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Theory and design of interferometric synthetic aperture radars
Abstract
Interferometric synthetic aperture radar (InSAR) is a method which may provide a means of estimating global topography with high spatial resolution and height accuracy. The paper presents a derivation of the signal statistics, an optimal estimator of the interferometric phase, and the expressions necessary to calculate the height-error budget. These expressions are used to derive methods of optimising the InSAR-system parameters, and are then used in a specific design example for a system to perform high-resolution global topographic mapping with a one-year mission lifetime, subject to current technological constraints. Finally, a Monte Carlo simulation of this InSAR system is performed to evaluate its performance for realistic topography. The results indicate that this system has the potential to satisfy the stringent accuracy and resolution requirements for geophysical use of global topographic data.
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Cited by
A Global Assessment of the SRTM Performance
Rodríguez, E., Morris, C. S., & Belz, J. E. (2006) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.72.3.249 · Cited by 1,106
Metadata
- Authors
- Rodriguez, E., & Martin, J. M.
- Venue
- IEE Proceedings F Radar and Signal Processing
- Published
- 1992
- Volume
- 139
- Issue
- 2
- Pages
- 147
- ISSN
- 0956-375X (ISSN-L)
- OpenAlex
- W2148505991
- Cited by
- 775
- References
- 0
- FWCI
- 19.25
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Theory and design of interferometric synthetic aperture radars. https://app.cochat.ai/papers/10.1049/ip-f-2.1992.0018
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- Rodriguez, E., & Martin, J. M. (1992). Theory and design of interferometric synthetic aperture radars. IEE Proceedings F Radar and Signal Processing. https://doi.org/10.1049/ip-f-2.1992.0018
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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.
