Model‐switched Gaussian sum cubature Kalman filter for attitude angle‐aided three‐dimensional target tracking

2015 ◽  
Vol 9 (5) ◽  
pp. 531-539 ◽  
Author(s):  
Kai Zhang ◽  
Ganlin Shan
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Three Dimensional ◽  
Gaussian Sum ◽  
Attitude Angle ◽  
Cubature Kalman Filter

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.

A Gaussian-Sum Based Cubature Kalman Filter for Bearings-Only Tracking

2013 ◽  
Vol 49 (2) ◽  
pp. 1161-1176 ◽  
Author(s):  
Pei H. Leong ◽  
Sanjeev Arulampalam ◽  
Tharaka A. Lamahewa ◽  
Thushara D. Abhayapala
Keyword(s):  
Kalman Filter ◽  
Gaussian Sum ◽  
Cubature Kalman Filter

scholarly journals Feedback Robust Cubature Kalman Filter for Target Tracking Using an Angle Sensor

Sensors ◽  
2016 ◽  
Vol 16 (5) ◽  
pp. 629 ◽  
Author(s):  
Hao Wu ◽  
Shuxin Chen ◽  
Binfeng Yang ◽  
Kun Chen
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Angle Sensor ◽  
Cubature Kalman Filter

scholarly journals Robust cubature Kalman filter target tracking algorithm based on genernalized M-estiamtion

2015 ◽  
Vol 64 (21) ◽  
pp. 218401
Author(s):  
Wu Hao ◽  
Chen Shu-Xin ◽  
Yang Bin-Feng ◽  
Chen Kun
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Tracking Algorithm ◽  
Cubature Kalman Filter

Developing Computationally Efficient Nonlinear Cubature Kalman Filtering for Visual Inertial Odometry

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Trung Nguyen ◽  
George K. I. Mann ◽  
Andrew Vardy ◽  
Raymond G. Gosine
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
High Speed ◽  
Three Dimensional ◽  
Measurement Model ◽  
Estimation Accuracy ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Deterministic Sampling ◽  
Cubature Kalman Filter

This paper presents a computationally efficient sensor-fusion algorithm for visual inertial odometry (VIO). The paper utilizes trifocal tensor geometry (TTG) for visual measurement model and a nonlinear deterministic-sampling-based filter known as cubature Kalman filter (CKF) to handle the system nonlinearity. The TTG-based approach is developed to replace the computationally expensive three-dimensional-feature-point reconstruction in the conventional VIO system. This replacement has simplified the system architecture and reduced the processing time significantly. The CKF is formulated for the VIO problem, which helps to achieve a better estimation accuracy and robust performance than the conventional extended Kalman filter (EKF). This paper also addresses the computationally efficient issue associated with Kalman filtering structure using cubature information filter (CIF), the CKF version on information domain. The CIF execution avoids the inverse computation of the high-dimensional innovation covariance matrix, which in turn further improves the computational efficiency of the VIO system. Several experiments use the publicly available datasets for validation and comparing against many other VIO algorithms available in the recent literature. Overall, this proposed algorithm can be implemented as a fast VIO solution for high-speed autonomous robotic systems.

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