Orthogonal Radiation Field Construction for Microwave Staring Correlated Imaging

Liu, Bo; Wang, Dongjin

doi:10.2528/pierm17042003

Cited by 14 publications

(12 citation statements)

References 14 publications

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“…The radiation field at the location rfalse→ can be expressed as [18,20]E(rfalse→,t)=Erad1false(truer→,tfalse)+Erad2false(truer→,tfalse)=∑i=1I1Aifalse(trueR→^i,truer→false)4πfalse|rfalse→−truer→ifalse|∑l=1L∑q=1QSifalse(t−qΔt−lT−τi(rfalse→)false)+∑i=I1+1I1+I2Aifalse(…”

Section: The Proposed Bi-static Msci Methodsmentioning

confidence: 99%

A Novel Microwave Staring Correlated Radar Imaging Method Based on Bi-Static Radar System

Yuan

Guo

Chen

et al. 2019

Sensors

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The stochastic characteristic of the radiation field of a mono-static microwave staring correlated imaging (MSCI) radar degenerates with the increase of the imaging distance, which results in degradation of the image quality. To address this issue, a novel MSCI method based on bi-static radar is proposed from two perspectives: site-deploying and waveform design. On the one hand, a new bi-static MSCI site-deploying scheme is proposed which adopts two transmitting stations with their azimuth angles relative to the center of the imaging region differing by 90 degrees. On the other hand, by using two transmitting arrays synchronously transmitting inner-and-inter pulse frequency hopping (IAIP-FH) signals, the radiation field of each station includes a few “frequency stripes” perpendicular to the radiation direction, and as a consequence, the “frequency stripes” of each radiation field are perpendicular to each other. As a result, the radiation field of the bi-static MSCI is the superposition of the two striped radiation fields, thus a latticed radiation field is constructed. Therefore, the targets in different latticed grids scatter independent fields, then, the images can be reconstructed using correlation process (CP) algorithms. The grid size of the latticed radiation field is determined by the inner-pulse frequency hopping (FH) interval of the IAIP-FH signals and the imaging geometry. Moreover, it is shown that the 3 dB beam width of the space correlation function of the radiation field does not change with the imaging distance, thus the stochastic characteristic of the radiation field is partly preserved when the imaging distance increases. Simulation results validate the analysis and show that the proposed method can obtain higher resolution images than the common mono-static MSCI method.

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Section: The Proposed Bi-static Msci Methodsmentioning

confidence: 99%

A Novel Microwave Staring Correlated Radar Imaging Method Based on Bi-Static Radar System

Yuan

Guo

Chen

et al. 2019

Sensors

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“…Waveform design is one of the main research areas in MSCI [7,[9][10][11][12][13][14]. The frequency-hopping (FH) waveforms are mostly studied given that FH waveforms are easy to generate and control by traditional radar hardwares [7,9,11,12].…”

Section: Introductionmentioning

confidence: 99%

Sparse Self-Calibration for Microwave Staring Correlated Imaging With Random Phase Errors

Yuan¹,

Jiang²,

Zhang³

et al. 2020

PIER C

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Microwave Staring Correlated Imaging (MSCI) technology can obtain high-resolution images in staring imaging geometry by utilizing the temporal-spatial stochastic radiation field. In MSCI, sparse-driven approaches are commonly used to reconstruct the target images when the radiation fields are accurately calculated. However, it is challenging to compute radiation filed with high precision due to the existence of random phase errors in MSCI systems. Therefore, in this paper, a self-calibration method is proposed to handle the problem. Specifically, a two-step self-calibration framework is applied which alternately reconstructs the target image and estimates the random phase errors. In the target image reconstruction step, sparse-driven approaches are utilized, while in the random phase errors calibration step, an adaptive learning rate method is adopted. Moreover, the batch-learning strategy is utilized to reduce computation burden and obtain effective convergence performance. Numerical simulations verify the advantage of the proposed method to obtain good imaging results and improve random phase errors correction performance.

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“…Because there is generally a gain-phase error, which is difficult to have accurate knowledge of, a sparse auto-calibration method was proposed in [ 8 ] to perform gain-phase error calibration in the sparsity-driven RCI. To improve the independence degree of the radiation field, Bo Liu et al presented a new type of ideal independent radiation field: the orthogonal radiation field for MSCI [ 9 ]. In [ 10 ], a novel adaptive clustered sparse Bayesian learning algorithm was proposed for extended target imaging.…”

Section: Introductionmentioning

confidence: 99%

“…Before the CIP, the MSCI must discretize the continuous target region to a fine grid and accurately compute the measurement matrix [ 1 , 2 , 3 , 4 , 7 , 8 , 9 , 10 ]. The MSCI system typically observes a wide area [ 1 , 2 , 11 ]; thus, using a uniform fine grid in the entire imaging region will generate an excessive number of grids, which makes the imaging process highly complex and in turn limits the real-time imaging performance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Microwave Staring Correlated Imaging for Targets Appearing in Discrete Clusters

Tian

Jiang

Chen

et al. 2017

Sensors

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Microwave staring correlated imaging (MSCI) can achieve ultra-high resolution in real aperture staring radar imaging using the correlated imaging process (CIP) under all-weather and all-day circumstances. The CIP must combine the received echo signal with the temporal-spatial stochastic radiation field. However, a precondition of the CIP is that the continuous imaging region must be discretized to a fine grid, and the measurement matrix should be accurately computed, which makes the imaging process highly complex when the MSCI system observes a wide area. This paper proposes an adaptive imaging approach for the targets in discrete clusters to reduce the complexity of the CIP. The approach is divided into two main stages. First, as discrete clustered targets are distributed in different range strips in the imaging region, the transmitters of the MSCI emit narrow-pulse waveforms to separate the echoes of the targets in different strips in the time domain; using spectral entropy, a modified method robust against noise is put forward to detect the echoes of the discrete clustered targets, based on which the strips with targets can be adaptively located. Second, in a strip with targets, the matched filter reconstruction algorithm is used to locate the regions with targets, and only the regions of interest are discretized to a fine grid; sparse recovery is used, and the band exclusion is used to maintain the non-correlation of the dictionary. Simulation results are presented to demonstrate that the proposed approach can accurately and adaptively locate the regions with targets and obtain high-quality reconstructed images.

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Orthogonal Radiation Field Construction for Microwave Staring Correlated Imaging

Cited by 14 publications

References 14 publications

A Novel Microwave Staring Correlated Radar Imaging Method Based on Bi-Static Radar System

A Novel Microwave Staring Correlated Radar Imaging Method Based on Bi-Static Radar System

Sparse Self-Calibration for Microwave Staring Correlated Imaging With Random Phase Errors

Adaptive Microwave Staring Correlated Imaging for Targets Appearing in Discrete Clusters

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