Browsing by Author "Rattan, Anmol"

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    Multistatic hybrid SAR/ISAR data generation using a stationary target
    (Institution of Engineering and Technology (IET), 2023-02-07) Rattan, Anmol; Andre, Daniel; Finnis, Mark
    There is great interest in multistatic synthetic aperture radar (SAR) systems as they are capable of providing high resolution images. These systems could prove promising candidates for provision of surveillance for both military and civilian interest. Both multistatic SAR and its counterpart, multistatic inverse synthetic aperture radar (ISAR), are limited by their assumptions of observing a stationary target from a moving platform and vice-versa. Hence, without adequate target motion compensation, their resultant radar images appear defocused. Arranging experiments capable of providing repeatable multistatic hybrid SAR/ISAR data of real moving targets can be difficult and costly. One viable approach is the novel method presented in this study, whereby multistatic hybrid SAR/ISAR data can be collected of a target moving with a theoretical motion, without the requirement of an actual moving target – the theoretical motion is brought about through the appropriate motion of antennas. The study demonstrates, both through simulation and experimentation, how radar trajectories of a given SAR system can be altered to arrive at the equivalent setup of observing a moving target. Results from simulation and from an experiment conducted at the Cranfield University Ground-Based SAR (GBSAR) laboratory are presented, showing the utility of this approach.
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    Multistatic synthetic aperture radar autofocus for back projection imaging of a moving target
    (Institution of Engineering and Technology (IET), 2025-01) Rattan, Anmol; Andre, Daniel; Finnis, Mark V.
    Synthetic Aperture Radar (SAR) plays a vital role in the surveillance of terrestrial and maritime targets, which are commonly in motion. As such, the ability to perform accurate real‐time focusing and localisation on moving targets, particularly those moving with complex motion, is desired. Many existing autofocus algorithms struggle to achieve this and rely on sub‐aperture processing of SAR data to estimate and compensate for phase errors attributed to unknown target motion. This paper presents a new metric‐based autofocus approach, called Localised Threshold Sharpness (LTS), which employs multistatic SAR data to localise and focus a target moving with up to six degrees of freedom motion on a real‐time, pulse‐by‐pulse basis. The algorithm is verified with experimental data, and its performance is compared against the performance of an existing measure of image sharpness suitable for pulse‐by‐pulse autofocusing, namely the intensity‐squared metric, with varying levels of added noise. Normalised cross‐correlation results demonstrate a resemblance of at least 80% between Multistatic SAR images focused via LTS autofocus and Multistatic SAR images ideally focused using target motion knowledge for signal‐to‐noise ratios above 3 dB.