The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Detection, classification, and tracking of targets
Abstract
Networks of small, densely distributed wireless sensor nodes are being envisioned and developed for a variety of applications involving monitoring and the physical world in a tetherless fashion. Typically, each individual node can sense in multiple modalities but has limited communication and computation capabilities. Many challenges must be overcome before the concept of sensor networks In particular, there are two critical problems underlying successful operation of sensor networks: (1) efficient methods for exchanging information between the nodes and (2) collaborative signal processing (CSP) between the nodes to gather useful information about the physical world. This article describes the key ideas behind the CSP algorithms for distributed sensor networks being developed at the University of Wisconsin (UW). We also describe the basic ideas on how the CSP algorithms interface with the networking/routing algorithms being developed at Wisconsin (UW-API). We motivate the framework via the problem of detecting and tracking a single maneuvering target. This example illustrates the essential ideas behind the integration between UW-API and UW-CSP algorithms and also highlights the key aspects of detection and localization algorithms. We then build on these ideas to present our approach to tracking multiple targets that necessarily requires classification techniques becomes a reality.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Wavelet Tour of Signal Processing
(1999) · Elsevier eBooks · doi:10.1016/b978-0-12-466606-1.x5000-4 · Cited by 16,227
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Pattern classification and scene analysis
Chien, Y. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100577 · Cited by 3,398
An Introduction to Signal Detection and Estimation
Poor, H. V. (1988) · Springer Texts in Electrical Engineering · doi:10.1007/978-1-4757-3863-6 · Cited by 3,149
Instrumenting the world with wireless sensor networks
Estrin, D., Girod, L., Pottie, G., & Srivastava, M. (2002) · 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221) · doi:10.1109/icassp.2001.940390 · Cited by 1,164
Space-time processing for wireless communications
Paulraj, A. J., & Papadias, C. B. (1997) · IEEE Signal Processing Magazine · doi:10.1109/79.637317 · Cited by 1,030
Scalable Information-Driven Sensor Querying and Routing for Ad Hoc Heterogeneous Sensor Networks
Chu, M., Haussecker, H., & Zhao, F. (2002) · The International Journal of High Performance Computing Applications · doi:10.1177/10943420020160030901 · Cited by 634
Blind beamforming on a randomly distributed sensor array system
Yao, K., Hudson, R. E., Reed, C. W., Chen, D.-C., & Lorenzelli, F. (1998) · IEEE Journal on Selected Areas in Communications · doi:10.1109/49.730461 · Cited by 255
Semantic Information Fusion for Coordinated Signal Processing in Mobile Sensor Networks
Friedlander, D. S., & Phoha, S. (2002) · The International Journal of High Performance Computing Applications · doi:10.1177/10943420020160030401 · Cited by 40
Cited by
A synchronized quorum of genetic clocks
Danino, T., Mondragón-Palomino, O., Tsimring, L., & Hasty, J. (2010) · Nature · doi:10.1038/nature08753 · Cited by 992
Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
Duchi, J. C., Agarwal, A., & Wainwright, M. J. (2012) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2011.2161027 · Cited by 947
Machine learning in wireless sensor networks: Algorithms, strategies, and applications
Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2320099 · Cited by 965
Connecting the physical world with pervasive networks
Estrin, D., Culler, D., Pister, K., & Sukhatme, G. (2002) · IEEE Pervasive Computing · doi:10.1109/mprv.2002.993145 · Cited by 836
Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810
Metadata
- Authors
- Li, D., Wong, K. D., Hu, Y. H., & Sayeed, A. M.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2002-03
- Volume
- 19
- Issue
- 2
- Pages
- 17-29
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W2097380742
- Cited by
- 932
- References
- 9
- FWCI
- 40.72
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Detection, classification, and tracking of targets. https://app.cochat.ai/papers/10.1109/79.985674
- The paper
- Li, D., Wong, K. D., Hu, Y. H., & Sayeed, A. M. (2002). Detection, classification, and tracking of targets. IEEE Signal Processing Magazine. https://doi.org/10.1109/79.985674
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
