scholarly journals An Adaptive Low-Cost INS/GNSS Tightly-Coupled Integration Architecture Based on Redundant Measurement Noise Covariance Estimation

Sensors ◽  
2017 ◽  
Vol 17 (9) ◽  
pp. 2032 ◽  
Author(s):  
Zheng Li ◽  
Hai Zhang ◽  
Qifan Zhou ◽  
Huan Che
Keyword(s):  
Low Cost ◽  
Covariance Estimation ◽  
Measurement Noise ◽  
Measurement Noise Covariance ◽  
Integration Architecture ◽  
Tightly Coupled ◽  
Noise Covariance

scholarly journals Maximum Likelihood-Based Measurement Noise Covariance Estimation Using Sequential Quadratic Programming for Cubature Kalman Filter Applied in INS/BDS Integration

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Bingbing Gao ◽  
Gaoge Hu ◽  
Wenmin Li ◽  
Yan Zhao ◽  
Yongmin Zhong
Keyword(s):  
Kalman Filter ◽  
Maximum Likelihood ◽  
Sequential Quadratic Programming ◽  
Covariance Estimation ◽  
Measurement Noise ◽  
Likelihood Principle ◽  
Maximum Likelihood Principle ◽  
Measurement Noise Covariance ◽  
Cubature Kalman Filter ◽  
Noise Covariance

With the completion of the Beidou-3 system (BDS) in China, INS/BDS integration will become a promising navigation and positioning strategy. However, due to the nonlinear propagation characteristic of INS error and inevitable involvement of inaccurate measurement noise statistics, it is difficult to achieve the optimal solution through the INS/BDS integration. This paper proposes a method of cubature Kalman filter (CKF) with the measurement noise covariance estimation by using the maximum likelihood principle to solve the abovementioned problem. It establishes an estimation model for measurement noise covariance according to the maximum likelihood principle, and then, its estimation is calculated by utilizing the sequential quadratic programming. The estimated measurement noise covariance will be fed back to the procedure of CKF to improve its adaptability. Simulation and comparison analysis verify that the proposed method can accurately estimate measurement noise covariance to effectively restrain its influence on navigation solution, leading to improved navigation performance for the INS/BDS integration.

scholarly journals Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1126
Author(s):  
Zhentao Hu ◽  
Linlin Yang ◽  
Yong Jin ◽  
Han Wang ◽  
Shibo Yang
Keyword(s):  
Real Time ◽  
Gaussian Mixture ◽  
Measurement Noise ◽  
Tracking Accuracy ◽  
Process Noise ◽  
Probability Hypothesis Density ◽  
Measurement Noise Covariance ◽  
Multi Target Tracking ◽  
Noise Covariance ◽  
Fading Factor

Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene. To solve this problem, a strong tracking PHD filter based on Variational Bayes (VB) approximation is proposed in this paper. The measurement noise covariance is described in the linear system by the inverse Wishart (IW) distribution. Then, the fading factor in the strong tracking principle uses the optimal measurement noise covariance at the previous moment to control the state prediction covariance in real-time. The Gaussian IW (GIW) joint distribution adopts the VB approximation to jointly return the measurement noise covariance and the target state covariance. The simulation results show that, compared with the traditional Gaussian mixture PHD (GM-PHD) and the VB-adaptive PHD, the proposed algorithm has higher tracking accuracy and stronger robustness in a more reasonable calculation time.

Interference-Compensating Magnetometer Calibration With Estimated Measurement Noise Covariance for Application to Small-Sized UAVs

2020 ◽  
Vol 67 (10) ◽  
pp. 8829-8840 ◽  
Author(s):  
Hakjun Lee ◽  
Chanu Lee ◽  
Hayeong Jeon ◽  
Junwoo Jason Son ◽  
Youngbin Son ◽  
...  
Keyword(s):  
Measurement Noise ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
Magnetometer Calibration

Passive Location of Angle Sensors Using Extend Square-Root Cubature Kalman Filter

2011 ◽  
Vol 143-144 ◽  
pp. 577-581 ◽  
Author(s):  
Yang Zhang ◽  
Guo Sheng Rui ◽  
Jun Miao
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Measurement Noise ◽  
Filter Method ◽  
Square Root ◽  
Passive Location ◽  
Mode Decomposition ◽  
Measurement Noise Covariance ◽  
Cubature Kalman Filter ◽  
Noise Covariance

A new nonlinear filter method Cubature Kalman Filter (CKF) is improved for passive location with moving angle-measured sensors’ measurements.Firstly,it used Empirical Mode Decomposition (EMD) algorithm to estimate measurement noise covariance; And then the covariance of the procession noise and measurement noise is brought into the circle; Meanwhile,CKF is improved by the way of square root to keep its stability and positivity,and the results of track by Extend SCKF are compared with the results by Unscented Kalman Filter (UKF) in the text;By the tracking results to the velocity of the target, Extend SCKF algorithm can not only track the target with unknown measurement noise but also improve the passive position precision remarkably as the same difficulty as UKF.

Sign in / Sign up

Export Citation Format

Share Document