The DOI matches its Crossref record.
Human walking estimation with radar
Abstract
Radar can be used to observe humans that are obscured by objects such as walls. These humans cannot be visually observed. The radar measurements are used to animate an obscured human in virtual reality. This requires detailed information about the motion. The radar measurements give detailed information about the movements of the human body parts; the Doppler signatures are time-varying and observed in the spectrogram. The authors focus on the extraction of parameters and describe a method for estimating human walking parameters from radar measurements. The parameters are estimated by minimising the difference between a simulated model and real measurements. A human walking model is presented which can be used both to calculate the radar response and to visually animate a walking person. The method is applied to real radar measurements of inbound walking humans from a distance of 20 m. The results show that estimation of the walking parameters is possible. The animated walking human generated with estimated parameters is a realistic likeness of the real walking human.
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
Cited by
Automotive Radar — From First Efforts to Future Systems
Waldschmidt, C., Hasch, J., & Menzel, W. (2021) · IEEE Journal of Microwaves · doi:10.1109/jmw.2020.3033616 · Cited by 580
Nuzzer: A Large-Scale Device-Free Passive Localization System for Wireless Environments
Seifeldin, M., Saeed, A., Kosba, A. E., El-Keyi, A., & Youssef, M. (2013) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2012.106 · Cited by 365
Doppler signatures of radar backscattering from objects with micro motions
Chen, V. C. (2008) · IET Signal Processing · doi:10.1049/iet-spr:20070137 · Cited by 146
Metadata
- Authors
- van Dorp, P., & Groen, F. C. A.
- Venue
- IEE Proceedings - Radar, Sonar and Navigation
- Published
- 2003-10-02
- Volume
- 150
- Issue
- 5
- Pages
- 356-365
- ISSN
- 1350-2395 (ISSN-L)
- 1359-7086
- OpenAlex
- W2093823111
- Cited by
- 261
- References
- 0
- FWCI
- 4.48
- 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: Human walking estimation with radar. https://app.cochat.ai/papers/10.1049/ip-rsn:20030568
- The paper
- van Dorp, P., & Groen, F. C. A. (2003). Human walking estimation with radar. IEE Proceedings - Radar, Sonar and Navigation. https://doi.org/10.1049/ip-rsn%3A20030568
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.