Daniel E. Clark

16.8k total citations
117 papers, 3.1k citations indexed

About

Daniel E. Clark is a scholar working on Artificial Intelligence, Computer Networks and Communications and Aerospace Engineering. According to data from OpenAlex, Daniel E. Clark has authored 117 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 91 papers in Artificial Intelligence, 44 papers in Computer Networks and Communications and 33 papers in Aerospace Engineering. Recurrent topics in Daniel E. Clark's work include Target Tracking and Data Fusion in Sensor Networks (88 papers), Distributed Sensor Networks and Detection Algorithms (42 papers) and Gaussian Processes and Bayesian Inference (17 papers). Daniel E. Clark is often cited by papers focused on Target Tracking and Data Fusion in Sensor Networks (88 papers), Distributed Sensor Networks and Detection Algorithms (42 papers) and Gaussian Processes and Bayesian Inference (17 papers). Daniel E. Clark collaborates with scholars based in United Kingdom, Australia and France. Daniel E. Clark's co-authors include Ba‐Ngu Vo, Branko Ristić, Ba-Tuong Vo, J. S. Bell, Kusha Panta, Murat Üney, Simon Julier, Jérémie Houssineau, Emmanuel Delande and Ronald Mahler and has published in prestigious journals such as Journal of the American College of Cardiology, IEEE Transactions on Automatic Control and IEEE Transactions on Information Theory.

In The Last Decade

Daniel E. Clark

112 papers receiving 3.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel E. Clark United Kingdom 30 2.6k 1.0k 943 461 403 117 3.1k
Mark R. Morelande Australia 27 1.8k 0.7× 923 0.9× 631 0.7× 389 0.8× 229 0.6× 137 2.8k
Dominic Schuhmacher Switzerland 10 1.8k 0.7× 685 0.7× 652 0.7× 348 0.8× 255 0.6× 25 2.3k
Fred Daum United States 22 1.8k 0.7× 864 0.8× 286 0.3× 335 0.7× 170 0.4× 87 2.6k
J.H. Kotecha United States 14 1.6k 0.6× 446 0.4× 814 0.9× 860 1.9× 150 0.4× 29 2.7k
W.D. Blair United States 26 1.9k 0.7× 1.5k 1.4× 467 0.5× 265 0.6× 178 0.4× 204 2.7k
Patrick Y. Hwang United States 11 1.1k 0.4× 1.8k 1.8× 244 0.3× 719 1.6× 319 0.8× 22 3.4k
Petr Tichavský Czechia 25 1.2k 0.5× 605 0.6× 481 0.5× 533 1.2× 214 0.5× 112 3.3k
Todd E. Humphreys United States 29 956 0.4× 2.7k 2.7× 937 1.0× 1.7k 3.6× 251 0.6× 119 3.9k
Mark L. Psiaki United States 33 1.1k 0.4× 3.3k 3.2× 497 0.5× 892 1.9× 759 1.9× 179 4.0k
Joaquı́n Mı́guez Spain 21 1.4k 0.5× 266 0.3× 853 0.9× 693 1.5× 91 0.2× 132 2.6k

Countries citing papers authored by Daniel E. Clark

Since Specialization
Citations

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

Fields of papers citing papers by Daniel E. Clark

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel E. Clark. 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 Daniel E. Clark. The network helps show where Daniel E. Clark may publish in the future.

Co-authorship network of co-authors of Daniel E. Clark

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel E. Clark. A scholar is included among the top collaborators of Daniel E. Clark 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 Daniel E. Clark. Daniel E. Clark 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
2.
Clark, Daniel E.. (2022). A Cramér Rao Bound for Point Processes. IEEE Transactions on Information Theory. 68(4). 2147–2155. 7 indexed citations
3.
Clark, Daniel E., et al.. (2021). Using Machine Learning Methods to Predict the Movement Trajectories of the Louisiana Black Bear. SMU Scholar (Southern Methodist University). 5(1). 11. 1 indexed citations
4.
Clark, Daniel E., et al.. (2021). An Algorithm for Large-Scale Multitarget Tracking and Parameter Estimation. IEEE Transactions on Aerospace and Electronic Systems. 57(4). 2053–2066. 15 indexed citations
5.
Clark, Daniel E.. (2021). Multi-Sensor Network Information for Linear-Gaussian Multi-Target Tracking Systems. IEEE Transactions on Signal Processing. 69. 4312–4325. 11 indexed citations
6.
Delande, Emmanuel, et al.. (2021). A Formulation of the Adversarial Risk for Multiobject Filtering. IEEE Transactions on Aerospace and Electronic Systems. 57(4). 2082–2092. 1 indexed citations
7.
Delande, Emmanuel, et al.. (2019). A new multi-target tracking algorithm for a large number of orbiting objects. Advances in Space Research. 64(3). 645–667. 17 indexed citations
8.
Delande, Emmanuel, et al.. (2017). A Second-Order PHD Filter With Mean and Variance in Target Number. IEEE Transactions on Signal Processing. 66(1). 48–63. 29 indexed citations
9.
Forder, John R., Daniel E. Clark, Andre Shih, et al.. (2016). Ventricular Fibrillation-Induced Cardiac Arrest Results in Regional Cardiac Injury Preferentially in Left Anterior Descending Coronary Artery Territory in Piglet Model. BioMed Research International. 2016. 1–6. 2 indexed citations
10.
Üney, Murat, B. Mulgrew, & Daniel E. Clark. (2016). Distributed localisation of sensors with partially overlapping field-of-views in fusion networks. Edinburgh Research Explorer (University of Edinburgh). 1340–1347. 8 indexed citations
11.
Clark, Daniel E. & Jérémie Houssineau. (2012). Hierarchical interacting stochastic population processes. arXiv (Cornell University). 1 indexed citations
12.
Clark, Daniel E. & Ronald Mahler. (2012). Generalized PHD filters via a general chain rule. International Conference on Information Fusion. 157–164. 25 indexed citations
13.
Clark, Daniel E., et al.. (2012). The PHD filter for extended target tracking with estimable extent shape parameters of varying size. International Conference on Information Fusion. 1111–1118. 16 indexed citations
14.
Houssineau, Jérémie, et al.. (2012). Disparity space: A parameterisation for Bayesian triangulation from multiple cameras. International Conference on Information Fusion. 1734–1740. 2 indexed citations
15.
Clark, Daniel E., et al.. (2011). The single-group PHD filter: An analytic solution. International Conference on Information Fusion. 1–8. 38 indexed citations
16.
Üney, Murat, Daniel E. Clark, & Simon Julier. (2011). Information measures in distributed multitarget tracking. UCL Discovery (University College London). 1–8. 22 indexed citations
17.
Clark, Daniel E., et al.. (2011). Fast sequential Monte Carlo PHD smoothing. International Conference on Information Fusion. 1–7. 6 indexed citations
18.
Clark, Daniel E., Ba-Tuong Vo, & Ba‐Ngu Vo. (2009). Forward-backward sequential Monte Carlo smoothing for joint target detection and tracking. UWA Profiles and Research Repository (University of Western Australia). 899–906. 6 indexed citations
19.
Vo, Ba‐Ngu, et al.. (2008). Gaussian mixture implementations of phd filters for non-linear dynamical models. Cambridge University Engineering Department Publications Database. 2 indexed citations
20.
Clark, Daniel E., Branko Ristić, & Ba‐Ngu Vo. (2008). PHD Filtering with target amplitude feature. UWA Profiles and Research Repository (UWA). 1–7. 14 indexed citations

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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