WeChat Mini Program
Old Version Features

Manoeuvre Detection in Low Earth Orbit with Radar Data.

Jose M. Montilla,Julio C. Sanchez,Rafael Vazquez,Jorge Galan-Vioque, Javier Rey Benayas,Jan Siminski

Advances in Space Research(2023)SCI 3区SCI 2区SCI 4区

Univ Seville

Cited 3|Views1
Abstract
This work outlines and assesses several methods for the detection of manoeuvres in Low Earth Orbit (LEO) from surveillance radar data. To be able to detect manoeuvres, the main starting assumption is that the object under analysis has an orbit known with a sufficient degree of precision. Based on the precise (a posteriori) orbit and radar data, several manoeuvre detection methods are presented; one is based on unscented Kalman filtering, whereas two others algorithms are based on reachability analysis of the state, which correlates its prediction set with the next track from the radar. The filtering algorithm can be extended for several radar tracks, whereas the reachability-based methods are more precise in detecting manoeuvres. Then, to inherit the best properties of both classes of algorithms, a manoeuvre detection filter that combines both concepts is finally presented. Manoeuvre detection results are analysed first for simulated scenarios—for validation and calibration purposes—and later for real data. Radar information comes from the Spanish Space Surveillance Radar (S3TSR), with real manoeuvre information and high-quality ephemerides. The results show promise, taking into account that a single surveillance radar is the only source of data, obtaining manoeuvre detection rates of more than 50% and false positive rates of less than 10%.
More
Translated text
Key words
Surveillance radar,Manoeuvre detection,OREKIT,Reachability,S3TSR
PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文提出并评估了几种利用雷达数据在低地球轨道(LEO)检测机动的方法,创新性地结合了无迹卡尔曼滤波和可达性分析,提高了机动检测的准确率和减少了误报率。

方法】:研究采用了基于无迹卡尔曼滤波和两种基于状态可达性分析的算法进行机动检测,并最终提出了结合这两种概念的机动检测滤波器。

实验】:实验首先在模拟场景中进行验证和校准,然后使用西班牙空间监视雷达(S3TSR)的实际数据进行分析。结果表明,仅使用单一监视雷达作为数据源,实现了超过50%的机动检测率和低于10%的误报率。