The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Deep learning for HRRP-based target recognition in multistatic radar systems
Abstract
In this paper, a deep learning approach is proposed for target recognition based on high range resolution profiles (HRRPs) in multistatic radar systems. The proposed deep learning approach employs deep convolutional neural networks to automatically extract features from the HRRPs. In a multistatic radar system the target can be observed from multiple aspect angles simultaneously which improves the reliability and robustness of target recognition. In this paper, the spatial diversity offered by a multistatic radar system is exploited by averaging the slocal classifier output probabilities from the different monostatic and bistatic transmitter/receiver pairs. The highest global target probability is compared to a threshold to decide whether the target is classified to one of the preknown classes or as unknown. Simulation results show that the proposed deep learning approach has very reliable classification performance even at low signal-to-noise ratios.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“A limitation of current evaluations of XAI methods is that they typically analyze peculiarities of the explanation methods without accounting for the interaction with the final user [3, 6, 7].”
From Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence (2023) · cites this paper as [7]
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Research works printed but not linked: 14
- lecun, 1990, Handwritten digit recognition with a back-propagation network, Proc Advances in Neural Information Processing Systems, 394
- 10.1002/9781118178072 · DOI
- einstein, 1984, Generation of high resolution radar range profiles and range profile auto-correlation functions using stepped frequency pulse trains, Tech Rep Massachusetts Institute of Technology Lincoln Laboratory
- chatzigeorgiadis, 2004, A MATLAB physical-optics RCS prediction code, IEEE Antennas and Propagation Magazine, 46, 137
- bastien, 2012, Theano: new features and speed improvements, NIPS 2012 Workshop on Deep Learning and Unsupervised Feature Learning
- bergstra, 2010, Theano: a CPU and GPU math expression compiler, Proceedings of the Python for Scientific Computing Conference (SciPy), 10.25080/Majora-92bf1922-003 · DOI
- 10.1109/RADAR.2014.6875666 · DOI
- 10.1049/iet-rsn.2013.0063 · DOI
- 10.14569/IJACSA.2012.030816 · DOI
- 10.1109/TGRS.2009.2025371 · DOI
- 10.1109/TAES.2014.120266 · DOI
- 10.2528/PIERC11030206 · DOI
- 10.1109/8.999623 · DOI
- 10.1155/2015/527095 · DOI
Cited by
Sorted by citations
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762
Metadata
- Authors
- Lunden, J., & Koivunen, V.
- Affiliations
- Aalto University (FI)
- ORCID
- Koivunen, Visa
- Venue
- 2016 IEEE Radar Conference (RadarConf)
- Publisher
- IEEE
- Published
- 2016-05
- Pages
- 1-6
- ISSN
- 1097-5659 (ISSN-L)
- OpenAlex
- W2435742192
- Cited by
- 82
- References
- 1
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 10 · 2023: 5 · 2022: 11 · 2021: 14 · 2020: 6 · 2019: 21 · 2018: 4 · 2017: 3
- FWCI
- 16.32
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Radar Conference (RadarConf) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Advanced SAR Imaging Techniques
- Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Wireless Signal Modulation Classification
- Keywords
- target recognition, high-resolution range profile, multistatic radar, bistatic radar, deep convolutional neural networks, spatial diversity, classifier combination, low signal-to-noise ratio
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: Deep learning for HRRP-based target recognition in multistatic radar systems. https://app.cochat.ai/papers/10.1109/radar.2016.7485271
- The paper
- Lunden, J., & Koivunen, V. (2016). Deep learning for HRRP-based target recognition in multistatic radar systems. 2016 IEEE Radar Conference (RadarConf). https://doi.org/10.1109/radar.2016.7485271
