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Cited by 230Target Tracking and Data Fusion in Sensor NetworksInertial Sensor and NavigationRadar Systems and Signal Processing

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

A Scalable Algorithm for Tracking an Unknown Number of Targets Using Multiple Sensors

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Meyer, Florian, Braca, Paolo, Willett, Peter, Hlawatsch, Franz·2017·IEEE Transactions on Signal Processing
DOI 10.1109/tsp.2017.2688966
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Abstract

We propose an algorithm for tracking an unknown number of targets based on measurements provided by multiple sensors. Our algorithm achieves low computational complexity and excellent scalability by running belief propagation on a suitably devised factor graph. A redundant formulation of data association uncertainty and the use of “augmented target states” including binary target indicators make it possible to exploit statistical independencies for a drastic reduction of complexity. An increase in the number of targets, sensors, or measurements leads to additional variable nodes in the factor graph but not to higher dimensions of the messages. As a consequence, the complexity of our method scales only quadratically in the number of targets, linearly in the number of sensors, and linearly in the number of measurements per sensor. The performance of the method compares well with that of previously proposed methods, including methods with a less favorable scaling behavior. In particular, our method can outperform multisensor versions of the probability hypothesis density (PHD) filter, the cardinalized PHD filter, and the multi-Bernoulli filter.

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  • An emerging approach to MTT and information fusion, based on a factor graph and the sum-product algorithm (SPA), has flexibility, low complexity and useful scalability.

    “An emerging approach to MTT and information fusion - one with flexibility, low complexity and useful scalability - is based on a factor graph and the sum-product algorithm (SPA) [38], [39], [49].”

    From Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques (2021) · cites this paper as [38]

  • Space-based sensor technologies enable persistent monitoring of the maritime domain and ship traffic on a global scale even in remote areas of the Earth.

    “Besides the most common terrestrial sensors for MS, e.g., the automatic identification system (AIS), X-band radars, and over-the-horizon (OTH) radars, space-based sensor technologies enable persistent monitoring of the maritime domain and ship traffic on a global scale even in remote areas of the Earth.”

    From Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques (2021) · cites this paper as [1]

    The introduction (p. 1) discusses multitarget tracking for surveillance using sensors such as radar, sonar, and cameras; the conclusion (p. 12) concerns the tracking algorithm. Searches for space-based sensors, maritime ship traffic, and global monitoring found no passage addressing the claimed persistent coverage of remote areas. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Space-based sensors for Earth observation installed on satellites allow collecting images of very large areas in remote regions of the globe with relatively short latency.

    “Space-based sensors for Earth observation installed on satellites allow collecting images of very large areas in remote regions of the globe with relatively short latency, and hence are strongly relevant to MS.”

    From Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques (2021) · cites this paper as [1]

    The introduction (p. 1) discusses radar, sonar, and cameras as sensors for multitarget tracking, but does not address satellite Earth-observation imagery. Searches for satellite imagery, remote-area coverage, and latency found no passage supporting the attributed claim. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Citations

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Research works printed but not linked: 63

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Metadata

Authors
Meyer, F., Braca, P., Willett, P., & Hlawatsch, F.
Affiliations
  • NATO Centre for Maritime Research and Experimentation (IT)
  • University of Connecticut (US)
  • TU Wien (AT)
ORCID
Venue
IEEE Transactions on Signal Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2017-07-01
Volume
65
Issue
13
Pages
3478-3493
ISSN
  • 1053-587X (ISSN-L)
  • 1941-0476
DOI
10.1109/tsp.2017.2688966
OpenAlex
W2511568459
Cited by
230
References
1
Citations by year
2026: 17 · 2025: 16 · 2024: 36 · 2023: 24 · 2022: 22 · 2021: 29 · 2020: 26 · 2019: 26 · 2018: 25 · 2017: 7
FWCI
13.70
Type
journal article
Language
en
Open access
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Transactions on Signal Processing (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • NATO Supreme Allied Command Transformation (SAC000601, SAC000608)
  • Naval Postgraduate School
  • ONR (N00244-16-1-0017)
  • Austrian Science Fund (FWF) (P27370-N30, J3886-N31)
  • Czech National Sustainability Program (LO1401)
  • Czech Science Foundation (GAČR) (17-19638S)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
  • Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
  • Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
Keywords
multi-target tracking, unknown number of targets, multiple sensors, belief propagation, factor graphs, computational complexity reduction, scalability, probability hypothesis density filter, multi-Bernoulli filter

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