Automatic target classification in passive ISAR range-crossrange images

Manno-Kovacs, Andrea; Giusti, Elisa; Berizzi, F.; Kovács, Levente

doi:10.1109/radar.2018.8378558

Cited by 13 publications

(16 citation statements)

References 28 publications

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“…Entropy and contrast were chosen to evaluate the quality of range profiles and ISAR images. The methods of ACM (5) and MEARP (6) were also used for comparison. The computation platform was based on Windows 10 64-bit operating system, Intel Core i5-9300H@2.40 GHz CPU, 8 GB memory, and MATLAB version 2017b.…”

Section: Resultsmentioning

confidence: 99%

“…Inverse synthetic aperture radar (ISAR), which is used to obtain images of non-cooperative and moving targets, has been widely applied in many civil and military domains in the last few decades [1], [2], [3], [4], [5]. ISAR achieves high resolutions both in range and azimuth directions by exploiting the wideband characteristics and angular diversity during the coherent processing interval.…”

Section: Introductionmentioning

confidence: 99%

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Noise-Robust Range Alignment Method for Inverse Synthetic Aperture Radar Based on Aperture Segmentation and Average Range Profiles Correlation

Yang

Zhang

2020

Preprint

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Abstract Range alignment is an essential procedure in translation motion compensation of inverse synthetic aperture radar imaging. Global optimization or maximum correlation-based algorithms have been used to realize range alignment. However, it is still challenging to achieve range alignment in low signal-to-noise ratio scenarios, which are common in inverse synthetic aperture radar imaging. In this paper, a novel anti-noise range alignment approach is proposed. In this new method, the target's motion is modelled as a uniformly accelerated motion during a short sub-aperture time. Minimum entropy optimization is implemented to estimate the motion parameters in each sub-aperture. These estimated parameters can be used to align the profiles of the current sub-aperture. Once each sub-aperture's range profiles are aligned, the noncoherent accumulation gain is obtained by averaging all profiles in each sub-aperture, which can be used as valuable information. The accumulation and correlation method is applied to align the average range profiles of each sub-aperture for the reason that the former step mainly focuses on alignment within the sub-apertures. Experimental results based on simulated and real measured data demonstrate the effectiveness of the proposed algorithm in low signal-to-noise ratio scenarios.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Noise-Robust Range Alignment Method for Inverse Synthetic Aperture Radar Based on Aperture Segmentation and Average Range Profiles Correlation

Yang

Zhang

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Entropy and contrast were chosen to evaluate the quality of the range profiles and ISAR images. The ACM (5) and MEARP (6) were also used for comparison. The computation platform was based on the Windows 10 64-bit operating system, an Intel Core i5-9300H@2.40 GHz CPU, 8 GB of memory, and MATLAB version 2017b.…”

Section: Resultsmentioning

confidence: 99%

“…k denotes the iteration number, and p(r, m) is the envelope of the range-compressed ISAR signal. The other mathematical symbols are the same as those in (5). The two algorithms above are representative of the maximum-correlation-based and global-optimizationcriterion-based methods, respectively.…”

Section: Related Workmentioning

confidence: 99%

Noise-Robust Range Alignment Method for Inverse Synthetic Aperture Radar Based on Aperture Segmentation and Average Range Profile Correlation

Yang²,

Zhang

2020

Preprint

View full text Add to dashboard Cite

Range alignment is an essential procedure in the translation motion compensation of inverse synthetic aperture radar imaging. Global optimization or maximum-correlation-based algorithms have been used to realize range alignment. However, it is still challenging to achieve range alignment in low signal-to-noise ratio scenarios, which are common in inverse synthetic aperture radar imaging. In this paper, a novelanti-noise range alignment approach is proposed. In this new method, the target motion is modelled as a uniformly accelerated motion during a short sub-aperture time. Minimum entropy optimization is implemented to estimate the motion parameters in each sub-aperture. These estimated parameters can be used to align the profiles of the current sub-aperture. Once the range profiles of eachsub-aperture are aligned, the non-coherent accumulation gain is obtained by averaging all profiles in each sub-aperture, which can be used as valuable information. The accumulation and correlation method is applied to align the average range profiles of each sub-aperture because the former step focuses mainly on alignment within the sub-apertures. Experimental results based on simulated and real measured data demonstrate the effectiveness of the proposed algorithm in low signal-to-noise ratio scenarios.

show abstract

“…Another target classification approach requires coordinated flight models, on-board recorded flight paths and profiles, altitude and velocity information, producing 21-100% rates. In [15] we presented a proof-of-concept generic passive ISAR image based classification with 61% average recognition 1558-1748 © 2018 IEEE. Translations and content mining are permitted for academic research only.…”

Section: Introductionmentioning

confidence: 99%