Particle filter initialization in non-linear non-Gaussian radar target tracking

When particle filter is applied in radar target tracking, the accuracy of the initial particles greatly effects the results of filtering. For acquiring more accurate initial particles, a new method called “competition strategy algorithm” is presented. In this method, initial measurements give birth to several particle groups around them, regularly. Each of the groups is tested several times, separately, in the beginning periods, and the group that has the most number of efficient particles is selected as the initial particles. For this method, sample initial particles selected are on the basis of several measurements instead of only one first measurement, which surely improves the accuracy of initial particles. The method sacrifices initialization time and computation cost for accuracy of initial particles. Results of simulation show that it greatly improves the accuracy of initial particles, which makes the effect of filtering much better.

Synchronization of Radar Observations with Multi-Scale Storm Tracking

The 3-D radar reflectivity data has become increasingly important for use in data assimilation towards convective scale numerical weather prediction as well as next generation precipitation estimation. Typically, reflectivity data from multiple radars are objectively analyzed and mosaiced onto a regional 3-D Cartesian grid prior to being assimilated into the models. One multi-radar observations is the synchronization of all of the scientific issues associated with the mosaic of the observations. Since radar data is usually rapidly updated （-every 5-10 min）, it is common in current multi-radar mosaic techniques to combine multiple radar＇ observations within a time window by assunfing that the storms are steady within the window. The assumption holds well for slow evolving precipitation systems, but for fast evolving convective storms, this assumption may be violated and the mosaic of radar observations at different times may result in inaccurate storm structure depictions. This study investigates the impact of synchronization on storm structures in multiple radar data analyses using a multi-scale storm tracking algorithm.

ANALYSIS OF TARGET TRACKING BASED ON RANGE DIFFERENCE MEASUREMENT OF MULTISTATIC RADAR SYSTEM

An analysis is presented for target tracking with short range multistatic radar system in this paper. The velocity vector is introduced into the model to depict target motion more precisely. The system measurement equation is such constructed on the basis of range difference that make the tracking model independent of the transmitter position. Therefore the algorithm is very much suitable for the case that the transmitter is not fixed. Simulation results show that the algorithm has the advantages of fast tracking and small steady tracking errors, and can be used for tracking target in short range with multistatic radar system.

Joint resource allocation scheme for target tracking in distributed MIMO radar systems

A joint resource allocation scheme concerned with the sensor subset,power and bandwidth for range-only target tracking in multiple-input multiple-output(MIMO)radar systems is proposed.By selecting an optimal subset of sensors with the predetermined size and implementing the power allocation and bandwidth strategies among them,this algorithm can help achieving a better performance within the same resource constraints.Firstly,the Bayesian Cramer-Rao bound(BCRB)is derived from it.Secondly,a criterion for minimizing the BCRB at the target location among all targets tracking in a certain range is derived.Thirdly,the optimization problem involved with three variable vectors is formulated,which can be simplified by deriving the relationship between the optimal power allocation vector and the bandwidth allocation vector.Then,the simplified optimization problem is solved by the cyclic minimization algorithm incorporated with the sequential parametric convex approximation(SPCA)algorithm.Finally,the validity of the proposed method is demonstrated with simulation results.

Effect of biased estimation on radar-to-ESM track association

The track association problem of radar and electronic support measure （ESM） has been considered in the literature for several years. This problem is crucial for radar-to-ESM track fusion and is complicated by the presence of individual systematic errors and measurement errors. In order to improve the track association of radar and ESM sensors, a pseudo-linear filtering algorithm is proposed to estimate the target states and improve the stability of the filter. It is found that, however, the correct probability of radar- to-ESM track association decreases as the radar measurement error decreases, when the pseudo-linear filter is used for ESM sensor filtering. In view of the strange phenomenon, this paper analyzes the reason for it by using the statistic theory and further performs Monte Carlo simulation to verify the analysis.

Tracking multiple targets in MIMO radar via adaptive asymmetric joint diagonalization with deflation

In view of the low performance of adaptive asymmetric joint diagonalization（AAJD）, especially its failure in tracking high maneuvering targets, an adaptive asymmetric joint diagonalization with deflation（AAJDd） algorithm is proposed. The AAJDd algorithm improves performance by estimating the direction of departure（DOD） and direction of arrival（DOA） directly, avoiding the reuse of the previous moment information in the AAJD algorithm.On this basis, the idea of sequential estimation of the principal component is introduced to turn the matrix operation into a constant operation, reducing the amount of computation and speeding up the convergence. Meanwhile, the eigenvalue is obtained, which can be used to estimate the number of targets. Then, the estimation of signal parameters via rotational invariance technique（ESPRIT） algorithm is improved to realize the automatic matching and association of DOD and DOA. The simulation results show that the AAJDd algorithm has higher tracking performance than the AAJD algorithm, especially when the high maneuvering target is tracked. The efficiency of the proposed method is verified.

Passive Target Tracking in Non-cooperative Radar System Based on Particle Filtering

We propose a target tracking method based on particle filtering(PF) to solve the nonlinear non-Gaussian target-tracking problem in the bistatic radar systems using external radiation sources. Traditional nonlinear state estimation method is extended Kalman filtering (EKF), which is to do the first level Taylor series extension. It will cause an inaccuracy or even a scatter estimation result on condition that there is either a highly nonlinear target or a large noise square-error. Besides, Kalman filtering is the optimal resolution under a Gaussian noise assumption, and is not suitable to the non-Gaussian condition. PF is a sort of statistic filtering based on Monte Carlo simulation that is using some random samples (particles) to simulate the posterior probability density of system random variables. This method can be used in any nonlinear random system. It can be concluded through simulation that PF can achieve higher accuracy than the traditional EKF.

ALGORITHMS FOR TRACKING MANEUVERING TARGET WITH PHASED ARRAY RADAR

Several typical algorithms for tracking maneuvering target with phased array radar are studied in this paper. The constant gain filter with multiple models is analyzed. A typical method for adaptively controlling the sampling interval is modified. The performance of the single model and multiple model estimator with uniform and variable sampling interval are evaluated and compared. It is shown by the simulation results that it is necessary to apply the adaptive sampling policy based on the multiple model method when the maneuvering targets are tracked by the phased array radar since saving radar resources is more important. The adaptive algorithms of variable sampling interval are better than the algorithms of variable model. The adaptive policy to determine the sampling interval based on multiple model are superior than those based on the single model filter, because IMM estimator can adapt to the maneuver more quickly and the prediction covariance of IMM is the more sensitive and more reliable index than residual to determine the sampling interval. With IMM based method, lower sampling interval is required for a certain accuracy.

Track-to-Track Association Technique in Radar Network in the Presence of Systematic Errors

The presence of systematic measuring errors complicates track-to-track association, spatially separates the tracks that correspond to the same true target, and seriously decline the performances of traditional track-to-track association algorithms. Consequently, the influence of radar systematic errors on tracks from different radars, which is described as some rotation and translation, has been analyzed theoretically in this paper. In addition, a novel approach named alignment-correlation method is developed to estimate and reduce this effect, align and correlate tracks accurately without prior registration using phase correlation technique and statistic binary track correlation algorithm. Monte-Carlo simulation results illustrate that the proposed algorithm has good performance in solving the track-to-track association problem with systematic errors in radar network and could provide effective and reliable associated tracks for the next step of registration.

Vessel fusion tracking with a dual-frequency high-frequency surface wave radar and calibrated by an automatic identification system

High-frequency surface wave radar（HFSWR） and automatic identification system（AIS） are the two most important sensors used for vessel tracking.The HFSWR can be applied to tracking all vessels in a detection area,while the AIS is usually used to verify the information of cooperative vessels.Because of interference from sea clutter,employing single-frequency HFSWR for vessel tracking may obscure vessels located in the blind zones of Bragg peaks.Analyzing changes in the detection frequencies constitutes an effective method for addressing this deficiency.A solution consisting of vessel fusion tracking is proposed using dual-frequency HFSWR data calibrated by the AIS.Since different systematic biases exist between HFSWR frequency measurements and AIS measurements,AIS information is used to estimate and correct the HFSWR systematic biases at each frequency.First,AIS point measurements for cooperative vessels are associated with the HFSWR measurements using a JVC assignment algorithm.From the association results of the cooperative vessels,the systematic biases in the dualfrequency HFSWR data are estimated and corrected.Then,based on the corrected dual-frequency HFSWR data,the vessels are tracked using a dual-frequency fusion joint probabilistic data association（JPDA）-unscented Kalman filter（UKF） algorithm.Experimental results using real-life detection data show that the proposed method is efficient at tracking vessels in real time and can improve the tracking capability and accuracy compared with tracking processes involving single-frequency data.

Improved chirp scaling algorithm integrated motion compensation for parallel-track bistatic synthetic aperture radar

To compensate motion errors of images from the parallel-track bistatic synthetic aperture radar（BiSAR）,an improved chirp scaling algorithm（CSA） is proposed.Since velocity vector of the moving aircrafts in the parallel-track BiSAR system can not remain invariant in an aperture,an actual aperture is divided into subapertures so that it is reasonable to assume that the aircrafts move with constant acceleration vector in a subaperture.Based on this model,an improved CSA is derived.The new phase factors incorporate three-dimensional acceleration and velocity.The motion compensation procedure is integrated into the CSA without additional operation required.The simulation results show that the presented algorithm can efficiently resolve motion compensation for parallel-track BiSAR.

基于SiamFC的海上舰船跟踪实验设计

为了提高海上舰船跟踪准确率,设计了一种基于SAR图像的海上舰船跟踪实验方案。首先利用三组连续的卫星SAR图像构建数据集,然后根据海上舰船尺寸小、全卷积孪生网络(SiamFC)算法网络层数少的特点,提出将SiamFC算法应用于海上舰船跟踪过程。通过调整模型相关参数,并构建数据集,对模型进行了训练及测试。测试结果表明,该模型具有可行性。为了达到更好的跟踪效果,引入伽马算子对SAR图像进行增强,减少了SAR图像的相干斑点噪声。最后,对改进后的模型进行测试的结果表明,该方案的舰船跟踪准确率大大提高。

面向对海监视的舰船目标跟踪与航迹融合数据集

对海监视中航迹实时关联与轨迹融合任务是安全防控、区域态势监视、远程精确打击等军民应用领域的热点和难点问题,高质量的数据集对推动目标跟踪与融合技术在该领域的研究有重要作用。本研究针对目标跟踪与融合领域技术研究的数据需求以及目前公开数据集所存在的数据缺乏、场景设计针对性差、数据格式单一、数据描述不全等问题,通过仿真软件对复杂场景中多传感器多目标探测数据进行仿真,提供了一套面向典型对海监视场景(以舰船为探测对象的2D雷达与侦察传感器﹝ESM﹞)的目标跟踪与航迹融合数据集。其中仿真软件包括剧情发生器和传感器模拟器两部分,是一套成熟的目标跟踪场景仿真环境,提供逼真的探测数据模拟能力。本数据集涵盖的传感器对象包括2D雷达与侦察传感器,目标包括典型的海上舰船类别,并支持携带辐射源配置,设计了高速运动、密集交通、多传感器数据融合、特定舰船侦测和交叉定位等多种典型场景。本数据集中共包含368155条目标点迹,舰船数量为101条,时间范围15000秒,数据格式符合实际设备上报情景、探测误差模型符合实际。本数据集通过对数据误差进行正态性检验、对检测率、虚警率的场景检验以及实地调研,完成了对数据的准确性评估与数据完备性控制,可为舰船目标跟踪、轨迹融合等算法研究与验证提供基础数据。

融合毫米波与激光雷达的障碍物检测与跟踪方法

在园区环境中,无人车装载单一毫米波雷达或激光雷达传感器进行障碍物检测跟踪时存在探测范围有限、准确率低及稳定性差等问题。为此,提出一种基于毫米波雷达与激光雷达融合的多障碍物检测跟踪方法。利用改进欧氏聚类算法对道路内激光点云目标进行提取,基于信息筛选策略获得毫米波雷达数据中的有效目标;基于目标检测交并比与可靠性分析,对2种目标进行自适应融合,并利用跟踪门与联合概率数据关联(JPDA)算法完成前后帧数据匹配;应用多运动模型交互与无迹卡尔曼滤波实现障碍物跟踪。实车实验表明:相比单一毫米波雷达与激光雷达障碍物检测跟踪,所提方法有更好的准确性与稳定性。

基于蒙特卡罗策略梯度的雷达观测器轨迹规划

在目标跟踪过程的雷达观测器轨迹规划(OTP)中,针对马尔可夫步进规划智能决策问题,在离散动作空间上,提出了一种基于蒙特卡罗策略梯度(MCPG)算法的雷达轨迹规划方法.首先,联合目标跟踪状态、奖励机制、动作方案和雷达观测器位置,将OTP过程建模为一个连续的马尔可夫决策过程(MDP),提出基于MCPG的全局智能规划方法.其次,将跟踪幕长内的每个时间步作为单独一幕来进行策略更新,提出基于MCPG目标跟踪中观测器轨迹的步进智能规划方法,并深入研究目标的跟踪估计特性,构造以跟踪性能优化为目的的奖励函数.最后,对最优非线性目标跟踪过程中基于强化学习的智能OTP决策仿真实验,表明了所提方法的有效性.

基于自适应KF的船载地波雷达目标跟踪方法

针对使用传统的卡尔曼滤波器进行多目标跟踪易发生状态估计不准确、航迹断裂的问题,提出了一种转换量测卡尔曼滤波增益自适应调整方法。结合船只目标的运动特性和历史船只航迹数据,采用线性最小二乘拟合提取出航迹参数的变化趋势。使用似然函数分别计算目标预测状态和量测状态与航迹的隶属度。利用预测状态和量测状态的隶属度值对滤波方式进行选择。利用仿真及实测船载地波雷达数据开展了目标跟踪实验,结果表明,使用该文方法能够得到平滑、持续的航迹输出,目标的跟踪距离远、跟踪精度高,优于现有的自适应滤波方法。

基于红外相机和毫米波雷达融合的烟雾遮挡无人驾驶车辆目标检测与跟踪

战场环境下无人驾驶车辆的感知系统易受烟雾、扬尘等天气的影响,对关键目标的检测与跟踪能力大大下降,造成目标漏检、目标误检、目标丢失等严重后果。针对该问题,开发毫米波雷达和红外相机融合系统,采用目标级融合方式建立简洁有效的融合规则,提炼和组合各传感器的优势信息,最终输出稳定的目标感知结果。对毫米波雷达的目标进行有效性检验和提取,并提出改进的基于密度的含噪声空间聚类应用算法,以减少毫米波雷达噪音干扰。以YOLOv4网络为基础,引入MobileNetv2主干网络,在网络训练过程中运用迁移学习方法,同时对红外数据样本进行扩充,解决了红外图像训练样本少的问题。试验结果表明,相较于仅基于红外相机的算法,融合检测算法在烟雾环境下的精度显著提升,且算法实时性高,实现了烟雾环境下毫米波雷达与红外相机融合的目标检测与跟踪,提高了无人驾驶车辆目标检测与跟踪系统的抗烟雾干扰能力。

基于功率谱流形的信息几何DP-TBD算法

针对复杂杂波背景下目标信杂比起伏而导致的漏检问题,本文结合信息几何检测器的性能优势与动态规划检测前跟踪(Dynamic Programming Track Before Detect,DP-TBD)的多帧信息积累能力,提出了基于功率谱流形的信息几何DP-TBD算法.该算法利用功率谱流形与矩阵流形对偶关系,设计了功率谱信息几何检测器,将信息几何检测器的计算复杂度降低了近两个数量级.通过实测数据实验验证,功率谱DP-TBD算法可实现与矩阵DP-TBD算法相近的检测性能,并将运算时间降低为矩阵DP-TBD算法的3%~8%.此外,相较于信息几何检测器,功率谱DP-TBD可将检测信杂比(Signal-to-Clutter Ratio,SCR)提高2~3 dB.

考虑综合性能最优的非短视快速天基雷达多目标跟踪资源调度算法

合理有效的资源调度是天基雷达效能得以充分发挥的关键。针对天基雷达多目标跟踪资源调度问题,建立了综合考虑目标威胁度、跟踪精度与低截获概率(LPI)的代价函数;考虑目标的不确定、天基平台约束以及长远期期望代价,建立了多约束下的基于部分可观测的马尔可夫决策过程(POMDP)的资源调度模型;采用拉格朗日松弛法将多约束下的多目标跟踪资源调度问题转换分解为多个无约束的子问题;针对连续状态空间、连续动作空间及连续观测空间引起的维数灾难问题,采用基于蒙特卡罗树搜索(MCTS)的在线POMDP算法—POMCPOW算法进行求解,最终提出了一种综合多指标性能的非短视快速天基雷达多目标跟踪资源调度算法。仿真表明,与已有调度算法相比,所提算法资源分配更合理,系统性能更优。

一次大暴雨中尺度短时强降水回波特征分析

为了做好江西宜丰大暴雨天气的预警预报服务,使用常规天气图、宜丰自动站雨量、江西雷达拼图等资料,采用天气学、雷达气象学等原理,对2022年6月3日江西宜丰地区大暴雨过程进行分析。结果表明,通过对6月3日宜丰大暴雨年月日统计特征进行分析,1981—2022年宜丰每年大暴雨出现频次在0~2次,主要集中出现在6月份。宜丰此次大暴雨过程前期雨势不大,但由于云系发展和地形、环境等条件的影响,在6月3日08时小时雨强达到了65 mm,到了09时小时雨强达到最大的70 mm,造成了局地的超短时强降水。受低槽及低层切变东移影响,边界层及地面增温明显,动力抬升加强。850 hPa低涡东侧的切变线一直延伸至赣北北部,切变线南侧西南急流达到14 m/s,宜丰处在西南急流的左前方,同时地面辐合线稳定在宜丰附近并与低层切变耦合,导致辐合扰动加强,有利于强风暴在辐合线附近发生。回波形态为絮状回波带结构,絮状回波带包含着多个较强单体回波,影响宜丰的组合反射率CR回波最强达到50 dBZ,≥45 dBZ回波稳定在宜丰南部,为短时强降水的生成形成了非常有利的条件,随着回波顶高的升高,加强了该单体回波的降水效率,08时出现了小时雨强65 mm/h的超强降水。