ISAR imaging of complex motion targets based on Radon transform cubic chirplet decomposition

For targets with complex motion, the echo of inverse synthetic aperture radar (ISAR) is a time-varying frequency signal in azimuth. Hence, the traditional range-Doppler (R-D) algorithm based on a constant frequency is invalid. In this letter, a novel ISAR imaging method for targets with complex motion is presented. The echo in azimuth is characterized as an amplitude-modulated (AM)-cubic phase signal, which is closer to the real ISAR scene, and Radon transform cubic chirplet decomposition (RTCCD) algorithm is proposed to process the signal. By introducing Radon transform and improved cubic chirplet function (CPF), the proposed algorithm estimates the chirp rate and the cubic chirp rate simultaneously to avoid error accumulation. Therefore, the parameter estimation precision is improved, and a high quality ISAR image can be obtained. Simulations and real data experiment validate the effectiveness of the proposed method.     

Parameter estimation for maneuvering targets with complex motion via scaled double-autocorrelation transform

In this paper, a novel parameter estimation method is proposed for maneuvering targets with complex motion. In the proposed method, the second-order keystone transform (SOKT) and modified range cell migration correction (RCMC)/integration are jointly applied to overcome the velocity ambiguity and eliminate the envelope migration. Then, since the azimuth echoes of maneuvering targets with complex motion can be modeled as cubic phase (CP) signals after motion compensation, a new transform, namely, scaled double-autocorrelation transform (SCDCT), is defined. This transform can be essentially interpreted as the two-dimensional (2-D) Fourier transform (FT) of a scaled parametric instantaneous double-autocorrelation (PIDAC) function. By employing this derived transform, the estimated chirp rates and derivative of chirp rates of CP signals can be obtained simultaneously without searching operation and thus the computational burden can be reduced significantly. Furthermore, the characteristics of cross terms and anti-noise performance of SCDCT are theoretically analyzed. Compared with three other popular methods, product high-order match phase transform, TC-dechirp Clean and modified discrete chirp Fourier transform, the proposed SCDCT-based method is more computationally efficient and has better estimation performance in low signal-to-noise ratio (SNR) circumstance. Simulation results verify the effectiveness of the proposed SCDCT-based method.     

基于稀疏表示和时频变换的ISAR成像算法

逆合成孔径雷达对空中机动飞行目标进行成像,在成像积累时间内,成像投影平面和横向尺度随时间变化,许多参数很难准确提取,人们无法获得更多的先验知识。一般采用鲁棒性强的距离-多普勒(RD)算法进行成像,但传统的RD成像算法基于目标匀速转动和方位均匀采样的假设,若用其对机动目标进行成像,则图像模糊,尤其对于随机缺损的雷达回波数据,其成像质量显著下降,甚至无法辨识。通过引入稀疏表示和时频变换,提出了一种基于稀疏表示和时频变换的距离-瞬时多普勒成像算法,其可对一般机动飞行目标进行有效成像。实验结果验证了所提算法的有效性和可行性。     

Resolution and parameters estimations for multiple maneuvering targets

Resolution and parameters estimations for multiple maneuvering targets in the same range cell is addressed in this work. The low-resolution radar cannot distinguish multiple targets in both distance and angle, but the detection of Doppler frequency variation of the multiple maneuvering targets can be used to resolve this problem. At present, most of researches on detection of Doppler frequency variation are carried out with time-frequency analysis methods, such as Fractional Fourier transformation （FRFT）, Adaptive Chirplet transformation （ACT）, and Wigner-Ville distribution （WVD） and so on, which need satisfy enough time duration and sampling theorem. This paper proposes a new method of resolution and parameters estimation for multiple maneuvering targets based on Compressive Sensing （CS） and clustering technique, which samples at low rate and short time duration without sacrificing estimation performance. Simulation results validate the effectiveness of the proposed algorithm, and also show that the performance of the proposed method is superior to that of FRFT in the condition of multiple targets.     

强跟踪输入估计概率假设密度多机动目标跟踪算法

针对多机动目标跟踪中,目标数目未知及加速度不确定的问题,提出一种强跟踪输入估计（modifiedinputestimation,MIE）概率假设密度多机动目标跟踪算法．在详细分析算法的基础上,通过引入强跟踪多重渐消因子,以不同速率实时调节滤波器各个通道的预测协方差及相应的滤波器增益,从而实现MIE算法对加速度未知或发生人幅度突变的机动目标白适应跟踪能力;并将该算法与概率假设密度滤波算法有效结合,町以较好地跟踪未知数目的多机动目标．仿真结果表明,新算法比传统的多机动目标跟踪算法具有更岛的跟踪精度,且具有较好的实时性．     

一种新的雷达和红外融合算法

针对作机动飞行的空中目标,利用目标多普勒信息和红外辐射信息,建立具有Markov跳交参数的红外系统距离估计模型,基于结构随机跳变系统最优滤波理论,提出了一种红外系统距离估计算法.基于交互式多模型算法(IMM),以红外测距为伪距量测,提出了一种新的主动雷达和红外融合算法.对一个高机动目标跟踪进行了仿真,结果表明红外距离估计误差小,融合算法跟踪精度高,性能良好,易于工程实现.     

Cross-range scaling for non-uniformly rotating targets by sharpness maximization

Cross-range scaling is a crucial work to target recognition in inverse synthetic aperture radar (ISAR) systems. However, it is still challenging for maneuvering targets due to the time-varying Doppler history, which degrades the image as well. This paper presents a novel method to estimate the scaling factor by optimizing the image quality. One highlight of this work is the usage of Matching Fourier Transform (MFT), which coincides with the non-uniform rotation model or the variant Doppler. The backscattered reflectivity can be concentrated in MFT domain when the parametric transform is perfectly matched with the signal form. Thus another novelty lies in the sharpness maximization of the MFT image by iteratively compensating the range-dependent phase error joint with the image formation. The proposed scheme manages to output the scaling factor and enhanced images. To alleviate the influence of residual translation phase error on estimation, conventional autofocus methods are invoked during the iterations. Simulation and real-data results confirm the effectiveness of the proposed method.     

划分交互式箱粒子概率假设密度滤波法

针对现有的多机动目标追踪问题,将交互式多模型(interacting multiple model,IMM)思想与箱粒子概率假设密度滤波器(box probability hypothesis density filter,Box-PHD)相结合,并针对箱粒子在区间密集杂波等复杂环境下箱体偏大,所导致的箱粒子冗余和目标跟踪位置估计不精确等问题,引入箱粒子划分技术,提出一种划分交互式概率假设密度滤波(partitioned interacting multiple model probability hypothesis density filter,PIMM-Box-PHD)算法,来处理椭圆形多机动目标的跟踪问题。该算法首先在预测阶段针对多目标的机动问题引入IMM预测,利用多模型交互方法来解决目标运动时模型失配问题;其次,利用箱划分技术将预测得到的箱粒子划分为大小和权值相同的多个子箱,以提高目标位置估计精度;最后,利用Box-PHD滤波对划分后的小箱粒子集进行区间量测更新。利用实验验证了PIMM-Box-PHD算法在多机动目标跟踪方面的良好性能,以及相较于IMM-Box-PHD算法在目标位置估计方面的优势。     

基于均匀圆阵的LFM信号DOA估计

针对为解决对雷达和通信中的线性调频信号源(LFM)定位的难题,为提高定位精度,提出了一种基于均匀圆阵的测向定位方法.方法通过分数傅立叶变换(FRFT)对LFM信号的能量聚焦特性,构造出一种新的FRFT域阵列数据模型;采用UCA-RB-MUSIC算法测向,通过模式激励法把阵元空间转换到实波束空间,对谱函数进行二维搜索,得到辐射源的方位角和俯仰角.通过理论推导和仿真验证,证明方法具有测向精度高、运算量小、360°全方位测向的优点.     

一种基于树形结构融合的目标跟踪算法

针对空中机动目标,利用目标多普勒信息和红外辐射信息建立具有树形结构的红外雷达跟踪系统状态估计模型.基于无迹卡尔曼滤波方法,提出一种自适应双波段红外并行融合算法,并基于红外融合结果,采用序贯滤波融合方法,与雷达传感器实现深层交互多模型融合估计.通过仿真表明了所提出的方法具有更小的距离跟踪误差和良好的跟踪精度.     

A low complexity coherent integration method for maneuvering target detection

This paper considers the coherent integration problem for detecting a maneuvering target with complex motions, where the velocity, acceleration and jerk result in respectively the range migration (RM), linear Doppler frequency migration (LDFM) and quadratic Doppler frequency migration (QDFM) within the coherent pulse interval. A new coherent integration algorithm based on keystone transform (KT) and generalized dechirp process (GDP), i.e., KTGDP, is proposed. In this method, KT and fold factor searching are first employed to correct the RM, and then GDP is applied to estimate the target's radial acceleration and jerk. With the estimated motion parameters, LDFM and QDFM can be compensated and the coherent integration can be achieved via Fourier transform. In addition, at the cost of some performance loss, a fast coherent integration method combing KT and cubic phase function (CPF), i.e., KTCPF, is also introduced to further reduce the computational complexity. Compared with the generalized Radon–Fourier transform (GRFT) method, the proposed algorithms can avoid the blind speed side lobe (BSSL) effect and have much lower computational burden. Finally, we evaluate the performance via some numerical simulations.     

针对机动目标的改进型交互多模型跟踪算法研究

为了提高对机动目标的跟踪精度,更准确地获得目标实时位置与速度信息,提出了一种改进型交互多模型跟踪算法.采用目标特征数据为初始数据提供限定域,然后在滤波器中加入调节参数,从而利用目标状态增益矩阵与协方差矩阵的迭代完成对跟踪精度的优化.实验仿真分析了机动目标的3种常见状态,并与传统交互多模型跟踪算法进行了对比.实验结果显示...     

多回波环境中多机动目标跟踪的新算法*

目标的状态估计与数据关联是机动多目标跟踪中的关键问题.针对杂波环境中多机动目标的跟踪问题,本文首先引入一种自适应滤波算法,并与快速概率数据关联算法结合,提出一种适于实际应用的密集回波环境下机动多目标跟踪的新算法-快速自适应概率数据关联(FAPDA)算法,利用近似概率数据关联(PDA)算法的计算量达到优于联合概率数据关联(JPDA)算法的跟踪效果,并能快速检测到机动.通过与JPDA算法的仿真结果进行对比,表明了该算法的有效性和快速性.     

An accurate SISAR imaging method of ground moving target in forward scatter radar

In order to accurately extract the profile image of the ground moving targets(GMT) in the forward scatter radar(FSR),a forward scatter signal model of GMT is built based on the Fresnel-Kirchhoff diffraction formula,the multipath propagation model and the high order phase approximation method.The classical shadow inverse synthetic aperture radar(SISAR) imaging algorithm is modified using the proposed signal model.In the modified imaging algorithm,the impacts of ground reflection,non-linear variation of target Doppler frequency and the target motion direction on imaging accuracy are discussed.Meanwhile,non-linear and time-dependent parameters are used to compensate for the motion phase of the GMT forward scatter signal,and a novel estimation method of the height difference and median line height of the target profile is put forward under the multipath interference.Finally,both the accuracy of the proposed signal model and the effectiveness of the modified imaging algorithm are validated by the simulation results.     

Detection,parameter estimation and imaging of maneuvering target in wide-band signal

The signal-to-noise ratio may be increased by the cross-range coherence integration so as to detect the moving target in low signal-to-noise ratio (SNR) condition. But, the radial velocity, acceleration and the change of acceleration due to the maneuvering motion of target may induce serious range migration and cross-range high-order phase terms leading to the unfocused cross-range image, the reduction of signal-noise ratio and the invalidation of target detection. Therefore, in order to solve these problems, this paper proposes a new method based on the adjacent correlation and scale transform methods for detection, parameters estimation and imaging of maneuvering targets in wide-band signal. This method can align the range and remove the cross-range high-order phase terms induced by the radial motion of target, enabling us to detect the target and estimate its moving parameters better. Finally, the simulated target is used to confirm that the method proposed by this paper can perfectly detect the maneuvering target in low signal-to-noise ratio condition, estimate its motion parameters and obtain an ISAR image of target. Supported by the National Natural Science Foundation of China (Grant No. 60502044), and the Program for New Century Excellent Talents in University (Grant No. NCET-06-0861)     

被动测向分段等速直线机动目标参数综合识别模型

对于纯方位机动目标跟踪问题，通过选取适当的状态变量和参数，在分析参数的一阶灵敏度函数的基础上，建立了分段等速直线运动目标运动参数综合识别模型和算法．仿真结果表明，该算法对本文描述的机动目标运动参数的识别有较好的适应性．     

交互式箱粒子标签多伯努利机动目标跟踪算法

针对多机动目标追踪问题, 将交互式多模型(Interacting multiple model, IMM)思想与箱粒子标签多伯努利滤波器(Box-labeled multi-bernoulli filter, Box-LMB)相结合, 提出交互式箱粒子标签多伯努利滤波器(IMM-Box-LMB)算法.该算法首先通过扩展多目标状态, 引入模型匹配概率变量, 并利用量测信息在预测阶段更新模型匹配概率, 进而使用交互式多模型算法对每个箱粒子状态进行混合估计.其次, 在更新阶段提出二次收缩算法, 通过二次收缩算法使更新后的箱粒子具有更大的区间和存活概率, 也更加接近真实目标位置, 从而达到提升后续时刻箱粒子多样性的目的.仿真结果表明, 二次收缩算法能够有效地提升箱粒子的多样性.将二次收缩算法应用于IMM-Box-LMB算法, 能够在不同信噪比下稳定准确地估计机动目标的个数.相同条件下, 与匀速直线运动(Constant velocity, CV)模型下的Box-LMB算法相比, IMM-Box-LMB算法能够对多机动目标的数目以及状态进行更加有效的估计.     

ISAR imaging via sparse frequency-stepped chirp signal

Frequency-stepped chirp signal can simplify the designation of radar system.However,it has a shortcoming of Doppler ambiguity for high-speed moving targets.Therefore,it is of great significance to study how to increase its equivalent pulse repeat frequency.The back scattering field of the ISAR target has strong sparsity;that is to say,most energy is contributed merely by a few scattering centers.Hence,based on the theory of the sparse signal representation,a novel method for ISAR imaging via sparse frequency-stepped chirp signals is proposed by analyzing the signal model of the target.In the proposed method,part of sub-pulses of the frequency-stepped chirp signal is randomly selected to transmit,and then the 2D high-resolution image of the target can be constructed by sparse signal decomposition.At the cost of computational resources,the method can effectively resolve the problem of Doppler ambiguity,decrease the sidelobes and obtain a super-resolution image.Furthermore,the validity of the proposed approach is confirmed by the results of numerical simulations and real data.     

基于自适应卡尔曼滤波的机动目标跟踪算法

在机动目标跟踪过程中,由于目标运动的不确定性,雷达系统接收的数据存在噪声,使预置目标运动模型通常很难得到较高的跟踪精度。为此,以自适应卡尔曼滤波为基础,将直角坐标系和球坐标系相结合,提出了一种混合坐标系下的自适应卡尔曼滤波算法。算法避免了两个坐标系变换引起的噪声统计规律变化问题,并针对目标发生大机动运动的情况,自适应的调整动态模型中机动目标运动参数。蒙特卡洛仿真结果表明,改进算法的收敛速度和对状态的估计精度均得到优化,并对机动目标具有较好的跟踪性能。     

Clutter suppression and ground moving target imaging approach for hypersonic vehicle borne multichannel radar based on two-step focusing method

In this paper, a novel clutter suppression and ground moving target imaging approach is proposed for hypersonic vehicle (HSV) borne multichannel (MC) synthetic aperture radar (SAR) system. Compared with the traditional MC SAR ground moving target indication (GMTI) methods, the proposed algorithm can suppress clutter and decrease the moving target energy loss based on the improved MC clutter suppression method in the chirp Fourier transform (CFT) domain. Moreover, the moving target can be accurately focused by compensating the range curvature and the third order phase item. In addition, the motion parameters can be estimated precisely and the moving target can be focused accurately. Finally, the effectiveness of the proposed method is validated by the simulation examples.