Sidelobe Suppression Techniques for Near-Field Multistatic SAR

Multirotor Unmanned Air Systems (UAS) represent a significant improvement in capability for Synthetic Aperture Radar (SAR) imaging when compared to traditional, fixed-wing, platforms. In particular, a swarm of UAS can generate significant measurement diversity through variation of spatial and freque...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-01, Vol.23 (2), p.732
Main Authors: Price, George A J, Moate, Chris, Andre, Daniel, Yuen, Peter
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
compressive sensing
Data Compression
Diagnostic Imaging
Image Processing, Computer-Assisted - methods
Image quality
Matched pursuit
multistatic
Near fields
Radar
Radar imaging
SAR
Sensor arrays
Sensors
Sidelobe reduction
Sidelobes
Sparsity
Synthetic aperture radar
Three dimensional imaging
UAS
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Summary:Multirotor Unmanned Air Systems (UAS) represent a significant improvement in capability for Synthetic Aperture Radar (SAR) imaging when compared to traditional, fixed-wing, platforms. In particular, a swarm of UAS can generate significant measurement diversity through variation of spatial and frequency collections across an array of sensors. In such imaging schemes, the image formation step is challenging due to strong extended sidelobe; however, were this to be effectively managed, a dramatic increase in image quality is theoretically possible. Since 2015, QinetiQ have developed the RIBI system, which uses multiple UAS to perform short-range multistatic collections, and this requires novel near-field processing to mitigate the high sidelobes observed and form actionable imagery. This paper applies a number of algorithms to assess image reconstruction of simulated near-field multistatic SAR with an aim to suppress sidelobes observed in the RIBI system, investigating techniques including traditional SAR processing, regularised linear regression, compressive sensing. In these simulations presented, Elastic net, Orthogonal Matched Pursuit, and Iterative Hard Thresholding all show the ability to suppress sidelobes while preserving accuracy of scatterer RCS. This has also lead to a novel processing approach for reconstructing SAR images based on the observed Elastic net and Iterative Hard Thresholding performance, mitigating weaknesses to generate an improved combined approach. The relative strengths and weaknesses of the algorithms are discussed, as well as their application to more complex real-world imagery.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23020732