Abstract
In this report, we propose compressed sensing inverse synthetic aperture radar (ISAR) imaging in the presence of highly maneuvering motion using a modified orthogonal matching pursuit (OMP) reconstruction algorithm. Unlike existing methods where motion is limited to first- or second-order phase terms, we take into account realistic motion of a maneuvering target that can involve a third-order phase term corresponding to the rate of rotational acceleration. In addition, unlike existing fixed dictionary-based methods, which require designing a large dictionary that needs to take into account all of the possible motion parameters, we propose a modified OMP reconstruction method that requires a dictionary only based on the first-order phase term and estimates the secondand third-order phase terms using an optimization algorithm. Simulation examples and comparison with existing methods show the viability of our approach for imaging moving targets consisting of higher order motion.
Highlights
Compressed sensing (CS) has demonstrated that a signal sparse in a certain dictionary can be undersampled to a certain extent and recovered without any aliasing artifacts
We present numerical results that show the effects of the level of mismatch in rotational acceleration and rotational acceleration rate on a fixed dictionary performance, further motivating the use of Dictionary-based GAOMP (DGAOMP)
We presented a new compressed sensing inverse synthetic aperture radar (ISAR) imaging algorithm in order to deal with the case of high maneuvering motion that consists of rotational acceleration rate
Summary
Compressed sensing (CS) has demonstrated that a signal sparse in a certain dictionary can be undersampled to a certain extent and recovered without any aliasing artifacts. The undersampling would mean that a signal can be converted into a digital form using a lower number of samples compared to those required by the Nyquist sampling theorem. This undersampling can lead to benefits, such as reduced data acquisition time, lower data sampling, resulting in smaller data size, helping in compressing the original signal, etc. Applications of CS have been demonstrated in different works, such as single-pixel remote sensing [10], tomographic SAR [11] , through-the-wall imaging for stationary and moving targets [12] , radar imaging [13,14,15], passive radar imaging [16], sparse microwave imaging of perfect electric conducting targets [17], etc
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