Multi-Channel Deconvolution for Forward-Looking Phase Array Radar Imaging

Xia, Jie; Lu, Xinfei; Chen, Weidong

doi:10.3390/rs9070703

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…During the detection process, we take the echo data of the strongest scattering points at the dimension of the same distance in the neighbouring range as the reference data. In addition, we set the evaluation threshold through continuous iterative computations, and then correct and reduce the range of the neighbouring data [18,19]. Accordingly, we achieve the angle discrimination curve within the range of the strongest scattering points, where the curve is considered as the optimal angle discrimination curve at the dimension of this distance.…”

Section: Self-adaptive High-resolution Forward-looking Imaging Algorithmmentioning

confidence: 99%

High-resolution forward-looking imaging algorithm for missile-borne detectors

Cheng

Gao

Zhou

2019

JSEE

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Aiming at a novel missile-borne detector in the optional burst height proximity fuze, a self-adaptive high-resolution forward-looking imaging algorithm (SAHRFL-IA) is presented. The echo data are captured by the missile-borne detector in the target regions; thereby the azimuth angulation accuracy at the same distance dimension is improved dynamically. Thus, azimuth information of the targets in the detection area may be obtained accurately. The proposed imaging algorithm breaks through the conventional misconception of merely using azimuth discrimination curves under ideal conditions during monopulse angulation. The real-time echo data from the target region are used to perform error correction for this discrimination curve, and finally the accuracy of the azimuth angulation may reach the optimum at the same distance dimension. A series of experiments demonstrate the validity, reliability and high performance of the proposed imaging algorithm. Azimuth angulation accuracy may reach ten times that of the detection beam width. Meanwhile, the running time of this algorithm satisfies the requirements of missile-borne platforms.

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Section: Self-adaptive High-resolution Forward-looking Imaging Algorithmmentioning

confidence: 99%

High-resolution forward-looking imaging algorithm for missile-borne detectors

Cheng

Gao

Zhou

2019

JSEE

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“…Radar forward-looking imaging has extensive applications in military and civil territories, for instance, in the automatic landing of aircraft, material airdrops, topographic mapping, etc. [1,2]. However, the conventional monostatic synthetic aperture radar (SAR) and Doppler beam sharpening (DBS) techniques cannot be utilized to image the forward area due to the difficulty in acquiring an effective Doppler bandwidth [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Bayesian Super-Resolution Method for Radar Forward-Looking Imaging Based on Markov Random Field Model

Tan

Yang

et al. 2021

Remote Sensing

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Super-resolution technology is considered as an efficient approach to promote the image quality of forward-looking imaging radar. However, super-resolution technology is inherently an ill-conditioned issue, whose solution is quite susceptible to noise. Bayesian method can efficiently alleviate this issue through utilizing prior knowledge of the imaging process, in which the scene prior information plays a pretty significant role in ensuring the imaging accuracy. In this paper, we proposed a novel Bayesian super-resolution method on the basis of Markov random field (MRF) model. Compared with the traditional super-resolution method which is focused on one-dimensional (1-D) echo processing, the MRF model adopted in this study strives to exploit the two-dimensional (2-D) prior information of the scene. By using the MRF model, the 2-D spatial structural characteristics of the imaging scene can be well described and utilized by the nth-order neighborhood system. Then, the imaging objective function can be constructed through the maximum a posterior (MAP) framework. Finally, an accelerated iterative threshold/shrinkage method is utilized to cope with the objective function. Validation experiments using both synthetic echo and measured data are designed, and results demonstrate that the new MAP-MRF method exceeds other benchmarking approaches in terms of artifacts suppression and contour recovery.

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“…However, for bistatic SAR, it is difficult to implement in practice due to synchronization problem, large range cell migration (RCM), and complicated structure [16][17][18]. For deconvolution, it is essentially a highly ill-posed problem which is sensitive to noise and hardly ensures a robust solution [19,20].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Forward-Looking Sar Imaging Algorithm Based on Compressive Sensing

Pang¹,

Wu²,

Xing³

et al. 2019

PIER M

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Having the imaging ability of the area in front of flight direction, forward-looking synthetic aperture radar (SAR) has become a hot topic in areas of SAR research. Nevertheless, constrained by limited azimuth aperture length, the imaging of forward-looking SAR suffers from poor azimuth resolution. Aiming at this problem, an enhanced forward-looking SAR imaging algorithm is proposed in this paper. This algorithm takes both super-resolving ability and computational burden into account. Firstly, an imaging framework is proposed to decrease the computational burden. Secondly, an iterative regularization implementation of compressive sensing (CS) is proposed to improve azimuth resolution. Finally, imaging experiments based on simulated data and Ku-band complex valued image data from the MiniSAR system demonstrate the effectiveness of the proposed algorithm.

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Multi-Channel Deconvolution for Forward-Looking Phase Array Radar Imaging

Cited by 8 publications

References 36 publications

High-resolution forward-looking imaging algorithm for missile-borne detectors

High-resolution forward-looking imaging algorithm for missile-borne detectors

A Novel Bayesian Super-Resolution Method for Radar Forward-Looking Imaging Based on Markov Random Field Model

An Enhanced Forward-Looking Sar Imaging Algorithm Based on Compressive Sensing

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