Velocity-ISAR: On the application of ISAR techniques to multichannel SAR imaging

Raj, Raghu G.; Jansen, Robert W.; Lipps, R.; Sletten, Mark A.; Rosenberg, Luke

doi:10.1109/radar.2015.7131149

Cited by 10 publications

(10 citation statements)

References 20 publications

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“…In the late 1990s, the theoretical work of Friedlander and Porat [6] showed how this information can be utilized by VSAR processing to form a coherent fusion of multiple SAR images (one for each phase center)-called the VSAR image-that, as suggested in their initial studies [6], tends to focus scatterers much better than individual SAR images. The scope and effectiveness of VSAR processing has subsequently been characterized in more detail by recent experimental works [1][2][3][4][5].…”

Section: Exploiting Multichannel Structurementioning

confidence: 99%

“…In this paper, however, we only focus on VSAR processing. We further point out that due to channel and antenna imperfections, additional processing may be needed to further enhance phase coherency across channels [1][2]. In the experiments in Section V, however, channel balancing was not applied as it did not significantly improve the imaging results for the particular boat that we image in this paper.…”

Section: Exploiting Multichannel Structurementioning

confidence: 99%

“…Indeed different applications may call for alternative methods of image formation. For example in [2] we showed when imaging rigid bodies in maritime conditions, superior overall image quality can be obtained by combining velocity processing with ISAR imaging (which we call VISAR imaging).…”

Section: Exploiting Multichannel Structurementioning

confidence: 99%

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A sparsity based approach to velocity SAR imaging

Raj

Jansen

Sletten

2016

2016 IEEE Radar Conference (RadarConf)

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Abstract-We recently successfully developed an airborne MSAR (Multichannel Synthetic Aperture Radar) test bed system that consists of 32 along-track phase centers through the use of two transmit horns and 16 receive antennas [1-4]. We have subsequently deployed this system, both in September 2014 and more recently in October 2015, to perform extensive and systematic data collections on a variety of land-based and maritime targets under different environmental conditions. The resulting data poses important signal processing challenges pertaining to optimum ways of combining the signals obtained from various channels so that the underlying information of interest can be effectively extracted in the presence of noise and clutter. In this paper we focus on the imaging problem and propose a novel method of simultaneously exploiting the multichannel structure of the data acquisition and the underlying sparse structure of the scene being imaged. After giving a brief overview of our airborne NRL MSAR system and the basics of velocity processing, we proceed to describe our novel algorithm and demonstrate our initial experimental results. The novelty of this paper is two-fold: to the best of our knowledge, this is first time that velocity processing has been used in conjunction with sparsity based processing; and that the resulting approach is applied to real data captured by our airborne NRL MSAR system.Keywords-Imaging; Synthetic Aperture Radar (SAR); Multichannel SAR (MSAR); Sparsity; Velocity Processing; Wavelets I.NRL MSAR SYSTEM The NRL MSAR is an airborne system based on the NRL Focused Phased Array Imaging Radar (FOPAIR), a groundbased MSAR test bed [3,5]. The MSAR system operates at Xband with a center frequency of 9.875 GHz and uses linear FM chirped waveforms with a bandwidth of 220 MHz to achieve a range resolution of approximately 0.7 m. The peak radiated power is approximately 1.4 kW, while the aggregate pulse repetition frequency (PRF) of 25 kHz and pulse length of 6 µs produce an average power of 210 W. The system flies on a Saab 340 aircraft using a belly-mounted radome with a nominal incidence angle of 70 o . Typical altitude and airspeeds are 914 m (3000 ft.) and 70 m/s, respectively. Fig. 1 shows a schematic of our airborne system for data collection of land and maritime targets; and Fig. 2 shows a close-up of the radome and our MSAR system. The airborne MSAR system consists of two modularly constructed 16 channel sections from FOPAIR. Though we had a maximum of 16 receive elements in our MSAR system, due to our on-board data storage constraints we received on every other element which was found to be the maximum that we could process while still providing sufficient velocity resolution. To improve the system still further we used two independent transmitters, one at either end of the array to effectively double the number of phase centers (and halve the minimum detectable velocity). This system provides up to 32 velocity bins from 1 to 20 m/s with a resolution of 0.7 m/s.All elements are vertically po...

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Section: Exploiting Multichannel Structurementioning

confidence: 99%

Section: Exploiting Multichannel Structurementioning

confidence: 99%

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A sparsity based approach to velocity SAR imaging

Raj

Jansen

Sletten

2016

2016 IEEE Radar Conference (RadarConf)

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“…In such cases, traditional approaches to scene induced motion compensation are known to be inadequate [5]. Recently, Multi-channel SAR (MSAR) imaging has been demonstrated as a powerful approach to systematically ameliorating the aforementioned scene induced motion error problem [5][6][7][8][9][10]. In particular, the additional along-track receivers provide new, independent information about the scene that can be used to correct automatically for the underlying scene motion.…”

Section: Introductionmentioning

confidence: 99%

Multi-Channel Synthetic Aperture Radar Based Classification of Maritime Scenes

et al. 2020

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We present novel experimental evidence that demonstrates the effectiveness of exploiting scene motion information for the analysis of scene structure in maritime imaging applications. We analyze data captured by our novel airborne Multi-channel SAR (MSAR) system that is particularly suited to sampling the velocity profile of scatterers in the maritime environment. While previous works have shown the utility MSAR systems for correcting scene motion induced blurring artifacts, our work shows, for the first time, how the information furnished by an MSAR system can systematically render accurate classification of maritime scenes into different perceptual categories. We offer a methodology that is superior to traditional classification techniques that are based purely on the spatial structure of an image. Furthermore, the simplicity of the feature space involved together with the demonstrated classification performance on imagery captured by our airborne MSAR system underscore the strength of the methodology. INDEX TERMS Multi-channel synthetic aperture radar (SAR), ocean imaging, image classification

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“…In these aforementioned references, combinations of ATI and displaced phase center antenna (DPCA) are exploited to overcome their own problems (clutter, interferometric phase alteration, and noise, limited clutter suppression); in all cases, DPCA is applied after range (i.e., waveform) compression and a complete orthogonality among the transmitted waveforms is assumed (thus ambiguous energy is neglected). Recently, the use of multiple transmitters has been considered also in [29] and [30] to provide the developed multiaperture SAR system equipped with 16 receive antenna of additional phase centers.…”

Section: Introductionmentioning

confidence: 99%

Ground Moving Target detection Based on MIMO SAR systems

Lombardo

Pastina

Turin

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Multichannel space-based synthetic aperture radar (SAR) systems that exploit receiving antenna subarrays displaced in the along-track direction have been shown to provide the capability to detect ground moving targets and potentially estimate their radial motion velocity, thus making possible their correct relocation inside the SAR image of the background. However, the use of only two receiving channels to contain cost, mass, and data-rate, imposes limitations in terms of potentiality of joint clutter cancelation and target velocity estimation. To overcome this problem, we propose a full MIMO SAR scheme obtained by transmitting nearly orthogonal waveforms with the two subarrays achieved from a single antenna aperture and receiving with the same subarrays. A complete processing chain for the joint detection, imaging, and radial speed estimation is presented showing that by properly applying the clutter cancelation step before waveform compression, a good clutter removal performance is obtained despite the waveforms are not fully orthogonal. The performance of the proposed MIMO system is deeply investigated and compared to the performance of conventional multichannel systems to assess the relative merits and drawbacks.

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Velocity-ISAR: On the application of ISAR techniques to multichannel SAR imaging

Cited by 10 publications

References 20 publications

A sparsity based approach to velocity SAR imaging

A sparsity based approach to velocity SAR imaging

Multi-Channel Synthetic Aperture Radar Based Classification of Maritime Scenes

Ground Moving Target detection Based on MIMO SAR systems

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