Jason Ford

1.5k total citations
101 papers, 1.0k citations indexed

About

Jason Ford is a scholar working on Aerospace Engineering, Control and Systems Engineering and Artificial Intelligence. According to data from OpenAlex, Jason Ford has authored 101 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Aerospace Engineering, 34 papers in Control and Systems Engineering and 27 papers in Artificial Intelligence. Recurrent topics in Jason Ford's work include Target Tracking and Data Fusion in Sensor Networks (26 papers), Fault Detection and Control Systems (16 papers) and Infrared Target Detection Methodologies (14 papers). Jason Ford is often cited by papers focused on Target Tracking and Data Fusion in Sensor Networks (26 papers), Fault Detection and Control Systems (16 papers) and Infrared Target Detection Methodologies (14 papers). Jason Ford collaborates with scholars based in Australia, United States and Iran. Jason Ford's co-authors include Luis Mejías, John Lai, Timothy L. Molloy, Peter O’Shea, Tristán Pérez, Gerard Ledwich, Hassan Bevrani, T. G. Farr, Charles Werner and T. W. Thompson and has published in prestigious journals such as IEEE Transactions on Automatic Control, IEEE Transactions on Information Theory and Automatica.

In The Last Decade

Jason Ford

90 papers receiving 987 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jason Ford Australia 18 518 270 261 204 174 101 1.0k
Jimmy Krozel United States 24 1.3k 2.5× 226 0.8× 273 1.0× 149 0.7× 82 0.5× 90 1.6k
Haiyang Chao United States 20 993 1.9× 356 1.3× 510 2.0× 267 1.3× 130 0.7× 74 1.6k
Cristina Barrado Spain 18 776 1.5× 366 1.4× 125 0.5× 115 0.6× 136 0.8× 94 1.4k
Trey Smith United States 18 445 0.9× 466 1.7× 169 0.6× 424 2.1× 60 0.3× 56 1.3k
Enric Pastor Spain 19 660 1.3× 286 1.1× 113 0.4× 103 0.5× 238 1.4× 116 1.5k
Andrea Cristofaro Italy 17 318 0.6× 129 0.5× 741 2.8× 114 0.6× 95 0.5× 80 1.3k
Amadou Gning United Kingdom 14 274 0.5× 144 0.5× 178 0.7× 583 2.9× 117 0.7× 42 798
Lingxia Mu China 13 162 0.3× 260 1.0× 260 1.0× 60 0.3× 50 0.3× 55 731
Valentin Polishchuk United States 16 424 0.8× 160 0.6× 100 0.4× 69 0.3× 119 0.7× 102 864
Jifeng Guo China 16 439 0.8× 232 0.9× 214 0.8× 203 1.0× 82 0.5× 79 916

Countries citing papers authored by Jason Ford

Since Specialization
Citations

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

Fields of papers citing papers by Jason Ford

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jason Ford. 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 Jason Ford. The network helps show where Jason Ford may publish in the future.

Co-authorship network of co-authors of Jason Ford

This figure shows the co-authorship network connecting the top 25 collaborators of Jason Ford. A scholar is included among the top collaborators of Jason Ford 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 Jason Ford. Jason Ford 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
1.
Ford, Jason, et al.. (2024). A Framework for Bayesian Quickest Change Detection in General Dependent Stochastic Processes. IEEE Control Systems Letters. 8. 790–795. 2 indexed citations
2.
Ford, Jason, et al.. (2023). Exactly Optimal Quickest Change Detection of Markov Chains. IEEE Control Systems Letters. 1–1. 1 indexed citations
3.
Donaire, Alejandro, et al.. (2021). String stable integral control design for vehicle platoons with disturbances. QUT ePrints (Queensland University of Technology). 21 indexed citations
4.
Donaire, Alejandro, et al.. (2020). String stable integral control of vehicle platoons with actuator dynamics and disturbances. QUT ePrints (Queensland University of Technology). 1 indexed citations
5.
Ford, Jason, et al.. (2019). Bushfire emergency response uncertainty quantification. QUT ePrints (Queensland University of Technology).
6.
Ford, Jason, et al.. (2018). Learning to Detect Aircraft for Long-Range Vision-Based Sense-and-Avoid Systems. IEEE Robotics and Automation Letters. 3(4). 4383–4390. 24 indexed citations
7.
Ford, Jason, et al.. (2017). Change Detection for Undermodelled Processes Using Mismatched Hidden Markov Model Test Filters. IEEE Control Systems Letters. 1(2). 238–243. 1 indexed citations
8.
Elliott, Robert J., Jason Ford, & J.B. Moore. (2015). On-line almost-sure parameter estimation for partially observed discrete-time linear systems with known noise charateristics. ANU Open Research (Australian National University). 1 indexed citations
9.
Molloy, Timothy L. & Jason Ford. (2014). Asymptotic minimax robust and misspecified Lorden quickest change detection for dependent stochastic processes. QUT ePrints (Queensland University of Technology). 1–8. 2 indexed citations
10.
Ford, Jason, et al.. (2014). Compressed sensing using hidden Markov models with application to vision based aircraft tracking. QUT ePrints (Queensland University of Technology). 1–8. 1 indexed citations
11.
Molloy, Timothy L., Jason Ford, & Luis Mejías. (2014). Looming Aircraft Threats: Shape-based Passive Ranging of Aircraft from Monocular Vision. QUT ePrints (Queensland University of Technology). 11 indexed citations
12.
Molloy, Timothy L. & Jason Ford. (2012). HMM triangle relative entropy concepts in sequential change detection applied to vision-based dim target manoeuvre detection. QUT ePrints (Queensland University of Technology). 255–262. 5 indexed citations
13.
Ford, Jason, et al.. (2012). Fusion of morphological images for airborne target detection. QUT ePrints (Queensland University of Technology). 1180–1187. 6 indexed citations
14.
Ford, Jason, Valery Ugrinovskii, & John Lai. (2011). An infinite-horizon robust filter for uncertain hidden Markov models with conditional relative entropy constraints. QUT ePrints (Queensland University of Technology). 499–506. 2 indexed citations
15.
Dong, Zhao Yang, et al.. (2008). Adaptive and optimal under frequency load shedding. QUT ePrints (Queensland University of Technology). 10 indexed citations
16.
Lai, John, Jason Ford, Peter O’Shea, Rodney Walker, & Michael Bosse. (2008). A Study of Morphological Pre-Processing Approaches for Track-Before-Detect Dim Target Detection. QUT ePrints (Queensland University of Technology). 15 indexed citations
17.
Ford, Jason. (2004). Optimal stopping time guidance: deterministic and stochastic targets. QUT ePrints (Queensland University of Technology). 3. 1826–1832. 1 indexed citations
18.
Carey, Nicole, Jason Ford, & Javaan Chahl. (2004). Biologically inspired guidance for motion camouflage. QUT ePrints (Queensland University of Technology). 3. 1793–1799. 19 indexed citations
19.
Ford, Jason. (2001). Precision Guidance with Impact Angle Requirements. Faculty of Built Environment and Engineering. 17(4). 193–7. 1 indexed citations
20.
Ford, Jason, et al.. (1993). Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states. QUT ePrints (Queensland University of Technology). 3 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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