Science Explorer Interactive view Map

Advanced Adaptive Filtering Techniques

Advanced Adaptive Filtering Techniques is a research topic within Computational Mechanics. Science Explorer counts 30k research works in it since 1950. 12.0% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the topic of adaptive filtering in non-Gaussian signal processing, with an emphasis on diffusion strategies, kernel algorithms, sparse system identification, active noise control, distributed estimation, variable step-size algorithms, robust adaptive filtering, and the application of correntropy criterion. The papers cover a wide range of applications and performance analyses for adaptive filtering techniques in complex and non-Gaussian signal environments.

  • Adaptive Filtering
  • Non-Gaussian Signal Processing
  • Diffusion Strategies
  • Kernel Algorithms
  • Sparse System Identification
  • Active Noise Control
  • Distributed Estimation
  • Variable Step-Size Algorithms
  • Robust Adaptive Filtering
  • Correntropy Criterion
Research works
30k
fractional, since 1950
In the world top 10%
3.6k
per year above
Top-10% rate
12.0%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
-19%
the tick is no change

Which countries lead Advanced Adaptive Filtering Techniques research?

By volume, China and India publish the most (1.2k and 337 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 1.2k works
  2. 2 India 337 works
  3. 3 United States 225 works
  4. 4 Japan 152 works
  5. 5 United Kingdom 98 works
  6. 6 Germany 95 works
  7. 7 Canada 57 works
  8. 8 South Korea 57 works
  9. 9 France 57 works
  10. 10 Taiwan 43 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: 51.4%India: 14.6%United States: 9.8%Japan: 6.6%6 others listed: 17.6%51%largest
China1,183 · 51.4%India337 · 14.6%United States225 · 9.8%Japan152 · 6.6%6 others listed406 · 17.6%

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

Which institutions lead Advanced Adaptive Filtering Techniques research?

By volume in 2022–2025, University of Electronic Science and Technology of China publishes the most Advanced Adaptive Filtering Techniques research, followed by Southwest Jiaotong University and Northwestern Polytechnical University.

Who are the leading researchers in Advanced Adaptive Filtering Techniques?

The most-cited researchers publishing on Advanced Adaptive Filtering Techniques include H. Vincent Poor, Georgios B. Giannakis and Andreas F. Molisch.

  1. 1 H. Vincent Poor United States 9.5k citations
  2. 2 Georgios B. Giannakis United States 4.6k citations
  3. 3 Andreas F. Molisch United States 4.3k citations
  4. 4 Dong Yu United States 4.1k citations
  5. 5 Yonina C. Eldar Israel 4k citations
  6. 6 Jerry M. Mendel United States 4k citations
  7. 7 Petre Stoica Sweden 3.8k citations

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

Where is Advanced Adaptive Filtering Techniques research done?

The largest centres of Advanced Adaptive Filtering Techniques research in 2022–2025 are Beijing (China), Chengdu (China), Xi'an (China) and Nanjing (China).

Largest cities, 2022–2025

  1. 1 Beijing China 197 works
  2. 2 Chengdu China 120 works
  3. 3 Xi'an China 105 works
  4. 4 Nanjing China 83 works
  5. 5 Tokyo Japan 66 works
  6. 6 Shanghai China 62 works
  7. 7 Chongqing China 55 works
  8. 8 Harbin China 51 works
  9. 9 Wuhan China 48 works
  10. 10 Hangzhou China 34 works
See Advanced Adaptive Filtering Techniques on the map

Where is the best place to study Advanced Adaptive Filtering Techniques?

Among universities, judged by research, Southwest Jiaotong University, Nanyang Technological University and Southwest University 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 16.46%fractional works in this node (log) →share in the world top 10% →Southwest Jiaotong University: 46, 17.9%Nanyang Technological University: 18, 41.9%Southwest University: 24, 14.9%University of Electronic Science and Technology of China: 47, 16.3%Northwestern Polytechnical University: 40, 19.1%Harbin Engineering University: 25, 9.5%Institut National de la Recherche Scientifique: 11, 19.4%University of Southampton: 16, 13.2%Southwest University of Science and Technology: 12, 12.4%University of Blida: 10, 0.0%Nanyang Technologica…Southwest Jiaotong U…University of Electr…Southwest University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Southwest Jiaotong UniversityChina 74.317.9%20.4×46 +248.3%
2 Nanyang Technological UniversitySingapore 66.341.9%7.5×18 -50.4%
3 Southwest UniversityChina 65.014.9%15.4×24 +954.9%
4 University of Electronic Science and Technology of ChinaChina 61.716.3%14.1×47 -2.3%
5 Northwestern Polytechnical UniversityChina 61.519.1%11.8×40 -3.7%
6 Harbin Engineering UniversityChina 52.39.5%16.5×25 +54.6%
7 Institut National de la Recherche ScientifiqueCanada 51.719.4%48.9×11 -14.2%
8 University of SouthamptonUnited Kingdom 50.413.2%11.5×16 -20.1%
9 Southwest University of Science and TechnologyChina 50.312.4%18.2×12 +134.1%
10 University of BlidaAlgeria 46.30.0%59.2×10 +208.0%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Advanced Adaptive Filtering Techniques research growing?

Output in 2018–2022 was 19% lower than in 2013–2017, peaking in 2002.

19801990200020102020
grewheldshrank

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

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.