Jonathan P. How

32.1k citations
574 papers · 20.9k indexed · 8 hit papers · h-index 72

Jonathan P. How

557 papers receiving 19.9k citations

Hit Papers

Kimera-Multi...1502002202620102018200400600

Peers

Jonathan P. How
Comparison fields: 5 of 159
  • Computer Vision and Pattern Recognition 7.9k
  • Aerospace Engineering 9.3k
  • Control and Systems Engineering 7.2k
  • Computer Networks and Communications 5.4k
  • Automotive Engineering 2.8k
Replace Vijay Kumar with:
Vijay Kumar United States
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Hugh Durrant‐Whyte Australia
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Citations per field
00.5×3.4×
Vijay Kumar · 1×
Citations per year

Countries citing papers authored by Jonathan P. How

Since Specialization
Citations

This map shows the geographic impact of Jonathan P. How'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 Jonathan P. How with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan P. How more than expected).

Fields of papers citing papers by Jonathan P. How

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan P. How. 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 Jonathan P. How. The network helps show where Jonathan P. How may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Jonathan P. How, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jonathan P. How Line = papers co-authored together Jonathan P. How links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20241
3 20241
4 20249
5 20243
6 202313
7
Performance analysis of adaptive dynamic tube mpc
20211
8 2021104
9 20202
10 2020126
11
Socially aware motion planning with deep reinforcement learningbreakdown →
2017463
12
Talk Resource-Efficiently to Me: Optimal Communication Planning for Distributed SLAM Front-Ends.
20173
13 20150
14 2002205
15 200233
16
Decentralized Relative Navigation for Formation Flying Spacecraft using Augmented CDGPS 1
200112
17
Onboard Pseudolite Augmentation System for Relative Navigation
19997
18
“GPS On A Chip” - An Advanced GPS Receiver for Spacecraft
19985
19
GPS Sensing for Spacecraft Formation Flying
199755
20
Carrier Differential GPS for Real-Time Control of Large Flexible Structures
19961

About Jonathan P. How

Jonathan P. How is a scholar working on Aerospace Engineering, Control and Systems Engineering and Computer Vision and Pattern Recognition, having authored 574 papers that have together received 20.9k indexed citations. Recurring topics across this work include Robotic Path Planning Algorithms (132 papers), Advanced Control Systems Optimization (84 papers), Reinforcement Learning in Robotics (71 papers), Distributed Control Multi-Agent Systems (69 papers), Robotics and Sensor-Based Localization (68 papers), Control Systems and Identification (58 papers), Target Tracking and Data Fusion in Sensor Networks (55 papers) and Spacecraft Dynamics and Control (48 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (7.9k citations), Aerospace Engineering (9.3k citations) and Control and Systems Engineering (7.2k citations). Jonathan P. How has collaborated with scholars based in United States, South Korea and United Kingdom. Frequent co-authors include Arthur Richards, Yoshiaki Kuwata, Han‐Lim Choi, Tom Schouwenaars, Éric Féron, Michael Tillerson, Mehdi Alighanbari, Arjang Hassibi, John Vian and Louis Breger. Their work appears in journals such as IEEE Control Systems, Journal of Guidance Control and Dynamics, IEEE Transactions on Robotics, IEEE Robotics and Automation Letters and IEEE Transactions on Control Systems Technology.

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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