Richard Linares

78 papers receiving 938 citations

Peers

Richard Linares
Comparison fields: 5 of 64
  • Aerospace Engineering 780
  • Astronomy and Astrophysics 388
  • Artificial Intelligence 193
  • Computer Vision and Pattern Recognition 135
  • Control and Systems Engineering 79
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Countries citing papers authored by Richard Linares

Since Specialization
Citations

This map shows the geographic impact of Richard Linares's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Richard Linares with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard Linares more than expected).

Fields of papers citing papers by Richard Linares

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard Linares. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Richard Linares. The network helps show where Richard Linares may publish in the future.

Co-authorship network of co-authors of Richard Linares

This figure shows the co-authorship network connecting the top 25 collaborators of Richard Linares. A scholar is included among the top collaborators of Richard Linares based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Richard Linares. Richard Linares is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 5
3 0
4 1
5 3
6 2
7 24
8 3
9 7
10 58
11 15
12
Space Debris Identification and Characterization via Deep Meta-Learning
6
13 14
14
Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks
14
15
Space Object classification using deep Convolutional Neural Networks
24
16
Space object classification and characterization via Multiple Model Adaptive Estimation
10
17
Photometric Data from Non-Resolved Objects for Space Object Characterization and Improved Atmospheric Modeling
4
18
IMPACT: Integrated Modeling of Perturbations in Atmospheres for Conjunction Tracking
1
19
Nonlinear sequential methods for impact probability estimation
0
20 2

About Richard Linares

Richard Linares is a scholar working on Aerospace Engineering, Astronomy and Astrophysics and Modeling and Simulation, having authored 92 papers that have together received 1.0k indexed citations. Recurring topics across this work include Space Satellite Systems and Control (38 papers), Inertial Sensor and Navigation (29 papers) and Astro and Planetary Science (23 papers). The work is most often cited by research in Aerospace Engineering (780 citations), Astronomy and Astrophysics (388 citations) and Computer Vision and Pattern Recognition (135 citations). Richard Linares has collaborated with scholars based in United States, Italy and United Kingdom. Frequent co-authors include Roberto Furfaro, John L. Crassidis, Brian Gaudet, Moriba Jah, Andrea Scorsoglio, Mauro Massari, Kathleen C. Howell, W. E. Parker, Yang Cheng and Francesco Topputo. Their work appears in journals such as Scientific Reports, Sensors and IEEE Transactions on Aerospace and Electronic Systems.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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