Robust range alignment algorithm via Hough transform in an ISAR imaging system

Sauer, Thomas; Schroth, A.

doi:10.1109/7.395222

Cited by 60 publications

(21 citation statements)

References 11 publications

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“…And, a novel coarse estimation method is also developed, where the radial velocity and acceleration are estimated by Radon Transform (RT) and IC, respectively. Utilizing RT for velocity estimation is very similar to the Hough-based range alignment approach [5]. Assuming the linear migration of the range profiles is mainly induced by the radial velocity, the velocity can be determined by the slope of range profiles estimated by RT [14].…”

Section: Coarse Motion Estimation For Initializationmentioning

confidence: 99%

“…Herein, we use the Yak-42 data to generate synthetic data with different SNRs. The high-order motion error with α = [1][2][3]5,10,30] is added into the corrected data causing misalignment and phase error. The misaligned profiles are shown in Figure 6c.…”

Section: Performance Analysismentioning

confidence: 99%

“…And, it is likely to fail when strong noise presents, as the coherence of HRRPs is contaminated seriously. The other group is optimization-based methods [5][6][7][8][9]. It is widely accepted that global optimization methods are more robust to reflectivity scintillation and additive noise than maximum correlation method, in which the problem of ISAR range alignment is formulated by using some global metrics.…”

Section: Introductionmentioning

confidence: 99%

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Translational motion compensation for ISAR imaging under low SNR by minimum entropy

Zhang

Sheng

Duan

et al. 2013

EURASIP J. Adv. Signal Process.

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In general, conventional error correction for inverse synthetic aperture radarimaging consists of range alignment and phase adjustment, which compensate range shift and phase error, respectively. Minimum entropy-based methods have been proposed to realize range alignment and phase adjustment. However, it becomes challenging to align high-resolution profiles when strong noise presents, even using entropy minimization. Consequently, the subsequent phase adjustment fails to correct phase errors. In this article, we propose a novel method for translational motion correction, where entropy minimization is utilized to achieve range alignment and phase adjustment jointly. And, a coordinate descent algorithm is proposed to solve the optimization implemented by quasi-Newton algorithm. Moreover, a method for coarse motion estimation is also proposed for initialization in solving the optimization. Both simulated and real-measured datasets are used to confirm the effectiveness of the joint motion correction in low signal-to-noise ratio situations.

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Section: Coarse Motion Estimation For Initializationmentioning

confidence: 99%

Section: Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Translational motion compensation for ISAR imaging under low SNR by minimum entropy

Zhang

Sheng

Duan

et al. 2013

EURASIP J. Adv. Signal Process.

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“…Thus, range alignment is implemented to the range-compressed SA data firstly by some novel approaches [9][10][11], which are proven to be effective even for SA data.…”

Section: Weighted Eigenvector Methods For Phase Error Correctionmentioning

confidence: 99%

“…Range alignment is used to remove the range migration of different pulses, while phase adjustment is used to correct their phase errors. It should be emphasized that several existing range alignment methods are already capable to correct range migrations for SA-ISAR data [9][10][11]. With respect to phase adjustment, there are lots of phase compensation methods available for conventional ISAR.…”

Section: Introductionmentioning

confidence: 99%

A weighted eigenvector autofocus method for sparse-aperture ISAR imaging

Duan

Zhang

Xing

2013

EURASIP J. Adv. Signal Process.

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With the development of multi-functional radar systems, inverse synthetic aperture radar (ISAR) imaging with sparse-aperture (SA) data has drawn considerable attention in the recent years. Motion compensation and imaging are among the most significant challenges that SA-ISAR imaging frequently faces. In this paper, we focus on the autofocus scheme, in which a modified eigenvector-based autofocus method is proposed. In the method, different weights are endued to different range cells according to their signal-to-noise ratios (SNRs). Using the weights, the contribution from the range cells with high SNR is enhanced, yielding accuracy improvement in phase error estimation. What is more is that to improve the estimation precision, an iterative scheme is introduced. Experimental results show that the proposal is not only robust to severe noise but also applicable to ISAR imaging with different SA patterns. Detailed comparisons are given in order to show the superiorities of the proposal in phase adjustment for ISAR data.

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New range alignment algorithm for ISAR based on high order moment

Zhu

2007

J. of Electron.(China)

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A new technique is proposed for range alignment in Inverse Synthetic Aperture Radar (ISAR). The basic idea is to perform range alignment using a maximum kurtosis (fourth-order central moment) criterion. After maximizing the kurtosis of the combined range profile of two adjacent echoes, the amount of range shift between them can be automatically tracked out. The combined range profile is constructed by a max operation, which only reserves the larger elements of the two echoes, and the echoes' amplitudes are limited before they are combined. This algorithm has been used to process real ISAR data and the results demonstrate the effectiveness of the method. Compared with the correlation method and the minimum entropy method, the proposed algorithm obtains much better results in both examples in this paper. Its computation complexity has the same order of magnitude as the minimum entropy method.

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Robust range alignment algorithm via Hough transform in an ISAR imaging system

Cited by 60 publications

References 11 publications

Translational motion compensation for ISAR imaging under low SNR by minimum entropy

Translational motion compensation for ISAR imaging under low SNR by minimum entropy

A weighted eigenvector autofocus method for sparse-aperture ISAR imaging

New range alignment algorithm for ISAR based on high order moment

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