A novel angle-of-arrival assisted extended Kalman filter tracking algorithm with space-time correlation based motion parameters estimation

2013 ◽  
Author(s):  
Li Zhang ◽  
Yong Huat Chew ◽  
Wai-Choong Wong
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Time Correlation ◽  
Space Time ◽  
Parameters Estimation ◽  
Tracking Algorithm ◽  
Angle Of Arrival ◽  
Motion Parameters

scholarly journals Satellite Inertia Parameters Estimation Based on Extended Kalman Filter

2019 ◽  
Author(s):  
Abdellatif Bellar ◽  
Mohammed Arezki Si Mohammed
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Critical Role ◽  
Low Earth Orbit ◽  
Parameters Estimation ◽  
Moment Of Inertia ◽  
Satellite Mission ◽  
Inertia Matrix ◽  
Inertia Parameters ◽  
The Moment

The moment of inertia parameters play a critical role in assuring the spacecraft mission throughout its lifetime. However, determination of the moment of inertia is a key challenge in operating satellites. During satellite mission, those parameters can change in orbit for many reasons such as sloshing, fuel consumption, etc. Therefore, the inertia matrix should be estimated in orbit to enhance the attitude estimation and control accuracy. This paper investigates the use of gyroscope to estimate the attitude rate and inertia matrix for low earth orbit satellite via extended Kalman filter. Simulation results show the effectiveness and advantages of the proposed algorithm in estimating these parameters without knowing the nominal inertia. The robustness of the proposed algorithm has been validated using the Monte-Carlo method. The obtained results demonstrate that the accuracy of the estimated inertia and angular velocity parameters is satisfactory for satellite with coarse accuracy mission requirements. The proposed method can be used for different types of satellites.

Fault-tolerant pose and inertial parameters estimation of an uncooperative spacecraft based on dual vector quaternions

2018 ◽  
Vol 233 (4) ◽  
pp. 1250-1269 ◽  
Author(s):  
Jianping Yuan ◽  
Xianghao Hou ◽  
Chong Sun ◽  
Yu Cheng
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Formation Flying ◽  
Fault Tolerant ◽  
Estimation Algorithm ◽  
Parameters Estimation ◽  
Small Satellites ◽  
Dual Vector ◽  
Inertial Parameters ◽  
Space Target

Estimating the parameters of an unknown free-floating tumbling spacecraft is an essential task for the on-orbit servicing missions. This paper proposes a dual vector quaternion based fault-tolerant pose and inertial parameters estimation algorithm of an uncooperative space target using two formation flying small satellites. Firstly, by utilizing the dual vector quaternions to model the kinematics and dynamics of the system, not only the representation of the model is concise and compacted, but also the translational and rotational coupled effects are considered. By using this modeling technique along with the measurements from the on-board vision-based sensors, a dual vector quaternion based extended Kalman filter for each of the two small satellites is designed. Secondly, both of the estimations from each small satellite will be used as inputs of the fault-tolerant algorithm. This algorithm is based on the fault-tolerant federal extended Kalman filter strategy to overcome the estimation errors caused by the faulty measurements, the unknown space environment and the computing errors by setting the appropriate ratios of the two estimations from the first step dual vector quaternions extended Kalman filter. Together with the first and second steps, a novel fault-tolerant dual vector quaternions federal extended Kalman filter using two formation flying small satellites is proposed by this paper to estimate the pose and inertial parameters of a free-floating tumbling space target. By utilizing the estimation algorithm, a good prior knowledge of the unknown space target can be achieved. Finally, the proposed dual vector quaternion federal extended Kalman filter is validated by mathematical simulations to show its robust performances.

scholarly journals Object tracking algorithm for unmanned surface vehicle based on improved mean-shift method

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092529
Author(s):  
Zuquan Xiang ◽  
Tao Tao ◽  
Lifei Song ◽  
Zaopeng Dong ◽  
Yunsheng Mao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Autonomous Navigation ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Similarity Function ◽  
Unmanned Surface Vehicle ◽  
Feature Map ◽  
Center Position ◽  
Self Organizing

The unmanned surface vehicle has the characteristics of high maneuverability and flexibility. Object detection and tracking skills are required to improve the ability of unmanned surface vehicle to avoid collisions and detect targets on the surface of the water. Mean-shift algorithm is a classic target tracking algorithm, but it may fail when pixel interference and occlusion occur. This article proposes a tracking algorithm for unmanned surface vehicle based on an improved mean-shift optimization algorithm. The method uses the self-organizing feature map spatial topology to reduce the interference of the background pixels on the target object and predicts the center position of the object when the target is heavily occluded according to the extended Kalman filter. First, a self-organizing feature map model is built to classify pixels in a rectangular frame and the background pixels are extracted. Then, the method optimizes the extended Kalman filter solution process to complete the prediction and correction of the target center position and introduces a similarity function to determine the target occlusion. Finally, numerical analyses based on a ship model sailing experiment are performed with the help of OpenCV library. The experimental results validated that the proposed method significantly reduces the cumulative error in the tracking process and effectively predicts the position of the target between continuous frames when temporary occlusion occurs. The research can be used for target detection and autonomous navigation of unmanned surface vehicle.

scholarly journals The Vehicles Positioning in Ultra-Dense 5G/V2X Radio Access Networks Using the Extended Kalman Filter

2020 ◽  
Vol 6 (4) ◽  
pp. 45-59
Author(s):  
G. Fokin ◽  
A. Vladyko
Keyword(s):  
Kalman Filter ◽  
Simulation Model ◽  
Extended Kalman Filter ◽  
Traffic Control ◽  
Access Networks ◽  
Line Of Sight ◽  
Angle Of Arrival ◽  
Radio Access Networks ◽  
Radio Access ◽  
Vehicle Positioning

This work is devoted to the study of mathematical models of vehicle positioning in ultra-dense V2X / 5G radio access networks using the extended Kalman filter. Based on the study of the probability of line-of-sight availability in the conditions of ultra-dense distribution of reference radio access stations and vehicles, as well as existing mathematical prototype positioning models, a new simulation model for constructing the trajectory of a vehicle has been developed to assess compliance with the requirements for the accuracy of coordinate assessment on the example of the scenario of priority passage of intersections. The simulation model implements the procedures for collecting primary angle and rangefinder measurements by reference stations received from the vehicle for subsequent secondary processing using the extended Kalman filter, as a result of which the vehicle trajectory is built in real time. In contrast to the existing prototype models, the simulation model developed in this work makes it possible to assess compliance with the specified requirements and other specifications depending on the current conditions of line-of-sight availability, as well as the accuracy of collecting primary angle measurements determined by the antenna array installed on the support device. The results of simulation are consistent with the known estimates of prototype models and confirm the possibility of achieving an accuracy of up to 1 m for a traffic control scenario with an error in determining the angle of arrival of a signal of 2 °.

An Approach of Extended Kalman Filter in Cooperative Localization

2021 ◽  
Vol 9 (VII) ◽  
pp. 3083-3089
Author(s):  
Akanksha Katiyar
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Mobile User ◽  
Base Station ◽  
Filter Method ◽  
Cooperative Localization ◽  
Angle Of Arrival ◽  
Target Node ◽  
Target User ◽  
Kalman Filter Method

In this paper we explored the problem of localizing a mobile user within the range of a base station in 5G communication. For solving this problem we utilized Cooperative localization method over conventional localization technique to estimate the position of a mobile user. In cooperative localization we estimate position of the target user using Angle of Arrival(AOA), Time Difference of Arrival(TDOA) and Received Signal Strength(RSS) observations from nodes which are neighbour to target node and the base station. This estimation is done with the help of Extended Kalman Filter method.

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