Estimation of the total rotational velocity of a non-cooperative target using a 3D InISAR system

Tran, Hai-Tan; Giusti, Elisa; Salvetti, Federica; Ng, Brian W.-H.; Phan, An

doi:10.1109/radar.2015.7131129

Cited by 10 publications

(8 citation statements)

References 7 publications

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“…is the range frequency with B denoting the bandwidth of the transmitted waveform, f s = 2 f 0 c Ωx and τ = 2y c , Ω denotes the effective rotational vector of the target [24], x O y denotes the local coordinate system centered at O on the target, the A( f s ) is the spectrum of the amplitude modulation due to the azimuth antenna beam pattern, and T( f s , τ) is the reflectivity distribution of the ISAR target scene to be reconstructed. The r mc (s, f t ) is the ISAR measurement after motion compensation and range compression.…”

Section: Model Of Isar Measurementsmentioning

confidence: 99%

Inverse Synthetic Aperture Radar Sparse Imaging Exploiting the Group Dictionary Learning

Wang

Zhu

et al. 2021

Remote Sensing

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Sparse imaging relies on sparse representations of the target scenes to be imaged. Predefined dictionaries have long been used to transform radar target scenes into sparse domains, but the performance is limited by the artificially designed or existing transforms, e.g., Fourier transform and wavelet transform, which are not optimal for the target scenes to be sparsified. The dictionary learning (DL) technique has been exploited to obtain sparse transforms optimized jointly with the radar imaging problem. Nevertheless, the DL technique is usually implemented in a manner of patch processing, which ignores the relationship between patches, leading to the omission of some feature information during the learning of the sparse transforms. To capture the feature information of the target scenes more accurately, we adopt image patch group (IPG) instead of patch in DL. The IPG is constructed by the patches with similar structures. DL is performed with respect to each IPG, which is termed as group dictionary learning (GDL). The group oriented sparse representation (GOSR) and target image reconstruction are then jointly optimized by solving a l1 norm minimization problem exploiting GOSR, during which a generalized Gaussian distribution hypothesis of radar image reconstruction error is introduced to make the imaging problem tractable. The imaging results using the real ISAR data show that the GDL-based imaging method outperforms the original DL-based imaging method in both imaging quality and computational speed.

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Section: Model Of Isar Measurementsmentioning

confidence: 99%

Inverse Synthetic Aperture Radar Sparse Imaging Exploiting the Group Dictionary Learning

Wang

Zhu

et al. 2021

Remote Sensing

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“…and then the performance of Doppler focussing and interferometric phase estimation will be affected. In [20], ML-FRFT algorithm was proposed to achieve the Doppler focussing, where the CR ki are roughly estimated by searching the peaks of (18), and the fine estimation of the CR ki is achieved by performing the FRFT on Equation ( 15), then all the ISAR images are focussed by using the unified CR ki , that is, CR ki = CR Ai . On one hand, bigger estimation error of CR ki may be caused due to the instability of the rough estimation of CR ki and the cross-term introduced by FRFT.…”

Section: D Target Reconstruction Based On Rlvdt-idfmentioning

confidence: 99%

Three‐dimensional InISAR imaging method for noncooperative targets based on respective LvD transformation and iterative Doppler focussing

Zhang

Dong

2021

IET Radar Sonar & Navi

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Interferometric inverse synthetic aperture radar (InISAR) is an usually adopted approach for three-dimensional (3D) imaging of noncooperative target with a real or equivalent turntable motion. In practice, the Doppler frequency of non-cooperative target usually vary nonlinearly, especially when the rotational velocity of the target is fast or the imaging time is relatively long, the azimuth echoes of the target often contain quadratic phase terms. In this case, if we do not compensate the nonlinear phase terms contained in the echoes, the ISAR image will be defocused and then the 3D imaging quality will be degraded. Aiming at solving this problem, a new 3D InISAR imaging method based on respective Lv's distribution transformation and iterative Doppler focussing (RLvDT-IDF) is proposed in this paper. The major contributions of the proposed approach are as follows: (1) the echo of a range cell which can be treated as the superposition of a series of linear frequency modulation signals (LFMs) is processed by LvDT to specify the chirp rates (CRs) of these LFMs. The CRs are used to construct phase functions so as to remove the quadratic phase term contained in the echo by the IDF method;(2) Considering that the CRs within a range cell received by different channels are relatively different, so different ISAR images are focussed by respective LvDT-IDF (RLvDT-IDF), so all the well-focussed ISAR images can be obtained, which help to improve the accuracy of the image registration and scatterer extraction; (3) Considering that the phase information from the well-focussed ISAR images may have different errors by the RLvDT-IDF processing and the unfocused ISAR images contain the complete phase information, so they are used instead of the well-focussed ISAR images to estimate the interferometric phases of the extracted scatterers; (4) Based on the estimated CRs and 3D coordinates, the target's 3D rotation vector is retrieved by minimising the sum of squared residuals. Both simulations and practical experiment are conducted to validate the proposed method.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…In practice, a useful technique for estimating the chirp rate for each scatterer (indexed by m ) is to perform an optimisation with respect to an objective

normalΛ

that measures the degree to which the scatterer is in focus

{\hat{μ}}_{m}^{false(n false)} = \underset{μ}{\arg \max} normalΛ ()(, I^{false(AC false)} false(τ_{m}, ν_{m}, μ false)) .

A commonly used measure for the objective is image contrast , defined as [1, 19]

normalΛ false(u_{n, m} false) = \frac{\sqrt{A \{{(| u_{n, m} | - A {| u_{n, m} |})}^{2}\}}}{A falsefalse{falsefalse| u_{n, m} falsefalse| falsefalse}},

where for the sake of notational brevity,

u_{n, m} = I^{false(AC false)} false(τ_{m}, ν_{m}, μ false)

, and the mean operation (denoted by

A falsefalse{\cdot falsefalse}

) is computed over a local region in the vicinity of the given scatterer. Note that the optimisation region is restricted to the Doppler dimension only, since the scatterer's range bin is already known from peak finding.…”

Section: Algorithm For Total Rotation Vector Estimationmentioning

confidence: 99%

“…In earlier work [19], it is reported that total velocity estimation technique is effective for a situation where the scatterers’ rotation in the IPP extends over several degrees. This scenario arises when the angular rotation speed is low, or relatively few pulses are used in forming the ISAR image ( T small).…”

Section: Performance Analysismentioning

confidence: 99%

Estimation of the total rotational velocity of a non‐cooperative target with a high cross‐range resolution three‐dimensional interferometric inverse synthetic aperture radar system

Tran

Giusti

et al. 2017

IET Radar, Sonar & Navigation

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Estimation of the total rotational velocity of a non-cooperative target using a 3D InISAR system

Cited by 10 publications

References 7 publications

Inverse Synthetic Aperture Radar Sparse Imaging Exploiting the Group Dictionary Learning

Inverse Synthetic Aperture Radar Sparse Imaging Exploiting the Group Dictionary Learning

Three‐dimensional InISAR imaging method for noncooperative targets based on respective LvD transformation and iterative Doppler focussing

Estimation of the total rotational velocity of a non‐cooperative target with a high cross‐range resolution three‐dimensional interferometric inverse synthetic aperture radar system

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