the open-loop and the closed-loop Kalman filter integrated navigation algorithms are proposed. The effectiveness of algorithms is verified by flight tests with rotational strapdown inertial navigation system (RSINS)/global navigation satellite system (GNSS) integrated navigation system.

Studies of the integrated navigation system correction in the absence of a satellite signal

2021 ◽  
Author(s):  
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Operation Mode ◽  
Satellite System ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Correction Signal ◽  
Satellite Signal ◽  
Navigation Information

The schemes of navigation systems correction are considered. The operation mode of the aircraft during navigation is analyzed. An adaptive modification of the linear Kalman filter is used to correct the navigation information. An algorithm for predicting a correction signal based on a neural network in the event of a loss of a SNS correction signal is formed. Experimental results show the effectiveness of the algorithm. Keywords aircraft; inertial navigation system; satellite system; Kalman filter; neural networks; genetic algorithm

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.

scholarly journals Multi-Radio Integrated Navigation System M&S Software Design for GNSS Backup under Navigation Warfare

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 188 ◽  
Author(s):  
Heyone Kim ◽  
Junhak Lee ◽  
Sang Heon Oh ◽  
Hyoungmin So ◽  
Dong-Hwan Hwang
Keyword(s):  
Software Design ◽  
Navigation System ◽  
Design Method ◽  
Satellite System ◽  
Navigation Satellite ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Very High Frequency ◽  
Integrated Navigation System ◽  
Function Modules

To avoid degradation of navigation performance in the navigation warfare environment, the multi-radio integrated navigation system can be used, in which all available radio navigation systems are integrated to back up Global Navigation Satellite System (GNSS) when the GNSS is not available. Before real-time multi-radio integrated navigation systems are deployed, time and cost can be saved when the modeling and simulation (M&S) software is used in the performance evaluation. When the multi-radio integrated navigation system M&S is comprised of independent function modules, it is easy to modify and/or to replace the function modules. In this paper, the M&S software design method was proposed for multi-radio integrated navigation systems as a GNSS backup under the navigation warfare. The M&S software in the proposed design method consists of a message broker and function modules. All the messages were transferred through the message broker in order to be exchanged between the function modules. The function modules in the M&S software were independently operated due to the message broker. A message broker-based M&S software was designed for a multi-radio integrated navigation system. In order to show the feasibility of the proposed design method, the M&S software was implemented for Global Positioning System (GPS), Korean Navigation Satellite System (KNSS), enhanced Long range navigation (eLoran), Loran-C, and Distance Measuring Equipment/Very high-frequency Omnidirectional Radio range (DME/VOR). The usefulness of the proposed design method was shown by checking the accuracy and availability of the GPS only navigation and the multi-radio integrated navigation system under the attack of jamming to GPS.

Enhanced Accuracy Navigation Solutions Realized through SINS/GPS Integrated Navigation System

2013 ◽  
Vol 332 ◽  
pp. 79-85
Author(s):  
Outamazirt Fariz ◽  
Muhammad Ushaq ◽  
Yan Lin ◽  
Fu Li
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Position Errors ◽  
Strapdown Inertial Navigation

Strapdown Inertial Navigation Systems (SINS) displays position errors which grow with time in an unbounded manner. This degradation is due to the errors in the initialization of the inertial measurement unit, and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Improvement to this unbounded growth in errors can be made by updating the inertial navigation system solutions periodically with external position fixes, velocity fixes, attitude fixes or any combination of these fixes. The increased accuracy is obtained through external measurements updating inertial navigation system using Kalman filter algorithm. It is the basic 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 Inertial Navigation System (SINS), Global Positioning System (GPS) is presented using a centralized linear Kalman filter.

Federated Particle Filter Technology Based on JIDS/SINS/GPS Integrated Navigation System

2013 ◽  
Vol 347-350 ◽  
pp. 1544-1548
Author(s):  
Zi Yu Li ◽  
Yan Liu ◽  
Ping Zhu ◽  
Cheng Ying
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Integrated System ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System ◽  
Non Linear ◽  
Federated Kalman Filter

In multi-sensor integrated navigation systems, when sub-systems are non-linear and with Gaussian noise, the federated Kalman filter commonly used generates large error or even failure when estimating the global fusion state. This paper, taking JIDS/SINS/GPS integrated navigation system as example, proposes a federated particle filter technology to solve problems above. This technology, combining the particle filter with the federated Kalman filter, can be applied to non-linear non-Gaussian integrated system. It is proved effective in information fusion algorithm by simulated application, where the navigation information gets well fused.

scholarly journals A Novel Fault Detection Method for an Integrated Navigation System using Gaussian Process Regression

2016 ◽  
Vol 69 (4) ◽  
pp. 905-919 ◽  
Author(s):  
Yixian Zhu ◽  
Xianghong Cheng ◽  
Lei Wang
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Gaussian Process ◽  
Navigation System ◽  
Detection Method ◽  
Gaussian Process Regression ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Chi Squared

For the integrated navigation system, the correctness and the rapidity of fault detection for each sensor subsystem affects the accuracy of navigation. In this paper, a novel fault detection method for navigation systems is proposed based on Gaussian Process Regression (GPR). A GPR model is first used to predict the innovation of a Kalman filter. To avoid local optimisation, particle swarm optimisation is adopted to find the optimal hyper-parameters for the GPR model. The Fault Detection Function (FDF), which has an obvious jump in value when a fault occurs, is composed of the predicted innovation, the actual innovation of the Kalman filter and their variance. The fault can be detected by comparing the FDF value with a predefined threshold. In order to verify its validity, the proposed method is used in a SINS/GPS/Odometer integrated navigation system. The comparison experiments confirm that the proposed method can detect a gradual fault more quickly compared with the residual chi-squared test. Thus the navigation system with the proposed method gives more accurate outputs and its reliability is greatly improved.

scholarly journals RL-AKF: An Adaptive Kalman Filter Navigation Algorithm Based on Reinforcement Learning for Ground Vehicles

2020 ◽  
Vol 12 (11) ◽  
pp. 1704
Author(s):  
Xile Gao ◽  
Haiyong Luo ◽  
Bokun Ning ◽  
Fang Zhao ◽  
Linfeng Bao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Navigation System ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
Integrated Navigation System ◽  
Navigation Algorithm ◽  
Noise Covariance

Kalman filter is a commonly used method in the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation system, in which the process noise covariance matrix has a significant influence on the positioning accuracy and sometimes even causes the filter to diverge when using the process noise covariance matrix with large errors. Though many studies have been done on process noise covariance estimation, the ability of the existing methods to adapt to dynamic and complex environments is still weak. To obtain accurate and robust localization results under various complex and dynamic environments, we propose an adaptive Kalman filter navigation algorithm (which is simply called RL-AKF), which can adaptively estimate the process noise covariance matrix using a reinforcement learning approach. By taking the integrated navigation system as the environment, and the opposite of the current positioning error as the reward, the adaptive Kalman filter navigation algorithm uses the deep deterministic policy gradient to obtain the most optimal process noise covariance matrix estimation from the continuous action space. Extensive experimental results show that our proposed algorithm can accurately estimate the process noise covariance matrix, which is robust under different data collection times, different GNSS outage time periods, and using different integration navigation fusion schemes. The RL-AKF achieves an average positioning error of 0.6517 m within 10 s GNSS outage for GNSS/INS integrated navigation system and 14.9426 m and 15.3380 m within 300 s GNSS outage for the GNSS/INS/Odometer (ODO) and the GNSS/INS/Non-Holonomic Constraint (NHC) integrated navigation systems, respectively.

Adaptive Two-stage Kalman Filter for SINS/Odometer Integrated Navigation Systems

2016 ◽  
Vol 70 (2) ◽  
pp. 242-261 ◽  
Author(s):  
Hongsong Zhao ◽  
Lingjuan Miao ◽  
Haijun Shao
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Scale Factor ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Two Stage ◽  
Integrated Navigation System ◽  
White Noise Sequence ◽  
Noise Sequence ◽  
Computation Load

In Strapdown Inertial Navigation System (SINS)/Odometer (OD) integrated navigation systems, OD scale factor errors change with roadways and vehicle loads. In addition, the random noises of gyros and accelerometers tend to vary with time. These factors may cause the Kalman filter to be degraded or even diverge. To address this problem and reduce the computation load, an Adaptive Two-stage Kalman Filter (ATKF) for SINS/OD integrated navigation systems is proposed. In the Two-stage Kalman Filter (TKF), only the innovation in the bias estimator is a white noise sequence with zero-mean while the innovation in the bias-free estimator is not zero-mean. Based on this fact, a novel algorithm for computing adaptive factors is presented. The proposed ATKF is evaluated in a SINS/OD integrated navigation system, and the simulation results show that it is effective in estimating the change of the OD scale factor error and robust to the varying process noises. A real experiment is carried out to further validate the performance of the proposed algorithm.

scholarly journals A Pragmatic Approach to the Design of Advanced Precision Terrain Aided Navigation for UAVs and Its Verification

2020 ◽  
Author(s):  
Jungshin Lee ◽  
Chang-Ky Sung ◽  
Juhyun Oh ◽  
Kyungjun Han ◽  
Sangwoo Lee ◽  
...  
Keyword(s):  
Navigation System ◽  
Graphics Processing Units ◽  
Satellite System ◽  
General Purpose ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Radar Altimeter ◽  
Computing Technique ◽  
Integrated Navigation System ◽  
Navigation Information

Autonomous unmanned aerial vehicles (UAVs) require highly reliable navigation information. Generally, navigation systems with the inertial navigation system (INS) and global navigation satellite system (GNSS) have been widely used. However, the GNSS is vulnerable to jamming and spoofing. The terrain referenced navigation (TRN) technique can be used to solve this problem. In this study, to obtain reliable navigation information even if a GNSS is not available or the degree of terrain roughness is not determined, we propose a federated filter based INS/GNSS/TRN integrated navigation system. we also introduce a TRN system that combines batch processing and an auxiliary particle filter to ensure stable flight of UAVs even in a long-term GNSS-denied environment. As an altimeter sensor for the TRN system, we use an interferometric radar altimeter (IRA) to obtain reliable navigation accuracy in high altitude flight. In addition, a parallel computing technique with general-purpose computing on graphics processing units (GPGPU) is applied to process a high resolution terrain database and a nonlinear filter in real time on board. Finally, we verify the performance of the proposed system through software-in-the-loop (SIL) tests and captive flight tests in a GNSS unavailable environment.

scholarly journals A Pragmatic Approach to the Design of Advanced Precision Terrain-Aided Navigation for UAVs and Its Verification

2020 ◽  
Vol 12 (9) ◽  
pp. 1396
Author(s):  
Jungshin Lee ◽  
Chang-Ky Sung ◽  
Juhyun Oh ◽  
Kyungjun Han ◽  
Sangwoo Lee ◽  
...  
Keyword(s):  
Navigation System ◽  
Graphics Processing Units ◽  
Satellite System ◽  
General Purpose ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Radar Altimeter ◽  
Computing Technique ◽  
Integrated Navigation System ◽  
Navigation Information

Autonomous unmanned aerial vehicles (UAVs) require highly reliable navigation information. Generally, navigation systems with the inertial navigation system (INS) and global navigation satellite system (GNSS) have been widely used. However, the GNSS is vulnerable to jamming and spoofing. The terrain referenced navigation (TRN) technique can be used to solve this problem. In this study, to obtain reliable navigation information even if a GNSS is not available or the degree of terrain roughness is not determined, we propose a federated filter based INS/GNSS/TRN integrated navigation system. We also introduce a TRN system that combines batch processing and an auxiliary particle filter to ensure stable flight of UAVs even in a long-term GNSS-denied environment. As an altimeter sensor for the TRN system, an interferometric radar altimeter (IRA) is used to obtain reliable navigation accuracy in high altitude flight. In addition, a parallel computing technique with general purpose computing on graphics processing units (GPGPU) is applied to process a high resolution terrain database and a nonlinear filter in real-time on board. Finally, the performance of the proposed system is verified through software-in-the-loop (SIL) tests and captive flight tests in a GNSS unavailable environment.

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