Abstract
Multi-channel Synthetic Aperture Radar (MSAR) in the maritime environment has proven to be an effective technique for improved image fidelity and scene characterization. Past demonstrations of MSAR-based methods for improvements such as motion induced distortion correction and image classification were performed using a uniformly spaced array of phase centers. In this paper, we derive novel extensions of these techniques to the case of sparse and non-uniformly spaced phase center configurations and demonstrate their effectiveness for a variety of platforms and sensor arrangements. We establish general techniques for correcting motion induced distortions, establish theoretical bounds of performance, and empirically validate our techniques on data derived from our airborne MSAR system. We then use these images to characterize the structure of the maritime scene using our MSAR-based classification technique. Our imaging and classification techniques are amenable to efficient implementation on diverse hardware platforms and sensor arrangements, and thus can find ready application in practical multichannel radar systems.
Highlights
Many applications in modern remote sensing and surveillance have tended towards the use of multiple sensors to probe the environment
EXPERIMENTAL RESULTS FOR SPARSE multichannel synthetic aperture radar (MSAR) BASED CLASSIFICATION The previous sections have both theoretically and empirically established the robustness of the MULTI-CHANNEL ALONG TRACK INTERFEROMETRY (MATI) algorithm compared to Non-Uniform Discrete Fourier Transform (NUDFT) based approach to motion distortion correction
We find that our sparse MSAR classification procedure is remarkably robust to variations in the sampling configurations and that for even extremely non-uniform sampling cases, i.e. Configuration C, the estimation of the eigenfeatures compare remarkably well to the baseline uniform sampling case in Configuration A
Summary
Many applications in modern remote sensing and surveillance have tended towards the use of multiple sensors to probe the environment. These include: i) SAR-MTI methods based on analysis of the Doppler rate maps obtained from the range compressed raw data [7]; ii) Time-frequency based matching pursuit analysis of fluctuating scatterers in processed SAR images [8]; iii) Scatterer velocity estimation from the smear length of the scatterer (for the case when scatterer motion is only along azimuth direction) [6]; iv) Scatterer velocity estimation directly from a time-frequency analysis of chirp center frequency and chirp rates [6] All these methods suffer from a key limitation of ambiguities between stationary and moving returned signal histories and of being highly dependent on the SNR associated with the point scatterers.
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