Distributed Space Debris Tracking with Consensus Labeled Random Finite Set Filtering

Author:

Wei Baishen,Nener BrettORCID

Abstract

Space debris tracking is a challenge for spacecraft operation because of the increasing number of both satellites and the amount of space debris. This paper investigates space debris tracking using marginalized δ -generalized labeled multi-Bernoulli filtering on a network of nodes consisting of a collection of sensors with different observation volumes. A consensus algorithm is used to achieve the global average by iterative regional averages. The sensor network can have unknown or time-varying topology. The proposed space debris tracking algorithm provides an efficient solution to the key challenges (e.g., detection uncertainty, data association uncertainty, clutter, etc.) for space situational awareness. The performance of the proposed algorithm is verified by simulation results.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimizing Distributed Multi-Sensor Multi-Target Tracking Algorithm Based On Labeled Multi-Bernoulli Filter;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04

2. An Adaptive Multi-Sensor Generalised Labelled Multi-Bernoulli Filter for Linear Gaussian Models;2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS);2022-11-21

3. A Moving Window Based Approach to Multi-scan Multi-Target Tracking;2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS);2022-11-21

4. Probabilistic Data Association for Orbital-Element Estimation Using Multistage Expectation–Maximization;Journal of Aerospace Information Systems;2021-05

5. A novel approach for space debris recognition based on the full information vectors of star points;Journal of Visual Communication and Image Representation;2020-08

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3