Bistatic ISAR Distortion Mitigation via Superresolution

Cataldo, Davide

doi:10.1109/taes.2018.2808079

Cited by 10 publications

(9 citation statements)

References 29 publications

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“…Then, (19) can be approximated as

\begin{matrix} right leftthickmathspace.5em \\ Δ R_{Pm} & = 2 y_{P} K_{0} + 2 y_{P} K_{1} t_{m} - y_{P} K_{0} ω_{0}^{2} t_{m}^{2} \\ + 2 ω_{0} x_{P} (K_{0} t_{m} + K_{1} t_{m}^{2}) \end{matrix}

From (13), we note that

normalΔ R_{Pm}

also induces the range cell migration (RCM), resulting in a shift of the sinc function in (13). However, the effect of RCM can be neglected, if the constraint

normalΔ R_{Pm} < c / 2 B

is satisfied [19]. Neglecting the constant phase term

2 y_{i} K_{0}

, the polynomial phase signal (PPS) of the n th range bin can be written as follows:

s_{n} false(t_{m} false) = \sum_{i = 1}^{L_{n}} A_{i} \exp (\frac{- j 2 π f_{c}}{c} ()(, \begin{matrix} 2 y_{i} K_{1} t_{m} - y_{i} K_{0} ω_{0}^{2} t_{m}^{2} \\ + 2 ω_{0} x_{i} ()(, K_{0} t_{m} + K_{1} t_{m}^{2}) \end{matrix}))

where

i = 1, 2, . . .,

are the indexes of the scatterers in the n th range bin,

L_{n}

is the number of scatterers.…”

Section: Bi‐isar Signal Modelling With Distortionsmentioning

confidence: 99%

“…Hence, Bi‐ISAR becomes an effective method of the surveillance of space targets [10–14]. The research of Bi‐ISAR including the imaging principle, algorithm, and application has attracted much attention recently [9, 15–22].…”

Section: Introductionmentioning

confidence: 99%

“…The bistatic equivalent monostatic (BEM) approximation of the B‐ISAR signal is established under certain constraints in [15, 16], which greatly simplifies the Bi‐ISAR signal processing. However, when the time‐varying effect of the bistatic angle cannot be neglected, the Bi‐ISAR image obtained by the RD algorithm is distorted and defocused [15, 19]. It degrades the image quality and affects the subsequent target recognition.…”

Section: Introductionmentioning

confidence: 99%

“…For the linear‐geometry distortion, in [19], Cataldo and Martorella propose a distortion reduction algorithm by shortening CPI and combining with a super resolution algorithm to retain the cross‐range resolution. However, the CPI reduction factor is required to be calculated deliberately to guarantee that the linear‐geometry distortion induced by the variation of bistatic angle is less than the resolution.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Bistatic ISAR distortion mitigation of a space target via exploiting the orbital prior information

Shi

Guo

Han³

et al. 2019

IET Radar, Sonar & Navigation

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“…Then, (19) can be approximated as

\begin{matrix} right leftthickmathspace.5em \\ Δ R_{Pm} & = 2 y_{P} K_{0} + 2 y_{P} K_{1} t_{m} - y_{P} K_{0} ω_{0}^{2} t_{m}^{2} \\ + 2 ω_{0} x_{P} (K_{0} t_{m} + K_{1} t_{m}^{2}) \end{matrix}

From (13), we note that

normalΔ R_{Pm}

also induces the range cell migration (RCM), resulting in a shift of the sinc function in (13). However, the effect of RCM can be neglected, if the constraint

normalΔ R_{Pm} < c / 2 B

is satisfied [19]. Neglecting the constant phase term

2 y_{i} K_{0}

, the polynomial phase signal (PPS) of the n th range bin can be written as follows:

s_{n} false(t_{m} false) = \sum_{i = 1}^{L_{n}} A_{i} \exp (\frac{- j 2 π f_{c}}{c} ()(, \begin{matrix} 2 y_{i} K_{1} t_{m} - y_{i} K_{0} ω_{0}^{2} t_{m}^{2} \\ + 2 ω_{0} x_{i} ()(, K_{0} t_{m} + K_{1} t_{m}^{2}) \end{matrix}))

where

i = 1, 2, . . .,

are the indexes of the scatterers in the n th range bin,

L_{n}

is the number of scatterers.…”

Section: Bi‐isar Signal Modelling With Distortionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bistatic ISAR distortion mitigation of a space target via exploiting the orbital prior information

Shi

Guo

Han³

et al. 2019

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

“…Meanwhile, Bi‐ISAR has other advantages over the monostatic ISAR, e.g. better detection performance of stealthy targets, strong anti‐interference and information acquisition ability [7–11]. The research of Bi‐ISAR with respect to the imaging principle, algorithms and applications has attracted much attention recently [12–18].…”

Section: Introductionmentioning

confidence: 99%

Bi‐ISAR sparse imaging algorithm with complex Gaussian scale mixture prior

Zhu

Shi

Guo

et al. 2019

IET Radar, Sonar & Navigation

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Polarisation variation of linear polarised Bi‐ISAR system in space targets imaging

Wang

2021

IET Radar Sonar & Navi

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A bistatic inverse synthetic aperture radar (Bi-ISAR) system is used to observe space targets through different viewing angles via a bistatic radar configuration. It is seen that the receiver of a linear polarised Bi-ISAR system with a long baseline will encounter energy variation and high-order phase deviation in ISAR imaging due to the polarisation variation of this system. Here, the authors quantitatively analyse the energy variation caused by polarisation mismatch in a linearly polarised Bi-ISAR system by investigating the time-varying bistatic angle and intrinsic variable polarisation effect of targets via the combination of observation geometry and computational electromagnetic. The highorder phase deviation in long-term coherent processing is also presented through the same strategy. The authors conducted simulations for a bistatic system, which is configured with different baseline vectors, to analyse the impact of polarisation variation of the linear polarised Bi-ISAR system on the energy loss and high-order phase terms.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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Bistatic ISAR Distortion Mitigation via Superresolution

Cited by 10 publications

References 29 publications

Bistatic ISAR distortion mitigation of a space target via exploiting the orbital prior information

Bistatic ISAR distortion mitigation of a space target via exploiting the orbital prior information

Bi‐ISAR sparse imaging algorithm with complex Gaussian scale mixture prior

Polarisation variation of linear polarised Bi‐ISAR system in space targets imaging

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