Sensor Tasking for Spacecraft Custody Maintenance and Anomaly Detection Using Evidential Reasoning

Jaunzemis, Andris D.; Holzinger, Marcus J.; Luu, Kim

doi:10.2514/1.i010584

Cited by 13 publications

(5 citation statements)

References 34 publications

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“…These ML methods, as well as deep learning (DL) methods [ 40 ], support evidence-based knowledge for SDA [ 41 ] of space-domain sensor fusion programs such as DARPA Hallmark [ 42 ]. SSA also includes the understanding of mission policies, technical aims, and orbital mechanics [ 43 , 44 , 45 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Massimi

Ferrara²,

Benedetto

2022

Sensors

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Artificial Intelligence of things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the Internet of Things (IoT) infrastructure. AI deals with the devices’ learning process to acquire knowledge from data and experience, while IoT concerns devices interacting with each other using the Internet. AIoT has been proven to be a very effective paradigm for several existing applications as well as for new areas, especially in the field of satellite communication systems with mega-constellations. When AIoT meets space communications efficiently, we have interesting uses of AI for Satellite IoT (SIoT). In fact, the number of space debris is continuously increasing as well as the risk of space collisions, and this poses a significant threat to the sustainability and safety of space operations that must be carefully and efficiently addressed to avoid critical damage to the SIoT networks. This paper aims to provide a systematic survey of the state of the art, challenges, and perspectives on the use of deep learning methods for space situational awareness (SSA) object detection and classification. The contributions of this paper can be summarized as follows: (i) we outline using AI algorithms, and in particular, deep learning (DL) methods, the possibility of identifying the nature/type of spatial objects by processing signals from radars; (ii) we present a comprehensive taxonomy of DL-based methods applied to SSA object detection and classification, as well as their characteristics, and implementation issues.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Massimi

Ferrara²,

Benedetto

2022

Sensors

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“…The presented IDEA-I addresses this gap through its use of DSTE for power system cyberphysical situational awareness, that handles uncertainty due to its ability of quantifying unknowns. Analogous to the space situational awareness (SSA) paradigm [26], we improve the cyber-physical situational awareness (CyPSA) framework by accurately representing the state knowledge of objects in the cyber-physical environment to provide better prediction capabilities for potential threats.…”

Section: Introductionmentioning

confidence: 99%

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

Sahu,

Davis

2021

Preprint

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False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. To solve this problem, research has focused on leveraging deep learning techniques that would help reduce false alerts. However, a shortcoming is that these works often require or implicitly assume the physical and cyber sensor data to be trustworthy. Implicit trust of data is a major problem with using artificial intelligence or machine learning (AI/ML) for cyber-physical system (CPS) security, because the times when these solutions are needed most to detect an attack are also the times when they are more at risk, with both greater likelihood and greater impact, of also being compromised. To address this inevitable shortcoming, the problem can thus be reframed as how to make good decisions given uncertainty. Then, the decision is detection, and the uncertainty includes whether or not the data that would be used in ML-based IDS is compromised. Thus, this article presents an approach for reducing false alerts in cyber-physical power systems that addresses this critical problem of dealing with uncertainty without the knowledge of prior distribution of the alerts. Specifically, an evidence theoretic based approach leveraging Dempster Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from various supervised-learning classifiers. Using this model, a location-cum-domain based fusion framework is proposed to evaluate the intrusion detector's performance using Disjunctive, Conjunctive and Cautious Conjunctive rules of combinations, that fuse multiple piece of evidences from inter-domain and intradomain sensors. The approach is demonstrated in a cyber-physical power system testbed (RESLab), and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, general Bayesian theorem based metrics as decision functions. To improve the performance, a multi-objective based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.

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“…The accurate detection of sensor signal is the basis for the effective operation of modern measurement and control system [1]. With the advancement of modern science and technology and the development of electronic information technology, more and more sensors are applied to industrial manufacturing [2], safety control [3], medical instrument [4], environmental detection [5], aerospace [6] and other fields. According to the need of external energy in the detection process, the sensor can be divided into active sensor and passive sensor.…”

Section: Introductionmentioning

confidence: 99%

Systematic Proportional Method for Improving the Measurement Accuracy of Passive Sensor Measurement System

et al. 2020

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The accuracy and stability of excitation source determine the measurement accuracy of passive sensor measurement system, but the implementation of high-precision excitation source (especially AC excitation source) is difficult or costly in design and process. For this purpose, this paper proposes a systematic proportional method. In this method, the DC voltage converted by the excitation signal is taken as the reference voltage of the ADC used in this system. And then the ratio of the measured signal to the excitation signal is obtained by using the transmission characteristics of the ADC, therefore the amplitude fluctuation of the excitation signal is compensated. The method can greatly suppress the measurement error caused by the amplitude fluctuation of the excitation signal and the fluctuation of the ADC reference voltage, and effectively improve the measurement accuracy of the measurement system. To verify the effectiveness of the proposed method, the Wheatstone bridge measurement circuit is designed. The experimental results show that when the amplitude of the excitation signal changes 50%, the measurement result of the measurement system using non-proportional method changes about 50%, while that of the measurement system using proportional method only changes about 1%. To further improve the measurement accuracy, the DC bias voltage compensation circuit is added, and the maximum variation error of the measurement result after compensation is only 0.3%. More importantly, the method can also be extended to other linear measurement systems with excitation source in principle, which has greater application value.

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Sensor Tasking for Spacecraft Custody Maintenance and Anomaly Detection Using Evidential Reasoning

Cited by 13 publications

References 34 publications

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

Systematic Proportional Method for Improving the Measurement Accuracy of Passive Sensor Measurement System

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