scholarly journals Novel Process Noise Model for GNSS Kalman Filter Based on Sensitivity Analysis of Covariance with Poor Satellite Geometry

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6056
Author(s):  
Yoji Takayama ◽  
Takateru Urakubo ◽  
Hisashi Tamaki
Keyword(s):  
Kalman Filter ◽  
Estimation Error ◽  
Conventional Technique ◽  
Urban Environments ◽  
Analysis Of Covariance ◽  
Noise Model ◽  
Process Noise ◽  
Satellite Geometry ◽  
Noise Covariance ◽  
Filter Divergence

One of the great unsolved GNSS problems is inaccuracy in urban canyons due to Non-Line-Of-Sight (NLOS) signal reception. Owing to several studies about the NLOS signal rejection method, almost all NLOS signals can be excluded from the calculation of the position. However, such precise NLOS rejection would make satellite geometry poor, especially in dense urban environments. This paper points out, through numerical simulations and theoretical analysis, that poor satellite geometry leads to unintentional performance degradation of the Kalman filter with a conventional technique to prevent filter divergence. The conventional technique is to bump up process noise covariance, and causes unnecessary inflation of estimation-error covariance when satellite geometry is poor. We propose a novel choice of process noise covariance based on satellite geometry that can reduce such unnecessary inflation. Numerical and experimental results demonstrate that performance improvement can be achieved by the choice of process noise covariance even for a poor satellite geometry.

Improved UFIR Tracking Algorithm for Maneuvering Target

2016 ◽  
Vol 2 (2) ◽  
pp. 344 ◽  
Author(s):  
Shoulin Yin ◽  
Jinfeng Wang ◽  
Tianhua Liu
Keyword(s):  
Target Tracking ◽  
Finite Impulse Response ◽  
Estimation Error ◽  
Statistical Properties ◽  
Noise Model ◽  
Noise Power ◽  
Actual Process ◽  
Maneuvering Target Tracking ◽  
Process Noise ◽  
Maneuvering Target

Maneuvering target tracking is a target motion estimation problem, which can describe the irregular target maneuvering motion. It has been widely used in the field of military and civilian applications. In the maneuvering target tracking, the performance of Kalman filter(KF) and its improved algorithms depend on the accuracy of process noise statistical properties. If there exists deviation between process noise model and the actual process, it will generate the phenomenon of estimation error increasing. Unbiased finite impulse response(UFIR) filter does not need priori knowledge of noise statistical properties in the filtering process. The existing UFIR filters have the problem that generalized noise power gain(GNPG) does not change with measurement of innovation. We propose an improved UFIR filter based on measurement of innovation with ratio dynamic adaptive adjustment at adjacent time. It perfects the maneuvering detect-ability. The simulation results show that the improved UFIR filter has the best filtering effect than KF when process noise is not accurate.

Flexible polarization demultiplexing method based on an adaptive process noise covariance Kalman filter

2018 ◽  
Vol 16 (6) ◽  
pp. 060601
Author(s):  
Jun Ge Jun Ge ◽  
Lianshan Yan Lianshan Yan ◽  
Anlin Yi Anlin Yi ◽  
Yan Pan Yan Pan ◽  
Lin Jiang Lin Jiang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Process Noise ◽  
Adaptive Process ◽  
Noise Covariance

Vision-based terrain referenced navigation of aerial vehicles using an adaptive extended Kalman filter

2017 ◽  
Vol 232 (8) ◽  
pp. 1584-1597 ◽  
Author(s):  
Sung-Hoon Mok ◽  
Youngjoo Kim ◽  
Hyochoong Bang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Least Squares Method ◽  
New Approach ◽  
Terrain Referenced Navigation ◽  
Digital Terrain ◽  
Highly Nonlinear ◽  
Noise Covariance ◽  
Adaptive Extended Kalman Filter ◽  
Filter Divergence

This paper addresses a vision-based terrain referenced navigation of an aircraft. A digital terrain map, in the surroundings of the aircraft, is compared with the camera measurements to estimate the aircraft position. Generally, the measurement equation in the terrain referenced navigation is highly nonlinear due to the sharp changes of terrain. Thus, the conventional extended Kalman filter could lead to unstable navigation solutions. In this paper, a new approach using an adaptive extended Kalman filter is proposed to cope up with the nonlinearity problem. A least squares method is utilized to derive the linearized measurement equations. The Jacobian matrix and sensor noise covariance are modified as a means of smoothing the sharp changes of terrain. Monte Carlo simulations verify that the proposed filter gives the stable navigation solutions, even when there is a large initial error, which is the primary reason for the filter divergence. Moreover, the proposed adaptation barely requires additional computational burden, whereas the high-order filters such as particle filter generally needs higher computational power.

GDOP, Ridge Regression and the Kalman Filter

1990 ◽  
Vol 43 (03) ◽  
pp. 409-427 ◽  
Author(s):  
R. J. Kelly
Keyword(s):  
Kalman Filter ◽  
Ridge Regression ◽  
Navigation Systems ◽  
Process Noise ◽  
Navigation Problem ◽  
Navigation Signal ◽  
Noise Covariance ◽  
Regression Techniques ◽  
Signal Processors ◽  
Parameter Estimators

Multicollinearity and its effect on parameter estimators such as the Kalman filter is analysed using the navigation application as a special example. All position-fix navigation systems suffer loss of accuracy when their navigation landmarks are nearly collinear. Nearly collinear measurement geometry is termed the geometric dilution of position (GDOP). Its presence causes the errors of the position estimates to be highly inflated. In 1970 Hoerl and Kennard developed ridge regression to combat near collinearity when it arises in the predictor matrix of a linear regression model. Since GDOP is mathematically equivalent to a nearly collinear predictor matrix, Kelly suggested using ridge regression techniques in navigation signal processors to reduce the effects of GDOP. The original programme intended to use ridge regression not only to reduce variance inflation but also to reduce bias inflation. Reducing bias inflation is an extension of Hoerl's ridge concept by Kelly. Preliminary results show that ridge regression will reduce the effects of variance inflation caused by GDOP. However, recent results (Kelly) conclude it will not reduce bias inflation as it arises in the navigation problem, GDOP is not a mismatched estimator/model problem. Even with an estimator matched to the model, GDOP may inflate the MSE of the ordinary Kalman filter while the ridge recursive filter chooses a suitable biased estimator that will reduce the MSE. The main goal is obtaining a smaller MSE for the estimator, rather than minimizing the residual sum of squares. This is a different operation than tuning the Kalman filter's dynamic process noise covariance Q, in order to compensate for unmodelled errors. Although ridge regression has not yielded a satisfactory solution to the general GDOP problem, it has provided insight into exactly what causes multicollinearity in navigation signal processors such as the Kalman filter and under what conditions an estimator's performance can be improved.

Kalman Filter With Recursive Covariance Estimation—Sequentially Estimating Process Noise Covariance

2014 ◽  
Vol 61 (11) ◽  
pp. 6253-6263 ◽  
Author(s):  
Bo Feng ◽  
Mengyin Fu ◽  
Hongbin Ma ◽  
Yuanqing Xia ◽  
Bo Wang
Keyword(s):  
Kalman Filter ◽  
Covariance Estimation ◽  
Process Noise ◽  
Noise Covariance

The Selection of Process Noise Covariance Q and Measurement Noise Covariance R on Kalman Filter

2014 ◽  
Vol 513-517 ◽  
pp. 4342-4345 ◽  
Author(s):  
Chao Yi Wei ◽  
Shu Jian Ye ◽  
Xu Guang Li ◽  
Mei Zhi Xie ◽  
Feng Yan Yi
Keyword(s):  
Kalman Filter ◽  
Simulation Model ◽  
Dynamic Model ◽  
Measurement Noise ◽  
State Estimator ◽  
Process Noise ◽  
Measurement Noise Covariance ◽  
High Measurement ◽  
Noise Covariance ◽  
Selection Of

At first, a seven degree of freedom dynamic model of tractor semi-trailer was established, and a simulation model with the Matlab/Simulink software was established too. Based on the model, a state estimator based on Kalman theory was designed, using easily measured parameters to estimate the parameters that are difficultly measured or have high measurement cost, after that, compared estimated values and simulation measurements and analyzed the influence with changes of process noise covariance Q and measurement noise covariance R.

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