Frame processing method offers a model-based approach to Inverse Synthetic Aperture Radar(ISAR) imaging. It also provides a way to estimate the rotation rate of a non-cooperative target from radar returns via the fram...Frame processing method offers a model-based approach to Inverse Synthetic Aperture Radar(ISAR) imaging. It also provides a way to estimate the rotation rate of a non-cooperative target from radar returns via the frame operator properties. In this paper, the relationship between the best achievable ISAR image and the reconstructed image from radar returns was derived in the framework of Finite Frame Processing theory. We show that image defocusing caused by the use of an incorrect target rotation rate is interpreted under the FP method as a frame operator mismatch problem which causes energy dispersion. The unknown target rotation rate may be computed by optimizing the frame operator via a prominent point. Consequently, a prominent intensity maximization method in FP framework was proposed to estimate the underlying target rotation rate from radar returns. In addition, an image filtering technique was implemented to assist searching for a prominent point in practice. The proposed method is justified via a simulation analysis on the performance of FP imaging versus target rotation rate error.Effectiveness of the proposed method is also confirmed from real ISAR data experiments.展开更多
The micro-Doppler effect of moving targets may suffer from aliasing and Doppler migration due to the translational motion, which affects the application in real-time target identification. A new compensating method fo...The micro-Doppler effect of moving targets may suffer from aliasing and Doppler migration due to the translational motion, which affects the application in real-time target identification. A new compensating method for the rotationally symmetric target is proposed and demonstrated. By utilizing the micro-Doppler symmetry cancellation effect, the method can accurately compensate the translation effect, meanwhile, it behaves well in noise restraint. Computer simulations verify the high accuracy and efficiency of this method.展开更多
The ability to build an imaging process is crucial to vision measurement.The non-parametric imaging model describes an imaging process as a pixel cluster,in which each pixel is related to a spatial ray originated from...The ability to build an imaging process is crucial to vision measurement.The non-parametric imaging model describes an imaging process as a pixel cluster,in which each pixel is related to a spatial ray originated from an object point.However,a non-parametric model requires a sophisticated calculation process or high-cost devices to obtain a massive quantity of parameters.These disadvantages limit the application of camera models.Therefore,we propose a novel camera model calibration method based on a single-axis rotational target.The rotational vision target offers 3D control points with no need for detailed information of poses of the rotational target.Radial basis function(RBF)network is introduced to map 3D coordinates to 2D image coordinates.We subsequently derive the optimization formulization of imaging model parameters and compute the parameter from the given control points.The model is extended to adapt the stereo camera that is widely used in vision measurement.Experiments have been done to evaluate the performance of the proposed camera calibration method.The results show that the proposed method has superiority in accuracy and effectiveness in comparison with the traditional methods.展开更多
Cross-range scaling plays an important role in the inverse synthetic aperture radar(ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation(FFT). However, the F...Cross-range scaling plays an important role in the inverse synthetic aperture radar(ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation(FFT). However, the FFT technique is resolution limited, so that the FFT-based algorithms will fail in the rotation velocity(RV) estimation of the slow rotation target. In this paper,we propose an accurate cross-range scaling algorithm based on the multiple signal classification(MUSIC) method. We first select some range bins with the mono-component linear frequency modulated(LFM) signal model. Then, we dechirp the signal of each selected range bin into the form of sinusoidal signal, and utilize the super-resolution MUSIC technique to accurately estimate the frequency. After processing all the range bins, a linear relationship related to the RV can be obtained. Eventually, the ISAR image can be scaled. The proposal can precisely estimate the small RV of the slow rotation target with low computational complexity. Furthermore, the proposal can also be used in the case of cross-range scaling for the sparse aperture data. Experimental results with the simulated and raw data validate the superiority of the novel method.展开更多
基金Partially supported by Australian Air Force Office of Scientific Research(AFOSR)Grant(FA2386-13-1-4080)
文摘Frame processing method offers a model-based approach to Inverse Synthetic Aperture Radar(ISAR) imaging. It also provides a way to estimate the rotation rate of a non-cooperative target from radar returns via the frame operator properties. In this paper, the relationship between the best achievable ISAR image and the reconstructed image from radar returns was derived in the framework of Finite Frame Processing theory. We show that image defocusing caused by the use of an incorrect target rotation rate is interpreted under the FP method as a frame operator mismatch problem which causes energy dispersion. The unknown target rotation rate may be computed by optimizing the frame operator via a prominent point. Consequently, a prominent intensity maximization method in FP framework was proposed to estimate the underlying target rotation rate from radar returns. In addition, an image filtering technique was implemented to assist searching for a prominent point in practice. The proposed method is justified via a simulation analysis on the performance of FP imaging versus target rotation rate error.Effectiveness of the proposed method is also confirmed from real ISAR data experiments.
文摘The micro-Doppler effect of moving targets may suffer from aliasing and Doppler migration due to the translational motion, which affects the application in real-time target identification. A new compensating method for the rotationally symmetric target is proposed and demonstrated. By utilizing the micro-Doppler symmetry cancellation effect, the method can accurately compensate the translation effect, meanwhile, it behaves well in noise restraint. Computer simulations verify the high accuracy and efficiency of this method.
基金Science and Technology on Electro-Optic Control Laboratory and the Fund of Aeronautical Science(No.201951048001)。
文摘The ability to build an imaging process is crucial to vision measurement.The non-parametric imaging model describes an imaging process as a pixel cluster,in which each pixel is related to a spatial ray originated from an object point.However,a non-parametric model requires a sophisticated calculation process or high-cost devices to obtain a massive quantity of parameters.These disadvantages limit the application of camera models.Therefore,we propose a novel camera model calibration method based on a single-axis rotational target.The rotational vision target offers 3D control points with no need for detailed information of poses of the rotational target.Radial basis function(RBF)network is introduced to map 3D coordinates to 2D image coordinates.We subsequently derive the optimization formulization of imaging model parameters and compute the parameter from the given control points.The model is extended to adapt the stereo camera that is widely used in vision measurement.Experiments have been done to evaluate the performance of the proposed camera calibration method.The results show that the proposed method has superiority in accuracy and effectiveness in comparison with the traditional methods.
基金supported by the National Natural Science Foundation of China (61871146,61622107)the China Scholarship Council(201906120113)。
文摘Cross-range scaling plays an important role in the inverse synthetic aperture radar(ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation(FFT). However, the FFT technique is resolution limited, so that the FFT-based algorithms will fail in the rotation velocity(RV) estimation of the slow rotation target. In this paper,we propose an accurate cross-range scaling algorithm based on the multiple signal classification(MUSIC) method. We first select some range bins with the mono-component linear frequency modulated(LFM) signal model. Then, we dechirp the signal of each selected range bin into the form of sinusoidal signal, and utilize the super-resolution MUSIC technique to accurately estimate the frequency. After processing all the range bins, a linear relationship related to the RV can be obtained. Eventually, the ISAR image can be scaled. The proposal can precisely estimate the small RV of the slow rotation target with low computational complexity. Furthermore, the proposal can also be used in the case of cross-range scaling for the sparse aperture data. Experimental results with the simulated and raw data validate the superiority of the novel method.
