Dynamical reconfiguration strategy of a multi sensor data fusion algorithm based on Information Theory

Tmazirte, Nourdine Aït; Najjar, Maan E. El; Smaili, Cherif; Pomorski, Denis

doi:10.1109/ivs.2013.6629580

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…In [158], Tmazirte et al consider the problem of detecting faults in multimodal sensors in a distributed data fusion framework, and dynamically reconfigure the system using information theoretical concepts. Their approach is based on detecting inconsistency in the mutual information contribution of each sensor w.r.t.…”

Section: B Challenges Due To Various Types Of Uncertaintymentioning

confidence: 99%

Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects

2015

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This paper provides an overview of the main challenges in multimodal data fusion across various disciplines and addresses two key issues: ''why we need data fusion'' and ''how we perform it.'' ABSTRACT | In various disciplines, information about the same phenomenon can be acquired from different types of detectors, at different conditions, in multiple experiments or subjects, among others. We use the term ''modality'' for each such acquisition framework. Due to the rich characteristics of natural phenomena, it is rare that a single modality provides complete knowledge of the phenomenon of interest. The increasing availability of several modalities reporting on the same system introduces new degrees of freedom, which raise questions beyond those related to exploiting each modality separately. As we argue, many of these questions, or ''challenges,'' are common to multiple domains. This paper deals with two key issues: ''why we need data fusion'' and ''how we perform it.''The first issue is motivated by numerous examples in science and technology, followed by a mathematical framework that showcases some of the benefits that data fusion provides. In order to address the second issue, ''diversity'' is introduced as a key concept, and a number of data-driven solutions based on matrix and tensor decompositions are discussed, emphasizing how they account for diversity across the data sets. The aim of this paper is to provide the reader, regardless of his or her community of origin, with a taste of the vastness of the field, the prospects, and the opportunities that it holds.

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Section: B Challenges Due To Various Types Of Uncertaintymentioning

confidence: 99%

Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects

2015

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“…By integrating random set theory, Kreucher, Hero & Kastella (2005) broadened the scope of the sensor management algorithm based on information theory. In 2005, Tmazirte et al (2013) proposed a novel approach that maximises the alpha-Rényi information gain measurement by utilising sensors to optimise the likelihood of accurate target location after the following measure. He, Shin & Tsourdos (2018) developed a new method to process various fields of view within the generalised covariance cross-fusion framework, enabling centralised and distributed point-to-point sensor networks to perform multi-target tracking.…”

Section: Related Workmentioning

confidence: 99%

Multi-sensor network tracking research utilizing searchable encryption algorithm in the cloud computing environment

Sun,

2023

PeerJ Computer Science

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Presently, the focus of target detection is shifting towards the integration of information acquired from multiple sensors. When faced with a vast amount of data from various sensors, ensuring data security during transmission and storage in the cloud becomes a primary concern. Data files can be encrypted and stored in the cloud. When using data, the required data files can be returned through ciphertext retrieval, and then searchable encryption technology can be developed. However, the existing searchable encryption algorithms mainly ignore the data explosion problem in a cloud computing environment. The issue of authorised access under cloud computing has yet to be solved uniformly, resulting in a waste of computing power by data users when processing more and more data. Furthermore, to save computing resources, ECS (encrypted cloud storage) may only return a fragment of results in response to a search query, lacking a practical and universal verification mechanism. Therefore, this article proposes a lightweight, fine-grained searchable encryption scheme tailored to the cloud edge computing environment. We generate ciphertext and search trap gates for terminal devices based on bilinear pairs and introduce access policies to restrict ciphertext search permissions, which improves the efficiency of ciphertext generation and retrieval. This scheme allows for encryption and trapdoor calculation generation on auxiliary terminal devices, with complex calculations carried out on edge devices. The resulting method ensures secure data access, fast search in multi-sensor network tracking, and accelerates computing speed while maintaining data security. Ultimately, experimental comparisons and analyses demonstrate that the proposed method improves data retrieval efficiency by approximately 62%, reduces the storage overhead of the public key, ciphertext index, and verifiable searchable ciphertext by half, and effectively mitigates delays in data transmission and computation processes.

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“…Other works (Maaref & Kassas, 2019;Maaref et al, 2020) fused terrestrial signals of opportunity (SOPs) with GPS signals to improve redundancy, and thereby performed FDE across GPS and SOP measurements using the traditional residual-based test statistic that considered the distribution to be centered chi-square in the absence of faults and non-centered chi-square otherwise. Tmazirte et al (2012) developed a distributed information filter for a multi-sensor framework, wherein faults were detected by examining the consistency through a log-likelihood ratio of the measurement residuals using a mutual information concept. However, the main drawback of these FDE approaches is that they modeled the measurement faults as Gaussian distributions, and are thus, not suitable for GPS-vision sensor fusion.…”

Section: Fde Approaches For Multiple Fault Modalities In Sensor Fusionmentioning

confidence: 99%

Robust GPS-Vision Localization via Integrity-Driven Landmark Attention

Bhamidipati

Gao

2022

navi

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For robust GPS-vision navigation in urban areas, we propose an integrity-driven landmark attention (ILA) technique via stochastic reachability. Inspired by cognitive attention in humans, we perform convex optimization to select a subset of landmarks from GPS and vision measurements that maximizes integrity-driven performance. Given known measurement error bounds in non-faulty conditions, our ILA technique follows a unified approach to address both GPS and vision faults and is compatible with any off-the-shelf estimator. We analyze measurement deviation to estimate the stochastic reachable set of positions associated with each landmark, which is parameterized via probabilistic zonotope (p-zonotope). We apply set union to formulate a p-zonotopic cost that represents the size of position bounds based on landmark inclusion/exclusion. We jointly minimize the p-zonotopic cost and maximize the number of landmarks via convex relaxation. For an urban data set, we demonstrate improved localization accuracy and robust predicted availability for a pre-defined risk and alert limit.

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Dynamical reconfiguration strategy of a multi sensor data fusion algorithm based on Information Theory

Cited by 7 publications

References 8 publications

Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects

Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects

Multi-sensor network tracking research utilizing searchable encryption algorithm in the cloud computing environment

Robust GPS-Vision Localization via Integrity-Driven Landmark Attention

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