Estimation of Pose Angle for Trihedral in Ultrawideband Virtual Aperture Radar

Chen, Bo; Jin, Tian; Zhang, Zhimin; Lu, Bi Ying

doi:10.2528/pier12120504

Cited by 3 publications

(4 citation statements)

References 30 publications

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“…Consequently, the fullparabola atom is not applied; it is assumed that a T-shaped corner is present when the representation yields a certain minimum value for both the "forward-looking" and "backwardlooking" atoms at a single point in the representation grid. The ratio between the two values is an indication for the angle of the T-shaped corner with respect to the radar track [37], [38].…”

Section: Phase Modelsmentioning

confidence: 99%

Extraction of Building Features from Stand-Off Measured Through-Wall Radar Data

Wit¹,

Rossum²

2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Automated extraction of building features is a great aid in synthesizing building maps from radar data. In this paper, a model-based method is described to detect and classify canonical scatters, such as corners and planar walls, inside a building. Once corners and walls have been located, a building map can be synthesized. To detect and classify the canonical scatterers, sparse reconstruction with an overcomplete dictionary is used. The dictionary is tuned to phase models that are specific to the different canonical scatterers, i.e., a linear phase change for walls and a quadratic phase change for corners. This is a potentially robust method for detecting canonical scatterers because the polynomial degree of the measured phase is preserved, even after propagation through a wall. Building-feature extraction results obtained with actually stand-off measured through-wall radar data are discussed.Index Terms-Overcomplete dictionary (OCD), radar imaging, sparse reconstruction, through-wall radar.

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Section: Phase Modelsmentioning

confidence: 99%

Extraction of Building Features from Stand-Off Measured Through-Wall Radar Data

Wit¹,

Rossum²

2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…However, it should be noted that spatial filtering applies to TWR with synthetic aperture array only. Although synthetic aperture radar (SAR) techniques have been making steady progress in many fields, the long scanning time to achieve a high azimuth resolution as well as limitations of time and space make multi-input and multi-output (MIMO) radars more suitable [18][19][20][21]. Unfortunately, in bistatic mode, the wall returns received by different receivers are no longer identical.…”

Section: Introductionmentioning

confidence: 99%

A Wall-Clutter Suppression Method Based on Spatial Signature in Mimo Through-the-Wall Radar Imaging

Zhang¹,

Lu²,

Zhou³

et al. 2013

PIER B

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Abstract-In through-the-wall radar imaging (TWRI), wall returns are often stronger than target returns, which make the targets behind walls invisible in the radar image. Spatial filtering that relies on the removal of the spatial zero-frequency components is a useful way for wall-clutter mitigation. Unfortunately, it applies to through-the-wall radar (TWR) with synthetic aperture array only. In this paper, a method based on spatial signature is proposed to suppress the wallclutter in multi-input and multi-output (MIMO) TWRI. Firstly, the traditional spatial filtering method is discussed, as well as the reasons for the inapplicability for MIMO TWR. Secondly, the wall and target spatial signatures based on MIMO array are analyzed, respectively. The results indicate that the former has stability and symmetry, whereas the latter not. Thirdly, according to the above differences, a new method, symmetry subtraction, is applied to describe the wallclutter suppression procedure. Finally, simulation results demonstrate that the proposed method is efficient in mitigating the wall returns and highlighting the targets.

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“…How to acquire these attributes has been researched. From [9], we have known that the pose angles of all the trihedrals in a radar image can be estimated by using a virtual aperture imaging model. It will not be discussed in this paper to avoid unnecessary repetition.…”

Section: Elements In Blg and Mstmentioning

confidence: 99%

“…In our previous work [9], a method of acquiring the attributes of a trihedral is presented. The orientation information of all the trihedrals in a through-the-wall radar image can be obtained by a virtual aperture imaging model [10].…”

Section: Introductionmentioning

confidence: 99%

Building interior layout reconstruction from through-the-wall radar image using MST-based method

Chen

Jin

et al. 2014

EURASIP J. Adv. Signal Process.

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In order to reconstruct the building interior layout from the through-the-wall radar image, this paper proposes a method based on minimum spanning tree (MST) which comes from the graph theory. The building layout graph (BLG) model is firstly established using the wall-wall-floor trihedrals inside the building. The vertex set and the edge set including the weight between any two nodes of the graph are defined afterwards. Then, the MST-based method is used to reconstruct the building interior layout. We have also improved the traditional method by applying the MST model with restricted conditions to solve the reconstruction problem when there exist interference targets in a building. The anechoic chamber measurements are carried out to demonstrate the suitability and efficacy of the MST-based method.

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Estimation of Pose Angle for Trihedral in Ultrawideband Virtual Aperture Radar

Cited by 3 publications

References 30 publications

Extraction of Building Features from Stand-Off Measured Through-Wall Radar Data

Extraction of Building Features from Stand-Off Measured Through-Wall Radar Data

A Wall-Clutter Suppression Method Based on Spatial Signature in Mimo Through-the-Wall Radar Imaging

Building interior layout reconstruction from through-the-wall radar image using MST-based method

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