A Linear Technique for Artifacts Correction and Compensation in Phase Interferometric Angle of Arrival Estimation

Florio, Antonello; Avitabile, G.; Coviello, Giuseppe

doi:10.3390/s22041427

Cited by 12 publications

(3 citation statements)

References 13 publications

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“…In laser range finders, this technique is frequently used. POA-based ranging equipment detects phase changes in sinusoidally modulated infrared signals [11,12].…”

Section: Parameter-based Positioningmentioning

confidence: 99%

RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities

Rathnayake

Maduranga

Tilwari

et al. 2023

Eng

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The rapid expansion of the Internet of Things (IoT) and Machine Learning (ML) has significantly increased the demand for Location-Based Services (LBS) in today’s world. Among these services, indoor positioning and navigation have emerged as crucial components, driving the growth of indoor localization systems. However, using GPS in indoor environments is impractical, leading to a surge in interest in Received Signal Strength Indicator (RSSI) and machine learning-based algorithms for in-building localization and navigation in recent years. This paper aims to provide a comprehensive review of the technologies, applications, and future research directions of ML-based indoor localization for smart cities. Additionally, it examines the potential of ML algorithms in improving localization accuracy and performance in indoor environments.

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“…In laser range finders, this technique is frequently used. POA-based ranging equipment detects phase changes in sinusoidally modulated infrared signals [11,12].…”

Section: Parameter-based Positioningmentioning

confidence: 99%

RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities

Rathnayake

Maduranga

Tilwari

et al. 2023

Eng

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“…Meng in [29] proposes a sparse solution method under the coexistence of signal coherence and mutual coupling, but the algorithm will have different performance when facing different parameters, so it still needs to be improved. Meng in [30] proposes a novel block sparse estimation DOA method based on weighted signal subspace, and Antonello in [31] proposes a lightweight technique for artifact correction and compensation in DOA Estimation, which provides us with a new perspective.…”

Section: Introductionmentioning

confidence: 99%

A Novel Weighted Block Sparse DOA Estimation Based on Signal Subspace under Unknown Mutual Coupling

Liu,

Yin,

et al. 2024

Electronics

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In this paper, a novel weighted block sparse method based on the signal subspace is proposed to realize the Direction-of-Arrival (DOA) estimation under unknown mutual coupling in the uniform linear array. Firstly, the signal subspace is obtained by decomposing the eigenvalues of the sampling covariance matrix. Then, a block sparse model is established based on the deformation of the product of the mutual coupling matrix and the steering vector. Secondly, a suitable set of weighted coefficients is calculated to enhance sparsity. Finally, the optimization problem is transformed into a second-order cone (SOC) problem and solved. Compared with other algorithms, the simulation results of this paper have better performance on DOA accuracy estimation.

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“…Processing and manufacturing phases are often used to extend shelf-life and ensure high food quality, particularly for perishable foods [11]. The food industry has developed many long-life products using innovative technologies [9,12] and new equipment [13] at both industrial and domestic levels. Well-established preservation techniques in daily life, especially for fruits and vegetables, are represented by frozen or dried products, jams, jellies, and products in oil and vinegar.…”

Section: Introductionmentioning

confidence: 99%

Life Cycle Assessment in the Agri-Food Supply Chain: Fresh Versus Semi-Finished Based Production Process

et al. 2022

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The agri-food supply chain is responsible for high GHG emissions. In industrial food processing, there is potential for reducing environmental impacts. In the case of apricot jam, several practices for realizing the finished product can be adopted. If, on the one hand, the scientific literature provides detailed studies on the economic aspects of the existing practices, on the other hand, a comprehensive assessment of the most common production practices from an environmental perspective is not provided. Apricot jam production can be based on two different kinds of raw materials: fresh fruit and the so-called “semi-finished product”, which is obtained by treating fresh fruits with mechanical and thermal processes. The most extended shelf-life of the semi-finished product allows for the adoption of more flexible production practices, leading to increased production performance and ensuring more efficient transportation strategies. In environmental terms, the convenience of this approach compared to adopting fresh fruit is questionable. The aim of this paper is to compare the environmental impacts due to these two different production and transport practices of apricot jam. Consistent with this end, a Life Cycle Assessment methodology has been adopted to evaluate the environmental impact of each step in the apricot jam supply chain. Research results suggest that the use of fresh apricots in the production of jams has a lower environmental impact (about 50–65% without considering transport activities) than the use of semi-finished products, and that losses due to the short shelf-life of fresh fruit limits the transportation distance of the raw and fresh material, also depending on the transport mode adopted (in the range of 875–4450 km). This highlights the need to adopt more clean and sustainable practices in the current industrial food processes.

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A Linear Technique for Artifacts Correction and Compensation in Phase Interferometric Angle of Arrival Estimation

Cited by 12 publications

References 13 publications

RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities

RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities

A Novel Weighted Block Sparse DOA Estimation Based on Signal Subspace under Unknown Mutual Coupling

Life Cycle Assessment in the Agri-Food Supply Chain: Fresh Versus Semi-Finished Based Production Process

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