Analysis of the effects of bearings-only sensors on the performance of the neural extended kalman filter tracking system

AbstractThis article concerns tracking of floating objects using fixed-wing UAVs with a monocular thermal camera. Target tracking from an agile aerial vehicle is challenging because uncertainty in the UAV pose negatively affects the accuracy of the measurements obtained through thermal images. Consequently, the accuracy of the tracking estimates is degraded if navigation uncertainty is neglected. This is especially relevant for the estimated target covariance since inconsistency is a likely consequence. A tracking system based on the Schmidt-Kalman filter is proposed to mitigate navigation uncertainty. Images gathered with an uncertain UAV pose are weighted less than images captured with a reliable pose. The UAV pose is estimated independently in a multiplicative extended Kalman filter where the estimated covariance matrix is a measure of the uncertainty. The method is compared experimentally with two traditional alternatives based on the extended Kalman filter. The results show that the proposed method performs better with respect to consistency and accuracy.

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Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System

International Journal on Advanced Science Engineering and Information Technology ◽

10.18517/ijaseit.7.3.2657 ◽

2017 ◽

Vol 7 (3) ◽

pp. 980

Author(s):

Rafina Destiarti Ainul ◽

Prima Kristalina ◽

Amang Sudarsono

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Tracking System ◽

Iterated Extended Kalman Filter ◽

Cooperative Tracking

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Leader-Follower Tracking System for Agricultural Vehicles: Fusion of Laser and Odometry Positioning Using Extended Kalman Filter

IAES International Journal of Robotics and Automation (IJRA) ◽

10.11591/ijra.v4i1.pp1-18 ◽

2015 ◽

Vol 4 (1) ◽

pp. 1

Author(s):

Zhang Lin Huan ◽

Takigawa Tomohiro ◽

Ahamed Tofael

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Detection System ◽

Tracking System ◽

Tracking Errors ◽

Steering Angle ◽

Agricultural Vehicles ◽

System A ◽

Formation Tracking ◽

Improved Accuracy

The aim of this research was to develop a safe human-driven and autonomous leader-follower tracking system for an autonomous tractor. To enable the tracking system, a laser range finder (LRF)-based landmark detection system was designed to observe the relative position between a leader and a follower used in agricultural operations. The virtual follower-based formation-tracking algorithm was developed to minimize tracking errors and ensure safety. An extended Kalman filter (EKF) was implemented for fusing LRF and odometry position to ensure stability of tracking in noisy farmland conditions. Simulations were conducted for tracking the leader in small and large sinusoidal curved paths. Simulated results verified high accuracy of formation tracking, stable velocity, and regulated steering angle of the follower. The tracking method confirmed the follower could follow the leader with a required formation safely and steadily in noisy conditions. The EKF helped to improve observation accuracy, velocity, and steering angle stability of the follower. As a result of the improved accuracy of observation and motion action, the tracking performance for lateral, longitudinal, and heading were also improved after the EKF was implemented in the tracking system.

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UWB and MEMS IMU Integrated Positioning Algorithm for a Work-Tool Tracking System

Applied Sciences ◽

10.3390/app11198826 ◽

2021 ◽

Vol 11 (19) ◽

pp. 8826

Author(s):

Seong-Geun Kwon ◽

Oh-Jun Kwon ◽

Ki-Ryong Kwon ◽

Suk-Hwan Lee

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Tracking System ◽

Mobile Node ◽

Automotive Production ◽

Space Matrix ◽

Positioning Algorithm ◽

Mems Imu ◽

Combined Sensor ◽

Coordinate Data

In this paper, we address a system that can accurately locate and monitor work tools in a complex assembly process, such as automotive production. Our positioning monitoring system is positioned by a combined sensor of the UWB module and the MEMS IMU (inertial measuring unit) sensor based on the extended Kalman filter. The MEMS IMU sensor provides the positioning calibration information. The proposed method incorporates IMU and UWB positioning to compensate for errors that can only occur in UWB positioning through the extended Kalman filter (EKT). This EKT is improved by the error dynamic equation derived from the sparse state-space matrix. Also, the proposed method computes the transmission time and distance between the tag and anchor of the UWB module by the TWR (two-way range) system. The tag of a mobile node, which is attached to a moving tool, measures the position of the work tool and transmits the position coordinate data to the anchor. Here, the proposed method uses the trilateration localization method by the confidence distance compensation to prevent the distance error by obstacles and changes in the indoor environment. Experimental results verified that the proposed method confirms whether a specific tool is accurately used according to the prescribed regulations and has more positioning accuracy than the conventional methods.

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