M-Estimation-Based Robust Kalman Filter Algorithm for Three-Dimensional AoA Target Tracking

2020 ◽  
Author(s):  
Yuexin Zhao ◽  
Wangdong Qi ◽  
Peng Liu ◽  
Jie Lin
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Three Dimensional ◽  
Robust Kalman Filter ◽  
Kalman Filter Algorithm ◽  
M Estimation

Combined Adaptive Robust Kalman Filter Algorithm

2021 ◽  
Author(s):  
Xu Lin ◽  
Wei Li ◽  
Shaoda Li ◽  
Jiang Ye ◽  
Chaolong Yao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Robust Kalman Filter ◽  
Kalman Filter Algorithm

Three-Dimensional Submarine-to-Submarine Passive Target Tracking in the Presence of Non-Gaussian Noises

2021 ◽  
Vol 17 (3) ◽  
pp. 1-24
Author(s):  
Kavitha Lakshmi M. ◽  
Koteswara Rao S. ◽  
Subrahmanyam Kodukula
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Gaussian Noise ◽  
Target Location ◽  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Noise Environment ◽  
Cubature Kalman Filter ◽  
Highly Nonlinear ◽  
Non Gaussian

In underwater surveillance, three-dimensional target tracking is a challenging task. The angles-only measurements (i.e., bearing and elevation) obtained by hull mounted sensors are considered to appraise the target motion parameter. Due to noise in measurements and nonlinearity of the system, it is very hard to find out the target location. For many applications, UKF is best estimator that remaining algorithms. Recently, cubature Kalman filter (CKF) is also popular. It is proposed to use UKF (unscented Kalman filter) and CKF (cubature Kalman filter) algorithms that minimize the noise in measurements. So far, researchers carried out this work (target tracking) in Gaussian noise environment, whereas in this paper same work is carried out for non-Gaussian noise environment. The performance evaluation of the filters using Monte-Carlo simulation and Cramer-Rao lower bound (CRLB) is accomplished and the results are analyzed. Result shows that UKF is well suitable for highly nonlinear systems than CKF.

Attitude dynamics aiding for three-dimensional passive target tracking of strap-down seeker based on instrumental variable Kalman filter

2020 ◽  
Vol 42 (14) ◽  
pp. 2645-2659
Author(s):  
Yingchao Xiao ◽  
Jun Zhou ◽  
Bin Zhao
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Instrumental Variable ◽  
Measurement Accuracy ◽  
Sliding Mode ◽  
Angular Rate ◽  
Three Dimensional ◽  
Measurement Unit ◽  
State Equations ◽  
Passive Target Tracking

The accuracy of three-dimensional (3D) passive target tracking under strap-down system is usually affected by the measurement accuracy of attitude angular rate and attitude angle. In order to save the problem, a novel 3D passive target tracking method based on instrumental variable Kalman filter (IVKF) aided by the attitude dynamic is proposed. At first, the maneuvering target motion model is established based on the “current” statistical model and the filtering equation of MEMS inertial measurement unit (IMU) is also set up. Then, linearize the nonlinear state equations and replace the nonlinear measurement equations with pseudolinear equations. The 3D pseudolinear Kalman filter (PLKF) algorithm is derived according to the linear Kalman filter (KF). To counter the severe bias problems with PLKF, bias compensation and recursive instrumental variable (IV) methods are considered. In order to enhance observability of the system, a 3D motion tracking sliding-mode guidance law is deduced. Finally, some mathematical simulations were made to verify the effectiveness of the proposed method. The simulation results show the effect of the measurement accuracy and complexity of the algorithm are reduced, which proves the validity of the method.

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