Method for compensating translational motion of rotationally symmetric target based on local symmetry cancellation

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.

Target rotation parameter estimation for ISAR imaging via frame processing

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.

Non-parametric camera calibration method using single-axis rotational target

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.

ISAR cross-range scaling based on the MUSIC technique

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.

点迹-曲线关联算法的旋转对称群目标分辨

基于微多普勒效应分析的弹道目标识别得到了广泛研究,然而传统的微多普勒特征提取技术大都难以解决空间群目标的分辨与识别。针对这一问题,提出了一种基于点迹-曲线关联算法的空间群目标分辨方法。在以旋转对称群目标为研究对象的前提下,建立了具有滑动散射特性的目标模型并分析推导了散射点的微动表达式,利用点迹凝聚处理抑制了一维距离像旁瓣,在此基础上,提出了点迹-曲线关联算法分离出混叠的群目标微多普勒曲线,通过各曲线的极值包络特性差异实现了旋转对称群目标分辨。实验仿真验证了本方法的正确性和可行性。

基于压缩感知的进动目标ISAR成像方法

针对弹道中段进动目标姿态复杂变化给逆合成孔径雷达(Inverse Synthetic Aperture Radar,ISAR)成像带来的困难,在分析旋转对称进动目标回波模型的基础上,给出了回波模型的线性化表征形式,提出了基于压缩感知的进动目标成像方法,根据算法资源需求提供了两种数据处理方式,即二维联合处理和分频处理,并对二者的性能进行了分析比较。基于暗室测量数据的实验结果表明,与现有方法相比,所提方法通过少量回波数据即可实现进动目标的高分辨成像,改善了成像质量,并提高了对进动参数的稳定性。

旋转对称目标滑动型散射中心的微Doppler特性

微动特征是当前学术界公认的弹道中段目标雷达识别的有效特征之一,目前的特征提取与识别研究通常假定散射中心与目标自身的微动规律一致,其微Doppler效应为正弦调频信号.但这种假设忽视了随着观测视角的改变,散射中心将在目标体上滑动这一事实.文中对弹体不同位置散射中心的微运动特性进行了完整建模,首次得到了滑动型散射中心微Doppler的数学表达式,并针对导弹攻防对抗条件导出其Taylor近似,指出并非文献上通常假定的正弦规律.设计了进动目标暗室动态测量实验,生动揭示了散射中心的这种滑动特性,实验现象与文中的理论分析结果相一致.文中揭示的微Doppler机理为弹头类目标微动特性模拟及特征反演等应用领域提供了更为准确的物理基础和数学模型.