Robust Divided Differences Gaussian Mixture Non Linear Filtering Applied to INS/GNSS Integrated Navigation System with Impulsive Measurement Noise

2020 ◽  
Vol 15 (2) ◽  
pp. 50
Author(s):  
Hamza Benzerrouk ◽  
Alexander Nebylov ◽  
Hassen Salhi
Keyword(s):  
Navigation System ◽  
Gaussian Mixture ◽  
Measurement Noise ◽  
Divided Differences ◽  
Integrated Navigation ◽  
Linear Filtering ◽  
Integrated Navigation System ◽  
Non Linear

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 Enhanced Redundant Measurement-Based Kalman Filter for Measurement Noise Covariance Estimation in INS/GNSS Integration

2020 ◽  
Vol 12 (21) ◽  
pp. 3500
Author(s):  
Baoshuang Ge ◽  
Hai Zhang ◽  
Wenxing Fu ◽  
Jianbing Yang
Keyword(s):  
Navigation System ◽  
Autoregressive Process ◽  
Satellite System ◽  
Measurement Noise ◽  
Difference Sequence ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
The Difference

Adaptive Kalman filters (AKF) have been widely applied to the inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation system. However, the traditional AKF methods suffer from the problems of filtering instability or covariance underestimation, especially when the GNSS measurement disturbances occur. In this paper, an enhanced redundant measurement-based AKF is developed to improve the filtering performance. The scheme is based on the mutual difference sequence derived from the redundant measurement of INS. By using the mutual difference sequence, the measurement noise covariance can be estimated without being affected by the inaccuracy estimates, hence avoiding the risk of filtering divergence. In addition, the kernel density estimation is used to estimate the GNSS measurement noise’s probability density to detect whether the Gaussian properties of the measurement noise are maintained. When the noise statistics are far from Gaussian distribution, the difference sequence will be modeled as an autoregressive process using the Burg’s method. The real variance of the difference sequence can then be updated relying on the autoregressive model in order to avoid the covariance underestimation. A field experiment was carried out to evaluate the performance of the proposed method. The test results demonstrate that the proposed method can effectively mitigate the GNSS measurement disturbances and improve the accuracy of the navigation solution.

An Adaptive & Fault Tolerant SINS/GPS Integrated Navigation Scheme Robustified against Slowly Growing Errors in GPS Updates

2013 ◽  
Vol 390 ◽  
pp. 500-505 ◽  
Author(s):  
Muhammad Ushaq ◽  
Fang Jian Cheng ◽  
Jamshaid Ali
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Measurement Noise ◽  
Integrated Navigation ◽  
Random Errors ◽  
Actual Measurement ◽  
Integrated Navigation System ◽  
System Noise ◽  
Measurement Noise Covariance ◽  
Noise Covariance

The Strapdown Inertial Navigation System (SINS) renders excellent attitude, position and velocity solutions on short term basis, but when used as stand-alone navigation system, its accuracy deteriorates with the passage of time. On the other hand GPS has long-standing stability with a consistent precisiongenerally having only bounded random errors in position and velocity. Integrated navigation system is used to augment the complementary features of SINS and GPS. In integrated navigation system external fixes for position and/or velocity and/or attitude are used to contain the growing errors of SINS. Kalman filter is generally used as integration tool for integrated navigation system. Kalman filter algorithm is based on the assumptions that the system model and the measurement models are linear and the system random errors and measurement random errors are Gaussian in nature expressed with fixed covariances. But in real navigation systems these assumptions are seldom fulfilled and hence Kalman filter renders unsatisfactory results. Adaptive Kalman filter provides the solution to the problem by adjusting the system noise covariance and measurement noise covariance in real time in the light of actual measurement errors or actual dynamics of thevehicle. In this paper an innovation and residual based adaption of measurement noise covariance and system noise covariance is presented. The presented scheme has been applied on an SINS/GPS Integrated Navigation Systemand it has been validated that the scheme provide significantly better results as compared to standard Kalman filter on occurrence slowly growing errors as well as excessive random errors in GPS measurements.

scholarly journals An Improved Adaptive Extended Kalman Filter Algorithm of SINS/GPS Loosely-Coupled Integrated Navigation System

2018 ◽  
Vol 7 (4.27) ◽  
pp. 87
Author(s):  
Yuyan Wang ◽  
Xiuyun Meng ◽  
Jilu Liu
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Measurement Noise ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Loosely Coupled ◽  
Kalman Filter Algorithm ◽  
Adaptive Extended Kalman Filter

The Kalman Filter algorithm usually cannot estimate noise statistics in real-time, in order to deal with this issue, a new kind of improved Adaptive Extended Kalman Filter algorithm is proposed. Based on residual sequence, this algorithm mainly improves the adaptive estimator of the filter algorithm, which can estimate measurement noise in real-time. Furthermore, this new filter algorithm is applied to a SINS/GPS loosely-coupled integrated navigation system, which can automatically adjust the covariance matrix of measurement noise as noise varies in the system. Finally, the original Extended Kalman Filter and the improved Adaptive Extended Kalman Filter are applied respectively to simulate for the SINS/GPS loosely-coupled model. Tests demonstrate that, the improved Adaptive Extended Kalman Filter reduces both position error and velocity error compared with the original Extended Kalman Filter.  

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