scholarly journals A Track-Before-Detect Strategy Based on Sparse Data Processing for Air Surveillance Radar Applications

2021 ◽  
Vol 13 (4) ◽  
pp. 662
Author(s):  
Nicomino Fiscante ◽  
Pia Addabbo ◽  
Carmine Clemente ◽  
Filippo Biondi ◽  
Gaetano Giunta ◽  
...  
Keyword(s):  
Data Processing ◽  
Ad Hoc ◽  
Tracking System ◽  
Sparse Data ◽  
Sparse Learning ◽  
Multiple Target ◽  
Novel Approach ◽  
Air Surveillance ◽  
Reference Track ◽  
Track Before Detect

In this paper we consider the tracking problem of a moving target competing against noise and clutter in a surveillance radar scenario. For a single array-antenna multiple-target tracking system and according to the Track-Before-Detect paradigm, we present a novel approach based on a three-stage processing chain that involves the Sparse Learning via Iterative Minimization algorithm, the k-means clustering method and the ad hoc detector by exploiting the sparse nature of the operating scenario. Under the latter assumption, the detection strategy declares the presence of targets subsequently to the retrieval of their corresponding tracks performed by jointly processing the received echoes of multiple consecutive radar scans. Simulation results show that the proposed approach is able to provide good tracking and detection capabilities for different multiple target trajectories with low Signal-to-Interference-plus-Noise ratio and results in providing advantages when compared to a number of other reference Track-Before-Detect strategies based on sparse data processing techniques.

scholarly journals Selection of solar tracking system using extended TOPSIS technique with interval type-2 pythagorean fuzzy numbers

2021 ◽  
Author(s):  
Rimsha Umer ◽  
Muhammad Touqeer ◽  
Abdullah Hisam Omar ◽  
Ali Ahmadian ◽  
Soheil Salahshour ◽  
...  
Keyword(s):  
Fuzzy Numbers ◽  
Tracking System ◽  
Weighted Averaging ◽  
Distance Method ◽  
Novel Approach ◽  
Solar Tracking ◽  
Interval Type ◽  
Topsis Technique ◽  
Pythagorean Fuzzy Numbers

AbstractThe Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is considered among the most frequently used techniques to deal with multi-criteria group decision-making (MCGDM) conflicts. In this article, we have presented an extended TOPSIS technique in the framework of interval type-2 trapezoidal Pythagorean fuzzy numbers (IT2TrPFN). We first projected a novel approach to evaluate the distance between them using ordered weighted averaging operator and $$(\alpha ,\beta )$$ ( α , β ) -cut. Subsequently, we widen the concept of TOPSIS method formed on the distance method with IT2TrPFNs and applied it on MCGDM dilemma by considering the attitudes and perspectives of the decision-makers. Lastly, an application of solar tracking system and numerous contrasts with the other existing techniques are presented to express the practicality and feasibility of our projected approach.

scholarly journals A Novel Approach for Reliable Route Discovery in Mobile Ad-Hoc Network

2015 ◽  
Vol 83 (2) ◽  
pp. 1519-1529 ◽  
Author(s):  
Shariq Mahmood Khan ◽  
R. Nilavalan ◽  
Abdulhafid F. Sallama
Keyword(s):  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Route Discovery ◽  
Novel Approach ◽  
Mobile Ad Hoc

A single colorimetric sensor for multiple target ions: the simultaneous detection of Fe2+ and Cu2+ in aqueous media

RSC Advances ◽  
2014 ◽  
Vol 4 (43) ◽  
pp. 22463-22469 ◽  
Author(s):  
Hyun Kim ◽  
Yu Jeong Na ◽  
Eun Joo Song ◽  
Kyung Beom Kim ◽  
Jeong Mi Bae ◽  
...  
Keyword(s):  
Metal Ions ◽  
Aqueous Media ◽  
Simultaneous Detection ◽  
Colorimetric Sensor ◽  
Multiple Target ◽  
Novel Approach

The receptor 1 provides a novel approach for the simultaneous colorimetric recognition of two metal ions Fe2+ and Cu2+.

Industrialization Research and Realization of Preprocessing in Rough Set Based on Similar Prediction

2012 ◽  
Vol 251 ◽  
pp. 185-190
Author(s):  
Dun Hong Yao ◽  
Xiao Ning Peng ◽  
Jia He
Keyword(s):  
Data Processing ◽  
Collaborative Filtering ◽  
Rough Set ◽  
Data Preprocessing ◽  
Sparse Data ◽  
Data Evaluation ◽  
Prediction Algorithm ◽  
Rating Prediction ◽  
Essential Problem ◽  
Data Processing Methods

In every field which needs data processing, the sparseness of data is an essential problem that should be resolved, especially in movies, shopping sites. The users with the same commodity preferences makes the data evaluation valuable. Otherwise, without any evaluation of information, it will result in sparse distribution of the entire data obtained. This article introduces a collaborative filtering technology used in sparse data processing methods - project-based rating prediction algorithm, and extends it to the areas of rough set, the sparse information table processing, rough set data preprocessing sparse issues.

scholarly journals Shearlet-based regularized reconstruction in region-of-interest computed tomography

2018 ◽  
Vol 13 (4) ◽  
pp. 34
Author(s):  
T.A. Bubba ◽  
D. Labate ◽  
G. Zanghirati ◽  
S. Bonettini
Keyword(s):  
Optimization Problem ◽  
Ad Hoc ◽  
Iterative Scheme ◽  
Tomographic Reconstruction ◽  
Region Of Interest ◽  
Projection Data ◽  
Reconstruction Problem ◽  
Convex Optimization Problem ◽  
Scanning Time ◽  
Novel Approach

Region of interest (ROI) tomography has gained increasing attention in recent years due to its potential to reducing radiation exposure and shortening the scanning time. However, tomographic reconstruction from ROI-focused illumination involves truncated projection data and typically results in higher numerical instability even when the reconstruction problem has unique solution. To address this problem, bothad hocanalytic formulas and iterative numerical schemes have been proposed in the literature. In this paper, we introduce a novel approach for ROI tomographic reconstruction, formulated as a convex optimization problem with a regularized term based on shearlets. Our numerical implementation consists of an iterative scheme based on the scaled gradient projection method and it is tested in the context of fan-beam CT. Our results show that our approach is essentially insensitive to the location of the ROI and remains very stable also when the ROI size is rather small.

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