Wide-angle radar imaging using time–frequency distributions

Lanterman, Aaron D.; Munson, D.C.; Wu, Thomas Y.

doi:10.1049/ip-rsn:20030712

Cited by 33 publications

(22 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The name phase center was given due to the fact that it was defined to determine the phase of the returned signal, i.e., the location of the equivalent point-scatterer, so that the returned signal from a surface is the same no matter what division we apply to the area. Placing the equivalent point-scatterer in the phase center, (12) holds and R i is calculated. Thus, the range R i of the facet i is calculated as the distance between its phase center and the radar.…”

Section: B Phase Center Of a Facetmentioning

confidence: 99%

See 1 more Smart Citation

Facet Model of Moving Targets for ISAR Imaging and Radar Back-Scattering Simulation

García-Fernández

Yeste-Ojeda

Grajal

2010

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

A facet model of targets is proposed to simulate inverse synthetic aperture radar (ISAR) images and radar back-scattering of moving targets with rigid and nonrigid motions. Targets are composed of solid objects which are modeled by triangular facets. It is shown that a facet can be treated as an equivalent point-scatterer whose radar cross section (RCS) and position depend on the shape of the triangle, the frequency, and the angle of incidence. A shadowing algorithm is also developed to detect the facets which actually have influence on the signal received by the radar. Besides, a facet division algorithm is implemented to improve the result of the shadowing algorithm and to have at least one facet per resolution cell. Finally, we apply our simulator to obtain the ISAR images of a ship and a helicopter and to calculate the micro-Doppler signature of a human.

show abstract

Section: B Phase Center Of a Facetmentioning

confidence: 99%

“…Nevertheless, this information can be useless if the target motion produces blurred images. Motion compensation algorithms [4][5][6][7][8] or time-frequency techniques [9][10][11][12][13][14][15] are useful techniques to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Facet Model of Moving Targets for ISAR Imaging and Radar Back-Scattering Simulation

García-Fernández

Yeste-Ojeda

Grajal

2010

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

show abstract

“…[9,10,11]. In the past three decades, many useful results have been obtained by scholars all around the world [12,13,14,15,16,17]. The high resolution in the range coordinate is achieved by transmitting a large bandwidth coded signal, and the high cross range resolution is achieved by the coherent integration of echoes collected at different viewing angles between the radar and target [18].…”

Section: Introductionmentioning

confidence: 99%

ISAR Imaging of Maneuvering Targets Based on the Modified Discrete Polynomial-Phase Transform

Wang

Abdelkader

Zhao

et al. 2015

Sensors

View full text Add to dashboard Cite

Inverse synthetic aperture radar (ISAR) imaging of a maneuvering target is a challenging task in the field of radar signal processing. The azimuth echo can be characterized as a multi-component polynomial phase signal (PPS) after the translational compensation, and the high quality ISAR images can be obtained by the parameters estimation of it combined with the Range-Instantaneous-Doppler (RID) technique. In this paper, a novel parameters estimation algorithm of the multi-component PPS with order three (cubic phase signal-CPS) based on the modified discrete polynomial-phase transform (MDPT) is proposed, and the corresponding new ISAR imaging algorithm is presented consequently. This algorithm is efficient and accurate to generate a focused ISAR image, and the results of real data demonstrate the effectiveness of it.

show abstract

“…Passive radar imaging involves image rebuilding from sparse and irregular samples of its Fourier transform [1] [2]. Many reconstruction methods have been researched [2][3][4][5]. [2] introduces deconvolution techniques to passive radar data.…”

Section: Introductionmentioning

confidence: 99%

A new approach to distributed passive radar imaging by 2-D NUFFT

Liu

et al. 2010

IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS

View full text Add to dashboard Cite

This paper studies distributed passive radar imaging of two-dimensional (2-D) scene. The echoes and the reflectivity of target are confirmed to be a Fourier transform pair. However, due to the space configuration based on the illuminators of opportunity, the echoes are sampled nonuniformly, which lead to the non-equispaced property of spacespectrum distribution. In this case, direct 2-D FFT reconstruction doesn't apply now. To retain the fast computing of image reconstruction like FFT, non-uniform fast Fourier transform (NUFFT) is introduced to avoid complex interpolation. We generalize NUFFT to 2-D NUFFT to tackle the problem that the space-spectrum is unequally-spaced in both directions of ȝ and Ȟ.This new 2-D NUFFT method is compared with polar-coordinate method in efficiency and accuracy using both the analytical and numerical approaches. Simulations demonstrate that 2-D NUFFT can reduce the computational complexity while ensuring image reconstruction accuracy. So it's a promising strategy for realtime imaging of distributed passive radar.

show abstract

Wide-angle radar imaging using time–frequency distributions

Cited by 33 publications

References 44 publications

Facet Model of Moving Targets for ISAR Imaging and Radar Back-Scattering Simulation

Facet Model of Moving Targets for ISAR Imaging and Radar Back-Scattering Simulation

ISAR Imaging of Maneuvering Targets Based on the Modified Discrete Polynomial-Phase Transform

A new approach to distributed passive radar imaging by 2-D NUFFT

Contact Info

Product

Resources

About