Sensor scheduling for ground maneuvering target tracking in presence of detection blind zone

Continuous and stable tracking of the ground maneuvering target is a challenging problem due to the complex terrain and high clutter. A collaborative tracking method of the multisensor network is presented for the ground maneuvering target in the presence of the detection blind zone(DBZ). First, the sensor scheduling process is modeled within the partially observable Markov decision process(POMDP) framework. To evaluate the target tracking accuracy of the sensor, the Fisher information is applied to constructing the reward function. The key of the proposed scheduling method is forecasting and early decisionmaking. Thus, an approximate method based on unscented sampling is presented to estimate the target state and the multi-step scheduling reward over the prediction time horizon. Moreover, the problem is converted into a nonlinear optimization problem, and a fast search algorithm is given to solve the sensor scheduling scheme quickly. Simulation results demonstrate the proposed nonmyopic scheduling method(Non-MSM) has a better target tracking accuracy compared with traditional methods.

ACCURATE DETECTION OF HIGH-SPEED MULTI-TARGET VIDEO SEQUENCES MOTION REGIONS BASED ON RECONSTRUCTED BACKGROUND DIFFERENCE

The paper first discusses shortcomings of classical adjacent-frame difference. Sec ondly, based on the image energy and high order statistic(HOS) theory, background reconstruction constraints are setup. Under the help of block-processing technology, background is reconstructed quickly. Finally, background difference is used to detect motion regions instead of adjacent frame difference. The DSP based platform tests indicate the background can be recovered losslessly in about one second, and moving regions are not influenced by moving target speeds. The algorithm has important usage both in theory and applications.

Capurability analysis for arbitrarily high-speed maneuvering targets

The capturability of an arbitrarily maneuvering target featuring speed superiority over an interceptor is analyzed for Augmented Pure Proportional Navigation(APPN)and RetroAugmented Proportional Navigation(RAPN)guidance.This paper focuses on intercepting arbitrary maneuvers to study more general interception problems.A comparative analysis of the capture region between head-on interception related to APPN and head-pursuit interception related to RAPN is proposed.The results indicate that RAPN performs better than APPN in capturability.It is concluded that increasing the target velocity,which increases the velocity ratio,significantly weakens the capturability of the interceptor,and the average acceleration and relative distance affect the location of the capture region but not its size.The analysis is based on prior knowledge of the target maneuver,which inevitably leads to deviations from actual maneuvers in practical engagement,so a deviation analysis is implemented.The effective capture region shrinks as the absolute value of acceleration deviation increases,and the RAPN has a better deviation fault tolerance compared with the APPN.The results reveal that a larger relative distance can weaken the deviation fault tolerance,and the target velocity has opposite effects on head-on and head-pursuit interception.

GS-orthogonalization OMP method for space target detection via bistatic space-based radar

A space-based bistatic radar system composed of two space-based radars as the transmitter and the receiver respectively has a wider surveillance region and a better early warning capability for high-speed targets,and it can detect focused space targets more flexibly than the monostatic radar system or the ground-based radar system.However,the target echo signal is more difficult to process due to the high-speed motion of both space-based radars and space targets.To be specific,it will encounter the problems of Range Cell Migration(RCM)and Doppler Frequency Migration(DFM),which degrade the long-time coherent integration performance for target detection and localization inevitably.To solve this problem,a novel target detection method based on an improved Gram Schmidt(GS)-orthogonalization Orthogonal Matching Pursuit(OMP)algorithm is proposed in this paper.First,the echo model for bistatic space-based radar is constructed and the conditions for RCM and DFM are analyzed.Then,the proposed GS-orthogonalization OMP method is applied to estimate the equivalent motion parameters of space targets.Thereafter,the RCM and DFM are corrected by the compensation function correlated with the estimated motion parameters.Finally,coherent integration can be achieved by performing the Fast Fourier Transform(FFT)operation along the slow time direction on compensated echo signal.Numerical simulations and real raw data results validate that the proposed GS-orthogonalization OMP algorithm achieves better motion parameter estimation performance and higher detection probability for space targets detection.

基于时频脊线提取的机动小目标多普勒展宽补偿方法

对雷达进行长时间积累来提高目标回波的信噪比(SNR),是小目标探测雷达信号处理的重要步骤。然而,对于高速机动目标,目标回波很可能出现距离徙动以及多普勒展宽(DFB),给脉间长时间积累带来困难。针对存在的DFB问题,文中利用慢时间维信号时频图脊线斜率与加速度的数学关系,提出一种基于时频脊线(TFR)提取的机动小目标DFB补偿方法。所提方法可以克服传统的搜索法计算效率不高、需要搜索范围先验信息,以及精度受搜索步长影响的不足,可在低SNR且没有加速度先验信息的情况下对加速度进行快速准确估计,解决DFB问题,实现脉间有效相干积累。通过仿真,验证了TFR提取法可以在没有任何先验信息和低SNR下快速准确计算出目标的加速度,从而对脉压信号进行准确补偿,实现机动小目标的有效检测。

面向高机动目标检测的激光雷达探测图像分角域识别方法

高速移动和快速变换的运动模式容易导致目标模糊,从而无法准确识别目标的轮廓、形状和位置。为此,提出了面向高机动目标检测的激光雷达探测图像分角域识别方法。首先,通过邻域范围内像素点距离异常检测,去除激光雷达探测图像中的噪声和异常信号;然后,利用L-R算法解决目标高速移动造成的图像模糊问题;最后,采用基于互信息的自适应角域划分方法将图像分割成不同的角度域,并通过卷积神经网络在各角域中进行高精度目标识别,以实现高机动目标激光雷达探测。实验结果表明,该方法能够有效去除高机动目标造成的激光雷达探测图像模糊现象;相较于其他传统方法,该方法的目标识别率和全类平均精度较高、单图识别平均耗时较低,具有良好的识别效果。

Hybrid integration method for highly maneuvering radar target detection based on a Markov motion model

To detect highly maneuvering radar targets in low signal-to-noise ratio conditions, a hybrid long-time integration method is proposed, which combines Radon-Fourier Transform(RFT), Dynamic Programming(DP), and Binary Integration(BI), named RFT-DP-BI. A Markov model with unified range-velocity quantification is formulated to describe the maneuvering target’s motion. Based on this model, long-time hybrid integration is performed. Firstly, the whole integration time is divided into multiple time segments and coherent integration is performed in each segment via RFT. Secondly, non-coherent integration is performed in all segments via DP. Thirdly, 2/4 binary integration is performed to further improve the detection performance. Finally, the detection results are exported together with target range and velocity trajectories. The proposed method can perform the long-time integration of highly maneuvering targets with arbitrary forms of motion.Additionally, it has a low computational cost that is linear to the integration time. Both simulated and real radar data demonstrate that it offers good detection and estimation performances.

一种基于CEI的GEO目标滑窗式机动探测方法

为了进一步提升地面跟踪站的监视预警能力,提出一种基于连线相位干涉测量(CEI)的地球静止轨道(GEO)目标滑窗式机动探测方法:立足于CEI的GEO短弧段定轨和预报,基于GEO卫星位保机动的持续小推力和轨道缓慢渐变特性,提出一种针对GEO目标尤其是非合作GEO目标的滑窗式准实时机动探测方法;并利用亚太七号和中星十号卫星的CEI仿真和实测数据进行机动探测。结果表明,东西机动和南北机动捕获与卫星发动机的点火时刻基本保持一致,机动探测延迟量均小于10 min;能够实现GEO目标的准实时机动告警。

一种面向纯方位机动目标跟踪的伪线性卡尔曼滤波方法

针对单站纯方位地面机动目标跟踪问题,提出一种改进距离参数化工具变量伪线性卡尔曼滤波算法。首先基于观测站探测范围,利用距离参数化将目标与观测站的相对距离划分为若干个子区间,并独立运行工具变量伪线性卡尔曼滤波器。然后基于滤波新息与新息协方差更新子滤波器权重并检测目标机动,通过重置子滤波器的权重和状态信息保证滤波稳定。最后将各子滤波器状态信息加权融合获取目标的状态,解决目标初始距离未知以及目标机动导致跟踪精度下降的问题。仿真结果显示,本文提出的算法的位置跟踪精度比传统方法有显著提高,能有效实现对机动目标的跟踪。

基于天基平台的空中高机动目标检测方法研究

提出了一种基于天基合成孔径雷达平台实现空中高机动目标的检测方法,相比于逆合成孔径雷达系统实现空中运动目标检测具有极大的灵活性。同时,文中充分考虑了空中运动目标(如飞机、导弹等)的机动性,区别于现有合成孔径雷达地面动目标显示方法对空中机动目标进行匀速直线运动的假设,因此文中方法更具有实际应用的价值。文中方法先采用相邻互相关方法对运动目标进行降阶处理,通过keystone方法对动目标进行统一的走动校正,然后对动目标信号进行变尺度的维格纳威尔分布变换,最后通过傅里叶变换能够充分实现对机动目标的有效相干积累,因而适用于低信噪比下对空中机动目标的检测。

基于奇异谱分析的空间非合作目标无源机动检测方法

针对空间非合作目标无源预警任务中无先验信息条件下的目标轨道异常检测难题,提出了基于奇异谱分析的目标机动无源检测方法。首先,在追踪卫星轨道坐标系下建立了相对动力学模型及无源相对视线角测量模型。然后,通过奇异谱分析对视线角原始测量序列进行特征提取,设计了差异化评价指标及其阈值的计算方法并利用与、或门逻辑进行综合机动判断。最后,在GEO类型轨道应用场景下对所提算法进行了数值仿真验证与结果分析。仿真结果表明,当无源测角精度不低于3×10^(-4)rad时,相对距离1000 km以内目标执行的1 m/s量级以上的小脉冲机动可被有效地检测,检测成功率超过90%。

基于时间节点联合估计的多平台纯方位机动目标跟踪算法

针对水面及水下纯方位变速、变向目标机动检测跟踪问题,提出一种基于时间节点联合估计的多平台纯方位机动目标跟踪算法,该算法将目标的机动时刻等价于“时间未知节点”,基于Taylor级数运动要素估计方法直接对目标机动时刻、目标机动前后运动要素进行估计。机动检测特征是估计机动时刻以及估计机动时刻迭代分量的迭代过程,当迭代过程震荡发散,或机动时刻估计值不在观测时间范围内时,目标未发生机动。仿真结果表明,该算法在跟踪中远距离海上变向变速目标时,具有较低的机动检测虚警率和较高的跟踪精度。

Signal waveform design to detect an underwater high-speed small target

The problem of sonar signal waveform design to detect a high-speed small target in an underwater environment is discussed. From theoretical analysis, time-frequency hop signal is regarded as the most suitable signal waveform in this application. To get precise target parameter estimation ability, the signal should have high range-Doppler resolution performance. The results of signal analysis show that hop signal with frequency serial coding as Costas array has sharp ambiguity characteristic, so it can be used in an active sonar system to detect a high speed small target. A scheme of frequency coding is also presented.

基于相对角动量的空间非合作目标机动仅测角检测方法

针对传统方法在空间非合作目标小脉冲轨道机动检测过程中性能不佳的问题,在构建了一种基于相对角动量的新型待检特征量的基础上,提出了利用无源光学相机仅测角信息的轨道机动检测新方法。首先,在轨道坐标系下建立了基于Clohessy-Wiltshire方程的相对动力学模型和相机偏离质心安装时的相对视线角测量模型;然后,给出了相对角动量的定义并量化分析了以相对角动量作为机动检测特征标志的优越性;接着,设计了基于二次滑窗方差比值的机动检测算法框架;最后,通过数值仿真对所提算法进行了有效性验证和性能分析。仿真结果表明,该方法可以实现对近圆轨道目标的快速轨道机动检测,机动时间点检测误差在60 s以内。

复杂背景下的低空无人机检测与跟踪算法

提出一种基于YOLOv5与CSRT算法优化的实时长跟踪方法,实现了对无人机在净空、城市、森林等场景的稳定跟踪。针对跟踪的不同阶段建立不同分辨率的两个捕获网络,分别对两个网络进行小目标检测优化和性能优化,并根据无人机数据集特点对其进行正负样本的添加以实现数据增强。然后,对CSRT算法使用GPU进行优化并结合特征点提取构建了低空无人机检测与跟踪模型。最后,将算法使用Tensorrt部署后在自建数据集上进行实验,实验结果表明,所提方法在RTX 2080Ti上实现了400FPS的跟踪性能,在NVIDIA Jetson NX上实现了70FPS的性能。在实际外场实验中也实现了稳定的长时间跟踪。

高速机动目标长时间相参积累算法

在高速高机动目标的长时间相参积累过程中,跨距离单元和跨多普勒单元现象的出现会导致雷达回波增益急剧降低,进而影响雷达目标检测能力。针对上述问题,提出一种高效的长时间积累算法。该算法首先对脉压回波信号做keystone变换消除目标的距离走动,然后利用改进相参积累型平滑广义三阶相位函数与改进相参积累型平滑三阶相位函数分别完成对目标的二阶与一阶加速度的估计,最后使用De-chirp技术消除目标的高阶多普勒相位项,完成回波在距离多普勒维度上的聚焦。由于所提算法利用了雷达回波的相参特性并通过恒定延迟引入机制,因此表现出优异的抗噪声性能与参数估计精度。仿真实验和理论分析都证实了所提算法的有效性与抗噪声鲁棒性。

一种基于航向估计的多平台纯方位目标转向机动检测算法

针对纯方位目标转向机动检测问题,提出一种基于航向估计的多平台纯方位目标机动检测算法。该算法通过选定假设机动点序列,解算假设机动点前后的两段目标运动要素,根据解算出的相邻段航向差序列变化来判别目标是否发生机动。基于Taylor级数要素解算模型,建立了两段运动要素联合解算模型和两段运动要素独立解算模型。通过对多种航路进行仿真计算,统计分析这两种解算模型下机动检测算法的虚警率、目标机动检测率、机动检测延迟时间以及机动时刻估计精度。仿真结果表明,两种解算模型下的机动检测算法能够有效地对转向机动目标进行机动检测。

Radar fast long-time coherent integration via TR-SKT and robust sparse FRFT

Long-time coherent integration(LTCI)is an effective way for radar maneuvering target detection,but it faces the problem of a large number of search parameters and large amount of calculation.Realizing the simultaneous compensation of the range and Doppler migrations in complex clutter back-ground,and at the same time improving the calculation efficiency has become an urgent problem to be solved.The sparse transformation theory is introduced to LTCI in this paper,and a non-parametric searching sparse LTCI(SLTCI)based maneuvering target detection method is proposed.This method performs time reversal(TR)and second-order Keystone transform(SKT)in the range frequency&slow-time data to complete high-order range walk compensation,and achieves the coherent integra-tion of maneuvering target across range and Doppler units via the robust sparse fractional Fourier transform(RSFRFT).It can compensate for the nonlinear range migration caused by high-order motion.S-band and X-band radar data measured in sea clutter background are used to verify the detection performance of the proposed method,which can achieve better detection performance of maneuvering targets with less computational burden compared with several popular integration methods.

A modified variable rate particle filter for maneuvering target tracking

To address the problem of maneuvering target tracking, where the target trajectory has prolonged smooth regions and abrupt maneuvering regions, a modified variable rate particle filter （MVRPF） is proposed. First, a Cartesian-coordinate based variable rate model is presented. Compared with conventional variable rate models, the proposed model does not need any prior knowledge of target mass or external forces. Consequently, it is more convenient in practical tracking applications. Second, a maneuvering detection strategy is adopted to adaptively adjust the parameters in MVRPF, which helps allocate more state points at high maneuver regions and fewer at smooth regions. Third, in the presence of small measurement errors, the unscented particle filter, which is embedded in MVRPF, can move more particles into regions of high likelihood and hence can improve the tracking performance. Simulation results illustrate the effectiveness of the proposed method.

目标机动检测算法综述

对机动目标跟踪中的机动检测技术进行了较为全面的综述。首先将机动检测表述为变化检测问题;然后给出了现有机动检测技术的三种分类方法;随后以检测器输入为主线对现有机动检测算法做了详细的介绍,指出了各种算法的基本原理、优缺点及相互关系;最后展望了机动检测技术的发展方向。