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Target Tracking and Data Fusion in Sensor Networks

Target Tracking and Data Fusion in Sensor Networks is a research topic within Artificial Intelligence. Science Explorer counts 36k research works in it since 1950. 9.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on particle filtering and nonlinear estimation methods, including Kalman filters, Monte Carlo methods, sensor fusion, sequential Monte Carlo, Gaussian filters, and Bayesian inference. The papers cover a wide range of applications such as state estimation, multitarget tracking, and sensor network management.

  • Particle Filters
  • Nonlinear Estimation
  • Kalman Filters
  • Monte Carlo Methods
  • State Estimation
  • Sensor Fusion
  • Sequential Monte Carlo
  • Gaussian Filters
  • Multitarget Tracking
  • Bayesian Inference
Research works
36k
fractional, since 1950
In the world top 10%
3.5k
per year above
Top-10% rate
9.8%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+6%
the tick is no change
Largest producer
China
62% of the 10 largest, 2022-now
Target Tracking and Data Fusion in Sensor Networks

Which countries lead Target Tracking and Data Fusion in Sensor Networks research?

By volume, China and the United States publish the most (3k and 687 works in 2022-now).

By volume, 2022-now

  1. 1 China 3k works
  2. 2 United States 687 works
  3. 3 Germany 199 works
  4. 4 India 189 works
  5. 5 United Kingdom 143 works
  6. 6 France 135 works
  7. 7 South Korea 132 works
  8. 8 Italy 120 works
  9. 9 Canada 107 works
  10. 10 Japan 92 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 62.3%United States: 14.4%Germany: 4.2%India: 3.9%6 others listed: 15.2%62%largest
China2,986 · 62.3%United States687 · 14.4%Germany199 · 4.2%India189 · 3.9%6 others listed728 · 15.2%

Shares of the rows listed above, not of the whole node.

Which institutions lead Target Tracking and Data Fusion in Sensor Networks research?

By volume in 2022-now, Northwestern Polytechnical University publishes the most Target Tracking and Data Fusion in Sensor Networks research, followed by Chinese Academy of Sciences and University of Electronic Science and Technology of China.

By volume, 2022-now

  1. 1 Northwestern Polytechnical UniversityChina 128 works
  2. 2 Chinese Academy of SciencesChina 109 works
  3. 3 University of Electronic Science and Technology of ChinaChina 97 works
  4. 4 Beihang UniversityChina 97 works
  5. 5 National University of Defense TechnologyChina 96 works
  6. 6 Beijing Institute of TechnologyChina 86 works
  7. 7 Harbin Institute of TechnologyChina 85 works
  8. 8 Harbin Engineering UniversityChina 80 works
  9. 9 Xidian UniversityChina 69 works
  10. 10 Nanjing University of Aeronautics and AstronauticsChina 58 works

Who are the leading researchers in Target Tracking and Data Fusion in Sensor Networks?

The most-cited researchers publishing on Target Tracking and Data Fusion in Sensor Networks include Geoffrey E. Hinton, Xiangyu Zhang and Karl John Friston.

  1. 1 Geoffrey E. Hinton Canada 454k citations
  2. 2 Xiangyu Zhang 327k citations
  3. 3 Karl John Friston United Kingdom 310k citations
  4. 4 Wei Huang China 267k citations
  5. 5 Xiaogang Wang China 260k citations
  6. 6 Deepak L. Bhatt United States 227k citations
  7. 7 Jerome H. Friedman United States 224k citations
  8. 8 Kai You Wang China 224k citations
  9. 9 Trevor J. Darrell United States 206k citations
  10. 10 Paul M. Thompson United States 196k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Target Tracking and Data Fusion in Sensor Networks?

The most cited paper in Target Tracking and Data Fusion in Sensor Networks is "A new look at the statistical model identification" (Hirotugu Akaike, 1974), with 51,685 citations. Next come "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking" (11,594) and "Novel approach to nonlinear/non-Gaussian Bayesian state estimation" (7,664).

Most cited papers
#PaperReleasedCitations
1 A new look at the statistical model identification Hirotugu Akaike · IEEE Transactions on Automatic Control Open access Dec 1974 51,685
2 A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking M.S. Arulampalam, Simon R. Maskell, Neil Gordon et al. · IEEE Transactions on Signal Processing 2002 11,594
3 Novel approach to nonlinear/non-Gaussian Bayesian state estimation Neil Gordon, David J. Salmond, A. F. M. Smith · IEE Proceedings F Radar and Signal Processing 1993 7,664
4 Unscented Filtering and Nonlinear Estimation Simon Julier, Jeffrey K. Uhlmann · Proceedings of the IEEE Mar 2004 6,611
5 New Results in Linear Filtering and Prediction Theory Rudolf E. Kalman, R. S. Bucy · Journal of Basic Engineering Mar 1961 6,409
All 100 most cited papers, with filters and sorting

Where is Target Tracking and Data Fusion in Sensor Networks research done?

The largest centres of Target Tracking and Data Fusion in Sensor Networks research in 2022-now are Beijing (China), Xi'an (China), Nanjing (China) and Harbin (China). Among places with at least 20 works in it, it is an unusually large share of all research in Harbin and Xi'an.

Largest cities, 2022-now

  1. 1 BeijingChina 577 works
  2. 2 Xi'anChina 358 works
  3. 3 NanjingChina 235 works
  4. 4 HarbinChina 226 works
  5. 5 ShanghaiChina 157 works
  6. 6 ChengduChina 153 works
  7. 7 ChangshaChina 127 works
  8. 8 WuhanChina 106 works
  9. 9 HangzhouChina 95 works
  10. 10 GuangzhouChina 60 works

Where it is the local speciality

  1. HarbinCN · 225.8 works10×
  2. Xi'anCN · 357.8 works8.2×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Target Tracking and Data Fusion in Sensor Networks than the world average.

See Target Tracking and Data Fusion in Sensor Networks on the map

Where is the best place to study Target Tracking and Data Fusion in Sensor Networks?

Among universities, judged by research, Nanyang Technological University, Southeast University and University of Electronic Science and Technology of China score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 18.44%fractional works in this field (log) →share of work in the world top 10% →Nanyang Technologica…Southeast UniversityUniversity of Electr…Northwestern Polytec…
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Nanyang Technological UniversitySingapore 67.130.0%3.7×15 +29.3%
2Southeast UniversityChina 67.018.0%7.5×55 +30.8%
3University of Electronic Science and Technology of ChinaChina 65.715.3%17.5×97 +21.0%
4Northwestern Polytechnical UniversityChina 61.912.2%22.6×128 +12.6%
5Hong Kong Polytechnic UniversityChina 61.824.0%3.1×13 +221.0%
6Harbin Institute of TechnologyChina 57.911.0%10.4×85 +29.8%
7Xidian UniversityChina 57.410.7%19.1×69 +28.2%
8China University of Mining and TechnologyChina 57.430.3%4.1×16 +8.6%
9Beijing Institute of TechnologyChina 56.910.3%14.8×86 +16.0%
10Dalian Maritime UniversityChina 56.322.6%11.4×21 +80.2%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Target Tracking and Data Fusion in Sensor Networks, country by country

Is Target Tracking and Data Fusion in Sensor Networks research growing?

Output in 2022-2025 was 6% higher than in 2018-2021, peaking in 2011. The fastest-growing topics are Target Tracking and Data Fusion in Sensor Networks.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Target Tracking and Data Fusion in Sensor Networks as a percentage of every work published that year, 0.025% in 2024.

still counting0.025%0.050%0.075%Target Tracking and Data Fusion in Sensor Networks0.026% in 202519801990200020102020

Fractional works of Target Tracking and Data Fusion in Sensor Networks over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Target Tracking and Data Fusion in Sensor Networks9.7% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.

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