- Open Access
Research on the 3D imaging algorithm of spin target based on the Hough transform
© Li and Pi; licensee Springer. 2013
- Received: 5 December 2012
- Accepted: 28 February 2013
- Published: 28 March 2013
As one of the most typical characteristics in space target motion, spin phenomenon has good 3D imaging application potential. Conventional target imaging algorithm fails to make full use of the rotating features of the target to obtain the characteristics of target space, and the speedy spin of targets will cause the dramatic changes in the positions of scattering center within short observation session, which may lead to the failure of imaging algorithm. Aiming at such a special phenomenon of the space target, the time frequency distribution curve of echoes in each scattering center could be mapped onto the parameter space to obtain the position of each scattering center by taking advantage of Hough transformation, thus the 3D features of spin target could be obtained. In this article, the 3D imaging algorithm was studied on the basis of Hough transformation, and its effectiveness was tested with simulation. Meanwhile, the translational motion and shielding effect of space target were discussed, and favorable imaging results were achieved.
- 3D imaging
- Spin target
- Hough transform
Study on the 3D inverse synthetic aperture radar (ISAR) imaging techniques has been attracting more and more attention [1–6]. Compared to 2D ISAR imaging techniques, more detailed information about the target can be provided by 3D ISAR imaging. The current 3D ISAR imaging algorithm mainly consists of two types. The first type takes advantage of various reception channels and receives the echo signals of the target on the basis of phase interference, and it can conduct 2D image for the signals received by each antenna with conventional imaging algorithm [7–9]. As a result, the three-dimensional spatial information of each scattering point can be extracted from the differences of 2D imaging interference phase. In the second type, the target 3D imaging is constructed with the 2D image sequence obtained from different observation angle through a receiving antenna . In both algorithms, a spatial freedom degree is added to obtain three-dimensional resolution.
The traditional ISAR imaging algorithm is based on slow turntable model. High-speed rotating targets, such as airplane propeller, spin precession-guided missile warhead, space debris, etc., often fail to meet the requirements of slow turntable model. However, for spin target, its rotating angular velocity can be estimated, which in actually provides a degree of freedom for 3D ISAR imaging [11, 12]. As in [13, 14], on the condition of the turntable model with high-speed rotating target and its spin angular velocity is known, the methods general radon transform and extended Hough transform were used to three-dimensional space information extraction for rotating targets. The basic idea of these algorithms is taking advantage of sine envelope of spin target to estimate the scattering points’ three-dimensional location by using curvilinear integral under range-compress domain. The operand of these algorithms is always huge because the energy accumulation along curve is four-dimensional curve detection process. In this article, sine envelope of spin target was used to estimate the 3D position of scattering point in the form of curvilinear integral within distance compressed domain, thus the extraction of 3D spatial information of the spin target could be realized.
In which, B = γT was the signal bandwidth.
3.3. D imaging algorithm
On the premise of given sine curve cycle (namely ω was given), its position and shape were determined by three spatial coordinate parameters x k ', y k ', and z k '. Therefore, the 3D position information of the scattering point could be obtained by extracting the information about sine curve.
However, it could be seen from the above analysis that, owning to the influence of the included angle α between the radar sight and target axis, the actually extracted position information of scattering point was not authentic position information, but the compression of real position, namely, the position coordinate extracted along the axis direction (namely along axis z) was about cosα times of the real position coordinate, while the position coordinate which was vertical to spin axis (namely along axes x and y) was about sinα times of the real position coordinate. Therefore, the smaller α was, the closer the coordinate along axis z would be. The better the resolution along axis z was, the worse the resolution along axes x and y would be, and vice versa.
For a target formed from one or several scattering points, the target echo was the sum of each scattering point. Therefore, sine curves might tangle with each other. In addition, with imaging treatment period, each scattering point may not always be shone by the radar beam owning to the shield, namely the scattering point may not have echo. Therefore, there might be discontinuities in sine curve. As a result, it was a little difficult to directly detect the sine curve within range residence time domain, also the image domain. Each sine curve in the image domain corresponded to a peak in the parameter domain, and the parameter corresponding to the peak was the 3D position coordinate of the scattering point. Consequently, with the help of Hough transformation, the detection for the global curve in the image domain could be converted to detection of peaks in easily realized parameter domain.
In the above equation, d(Φ) was the Hough transformation result of image D(m,n), Φ was the multi-dimensional vectors formed by related curve parameters, n = f(m; Φ) was the curve to be detected. Here, m was the corresponding residence time t, the curve parameter vector Φ consisted of three positional parameters, namely , , and , f(m; Φ) corresponded to the curve r = x k ' sin ωt + y k ' cos ωt + z k ' in the image for range residence time domain.
After Hough transformation, the corresponding curve of each scattering point in range residence time domain would produce a peak in the parameter domain. The estimation for the spatial position of scattering point could easily be realized through detecting the peaks in parameter domain.
According to certain standard and taking advantage of S(r,t) and X(r,t), the scattering coefficient of scattering point in this position could be estimated.
After the information about 3D position and scattering coefficient of this scattering point was obtained, the information about this scattering point in the echo data was eliminated, namely suppose , and the above procedures of parameter estimation were repeated with new data S(r, t), till was smaller than the pre-set threshold. At this moment, the information of all scattering points was extracted from echo data, and 3D image of the target could be reconstructed with the information.
Coordinate and scattering coefficient of each scattering point
Coordinate of 3D position
One or several scattering points could be used to obtain the estimation value of translational velocity, which would compensate for the translation component caused by this speed. However, the original 3D parameter domain would be added to 4D parameter domain . When estimating the parameter, 4D search was needed. Therefore, this method would greatly increase the calculated amount.
In this article, 3D imaging algorithm of spin target based on Hough transformation was proposed. By taking advantage of the sine envelope of spin target, the 3D position of scattering point was estimated in the form of curve integral within range compression domain, thus the extraction of 3D spatial information of the spin target could be realized, and this algorithm was tested to be valid through simulation experiment. In addition, the translational motion and shielding condition existed in the real target were also taken into consideration in the simulation experiment, and corresponding measures were adopted for translational motion compensation. Under the circumstance of incomplete target sine curve, the target parameter was extracted to realize the 3D imaging of spin target.
This study was supported by the National Natural Science Foundation of China (61271287) and the Fundamental Research Funds for the Central Universities (ZYGX2011J020).
- Chen VC, Li F, Ho SS: Analysis of micro-Doppler signatures. IEE Proc. Radar Sonar Navigat. 2003, 150(4):271-276. 10.1049/ip-rsn:20030743View ArticleGoogle Scholar
- Zhang Q, Yeo TS, Du G, Zhang SH: Estimation of three dimensional motion parameters in interferometric ISAR imaging. IEEE Trans. Geosci. Remote Sens. 2004, 42(2):292-300. 10.1109/TGRS.2003.815669View ArticleGoogle Scholar
- Tsao J, Steinberg BD: Reduction of sidelobe and speckle artifacts in microwave imaging: the CLEAN technique. IEEE Trans. Antennas Propagat. 1988, 36(4):543-556. 10.1109/8.1144View ArticleGoogle Scholar
- Sparr T, Krane B: Micro-Doppler analysis of vibrating targets in SAR. Proc. Inst. Electr. Eng.-Radar Sonar Navigat. 2003, 150(4):277-283. 10.1049/ip-rsn:20030697View ArticleGoogle Scholar
- Li J, Pi YM: Micro-Doppler signature feature analysis in terahertz band. J. Infrared Millim. Terahertz Waves 2010, 31(3):319-328.MathSciNetGoogle Scholar
- Li J, Ling H: Application of adaptive chirplet representation for ISAR feature extraction from targets with rotating parts. Proc. Inst. Electr. Eng.-Radar Sonar Navigat. 2003, 150(4):284-291. 10.1049/ip-rsn:20030729MathSciNetView ArticleGoogle Scholar
- Wang GY, Xia XG, Chen VC: Three-dimensional ISAR imaging of maneuvering targets using three receivers. IEEE Trans. Image Process. 2001, 10(3):436-448. 10.1109/83.908519View ArticleGoogle Scholar
- Xu XJ, Narayanan RM: Three-dimensional Interferometric ISAR imaging for target scattering diagnosis and modeling. IEEE Trans. Image Process. 2001, 10(7):1094-1102. 10.1109/83.931103View ArticleGoogle Scholar
- Mayhan JT, Burrows ML, Cuomo KM, Piou JE: High resolution 3D “snapshot” ISAR imaging and feature extraction. IEEE Trans. Aerosp. Electron. Syst. 2001, 37(2):630-642. 10.1109/7.937474View ArticleGoogle Scholar
- Walker JL: Range-Doppler imaging of rotating objects. IEEE Trans. Aerosp. Electron. Syst. 1980, AES-16(1):23-52.View ArticleGoogle Scholar
- Hansen KV, Toft PA: Fast curve estimation using preconditioned generalized radon transform. IEEE Trans. Image Process. 1996, 5(12):1651-1661. 10.1109/83.544572View ArticleGoogle Scholar
- Tof PA: Using the generalized radon transform for detection of curves in noisy images. IEEE International Conference on Acoustics, Speech and Signal Processing, Atlanta, USA, vol. IV 1996, 2219-2222.Google Scholar
- Wang Q, Xing MD: High-resolution three-dimensional radar imaging for rapidly spinning targets. IEEE Trans. Geosci. Remote Sens. 2008, 46(1):22-30.View ArticleGoogle Scholar
- Zhang Q, Yeo TS: Imaging of a moving target with rotating parts based on the Hough transform. IEEE Trans. Geosci. Remote Sens. 2008, 46(1):291-299.View ArticleGoogle Scholar
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