Conditionally Minimax Nonlinear Filter and Unscented Kalman Filter: Empirical Analysis and Comparison

2019 ◽  
Vol 80 (7) ◽  
pp. 1230-1251 ◽  
Author(s):  
A. V. Bosov ◽  
G. B. Miller
Keyword(s):  
Kalman Filter ◽  
Empirical Analysis ◽  
Unscented Kalman Filter ◽  
Nonlinear Filter

Hybrid consensus sigma point approximation nonlinear filter using statistical linearization

2018 ◽  
Vol 40 (8) ◽  
pp. 2517-2525 ◽  
Author(s):  
Guoqing Wang ◽  
Ning Li ◽  
Yonggang Zhang
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Nonlinear Filter ◽  
Statistical Linearization ◽  
Nonlinear Functions ◽  
Central Difference ◽  
Sigma Point ◽  
Point Approximation ◽  
Cubature Kalman Filter ◽  
Central Difference Kalman Filter

In this paper, a hybrid consensus sigma point approximation nonlinear filter is proposed for state estimation in collaborative sensor network, where hybrid consensus of both measurement and information is utilised. Statistical linearization of nonlinear functions is used in sigma point filters, that is, unscented Kalman filter (UKF), cubature Kalman filter (CKF), and central difference Kalman filter (CDKF). Stability of the proposed algorithm is also analysed with the help of linearization operation and some conservative assumptions. Two typical target tracking examples are used to demonstrate the effectiveness of the proposed algorithms. Simulation results show that the proposed algorithms are more stable than existing algorithms, and among our proposed algorithms, CKF- and CDKF-based algorithms are more accurate and stable than the UKF-based one.

scholarly journals The UKF Based Approach to Improving Attitude and Position of Quadcopter Through Autonomous and Non-Autonomous Flight

2020 ◽  
Vol 16 (2) ◽  
pp. 1-9
Author(s):  
Ahmed Abdulkareem ◽  
Basil Jasim ◽  
Safanah Raafat
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Nonlinear Filter ◽  
Linear Estimation ◽  
Autonomous Flight ◽  
Physical Sensors ◽  
Aerial Vehicle ◽  
Simulation Results ◽  
And Performance ◽  
The Stability

The gyroscope and accelerometer are the basic sensors used by most Unmanned Aerial Vehicle (UAV) like quadcopter to control itself. In this paper, the fault detection of measured angular and linear states by gyroscope and accelerometer sensors are present. Uncertainties in measurement and physical sensors itself are the main reasons that lead to generate noise and cause the fault in measured states. Most previous solutions are process angular or linear states to improving the performance of quadcopter. Also, in most of the previous solutions, KF and EKF filters are used, which are inefficient in dealing with high nonlinearity systems such as quadcopter. The proposed algorithm is developed by the robust nonlinear filter, Unscented Kalman Filter (UKF), as an angular and linear estimation filter. Simulation results show that the proposed algorithm is efficient to decrease the effect of sensors noise and estimate accurate angular and linear states. Also, improving the stability and performance properties of the quadcopter. In addition, the new algorithm leads to increasing the range of nonlinearity movements that quadcopter can perform it.

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