被动传感器系统的快速航迹起始算法

针对传统航迹起始算法在纯方位目标跟踪应用中存在的问题,提出了一种基于被动式单传感器系统的快速航迹起始算法.算法通过分析二维纯方位目标运动的特点设定威胁目标的筛选规则,利用改进的启发式算法在最短时间内对威胁目标进行舷迹起始.再将基于逻辑的方法应用到纯方位坐标系下,进行一般目标的航迹起始.通过这两种方法在纯方位目标跟踪系统中的联合运用,不仅使目标航迹得到有效确认,而且为系统建立目标威胁等级提供了基础.仿真和实际系统的应用表明了此算法的有效性和实用性.     

基于无偏转换的雷达配准算法

在多雷达数据处理系统中,雷达本身的系统偏差是影响目标跟踪和数据融合质量的一个重要因素.为了使融合结果更加准确可靠,本文提出了基于无偏转换的雷达配准算法.该算法是通过无偏转换把其它雷达的测量转换到主雷达下,利用各雷达相对主雷达的测量差值,采用Kalman滤波器实时估计出各雷达的系统偏差(方位和距离),从而进行配准.仿真实验结果表明这种算法是有效的.     

A self‐tuning control approach of mechanical manipulators

Abstract

The dynamic model of a manipulator system is a time‐varying highly nonlinear coupling equation set. When the moving speed increases or the payload, compared to its own weight, is no longer small, the performance of the conventional control schemes is not satisfactory for precision industrial application. Here a new adaptive control approach is developed for the manipulators to solve these problems. This algorithm directly uses a nonlinear dynamic model in the controller design to account for the nonlinear effects of the system. The least‐square time‐varying parameter identification scheme has been used to identify the change in configuration and payload. The simulation results show that this new approach has a very good trajectory tracking performance.     

基于R-FCN框架的多候选关联在线多目标跟踪

在线多目标跟踪是实时视频序列分析的重要前提。针对在线多目标跟踪中目标检测可靠性低、跟踪丢失较多、轨迹不平滑等问题,提出了基于R-FCN网络框架的多候选关联的在线多目标跟踪模型。首先,通过基于R-FCN网络从KF预测结果和检测结果中获取更可靠的候选框,然后利用Siamese网络进行基于外观特征的相似性度量,实现候选与轨迹之间的数据关联,最后通过RANSAC算法优化跟踪轨迹。在人流密集和目标被部分遮挡的复杂场景中,提出的算法具有较高的目标识别和跟踪能力,大幅减少漏检和误检现象,跟踪轨迹更加连续平滑。实验结果表明,在同等条件下,与当前已有的方法对比,本文提出在目标跟踪准确度(MOTA)、丢失轨迹数(ML)和误报次数(FN)等多个性能指标均有较大提升。     

Maneuvering target tracking in the presence of complicated measurement noises

Abstract

In modern radar tracking systems, measurement noise is significantly correlated when the measurement frequency is high enough. The problem of maneuvering target tracking in the presence of complicated measurement noise is considered in this paper. The measurement noise is modeled as the sum of a high‐order autoregressive process and a white process. This noise can be decorrelated by including the noise‐correlation variables in the target state. The theoretical analysis of tracking performance is derived. If some of the parameters (including the noise‐correlation parameters) are unknown, these unknown parameters can be estimated adaptively using a modified innovation correlation method. This parameter estimation method is very useful when the measurement noise can be modeled as the sum of a first‐order Markov process and a white process.     

Reconstructing the flight kinematics of swarming and mating in wild mosquitoes

We describe a novel tracking system for reconstructing three-dimensional tracks of individual mosquitoes in wild swarms and present the results of validating the system by filming swarms and mating events of the malaria mosquito Anopheles gambiae in Mali. The tracking system is designed to address noisy, low frame-rate (25 frames per second) video streams from a stereo camera system. Because flying A. gambiae move at 1–4 m s⁻¹, they appear as faded streaks in the images or sometimes do not appear at all. We provide an adaptive algorithm to search for missing streaks and a likelihood function that uses streak endpoints to extract velocity information. A modified multi-hypothesis tracker probabilistically addresses occlusions and a particle filter estimates the trajectories. The output of the tracking algorithm is a set of track segments with an average length of 0.6–1 s. The segments are verified and combined under human supervision to create individual tracks up to the duration of the video (90 s). We evaluate tracking performance using an established metric for multi-target tracking and validate the accuracy using independent stereo measurements of a single swarm. Three-dimensional reconstructions of A. gambiae swarming and mating events are presented.     

Detection for high resolution radars

Abstract

In this paper we discuss detection problems for a high resolution radar. Fluctuation in the target radar cross section usually decreases the probability of detection. However, through integration of cells within range profiles of a high resolution radar, variation of the integrated magnitude with respect to the change of carrier frequency and target aspect becomes much smaller, and this is helpful for improving the probability of detection. Two detection algorithms, the cell integration method and the correlation method, for a high resolution radar are proposed, and their detection performances are compared with that obtained by a conventional low resolution radar. Some theoretical formulations are developed. Simulation results show the effectiveness of the proposed algorithms.     

一种多目标自动跟踪的逻辑关联方法

目标跟踪是被动声纳系统的主要功能之一,跟踪器为目标运动分析和目标识别等后置处理提供输入数据。声纳操作员利用人脑独特的认知模式、先验知识和视觉的迹迹相关效应,能够很好地在声纳显示方位历程图上发现目标并录取跟踪。但实际应用中由于低信噪比、航迹交叉和转向等复杂多目标情形,声纳系统实现多目标自动跟踪十分困难。给出一种多目标自动跟踪的逻辑关联方法,利用被动声纳宽带波束输出数据,通过点迹与航迹关联、航迹评价管理和交叉处理等过程,可以较好实现多目标自动跟踪。试验数据的处理结果验证了所提方法的有效性,能够在声纳装备中得到工程应用。     

Multiple sequence alignment using modified dynamic programming and particle swarm optimization

Abstract

Dynamic Programming (DP) is widely used in Multiple Sequence Alignment (MSA) problems. However, when the number of the considered sequences is more than two, multiple dimensional DP may suffer from large storage and computational complexities. Often, progressive pairwise DP is employed for MSA. However, such an approach also suffers from local optimum problems. In this paper, we present a hybrid algorithm for MSA. The algorithm combines the pairwise DP and the particle swarm optimization (PSO) techniques to overcome the above drawbacks. In the algorithm, pairwise DP is used to align sequences progressively and PSO is employed to avoid the result of alignment being trapped into local optima. Several existing MSA tools are employed for comparison. The experimental results show excellent performance of the proposed algorithm.     

时间异步无线传感器网络的目标跟踪动态成簇算法

为了解决时间异步无线传感器网络在目标跟踪时的节点协作管理和跟踪时间配准问题,提出了一种适用于时间异步条件下目标跟踪的动态成簇算法。该方法通过分析目标的无线信号强度和各节点至目标的距离来动态组建跟踪簇,然后依据目标及簇头的通信距离对簇头射频信号的覆盖区域进行功能划分,实现节点对目标的协作跟踪,同时以簇为跟踪时间的计算单元,通过簇内计时和簇间贯序传递的方法实现跟踪时间的配准。仿真实验表明,该算法进行目标跟踪时能有效均衡网络能耗,且具有较好的跟踪精度和系统鲁棒性。     

Mathematical modelling of microsegregation in eutectic and peritectic binary alloys

Abstract

A mathematical model of microsegregation for eutectic and peritectic binary alloys was implemented using a finite volume method to solve the differential equations for mass transport. In this model simple ideas are used to handle phase boundaries and coarsening without the need to employ node jumping schemes or any transformation of variables to fix the domain size. Some model results were compared with available analytical solutions, revealing excellent agreement, which proved the approach useful to solve dissolution and diffusion coupled problems as well as microsegregation ones. Furthermore, good agreement was observed between the model results and measurements of eutectic volume fractions published previously for an Al–Cu alloy. The model was also capable of showing some important features of a typical peritectic transformation. Some instability was observed during model calculations, but it was easily handled by a time step refining technique.     

一种灰度成像扩展目标跟踪方法

灰度图像色彩信息贫乏会导致易陷于局部相似,使跟踪点发生漂移。针对此问题,提出了基于目标成像空间域与特征域分布的改进直方图模式扩展目标跟踪算法。以基于像素位置和像素灰度值的直方图模式作为目标模式;相似度测量采用基于Bhattacharyya系数定义的距离;用均值偏移迭代进行匹配区域搜索;通过建立卡尔曼预测跟踪策略解决目标被遮挡时的跟踪问题。试验结果表明,采用该算法,跟踪点漂移由原来十几个像素的波动减少到仅偶有1个像素的抖动。     

Experimental verification of a radar target discrimination technique using multiple‐frequency amplitude returns

Abstract

An experimental study of radar target discrimination using multiple‐frequency scattering amplitude without phase data is presented. Based on the concept of natural resonance frequencies, the technique is aspect‐angle independent so that a priori information of the aspect‐angle is not necessary. The radar cross sections of a spheroid and a thin wire are measured at different aspect ‐ and bistatic‐angles to distinguish different spheroids and wires in the resonance frequency range. The results show that the discrimination algorithm works well and can be applied to both monostatic and bistatic radars.     

Classification of primary radar tracks using gaussian mixture models

Classification of primary surveillance radar tracks as either aircraft or non-aircraft is critical to a number of emerging applications, including airspace situational awareness and collision avoidance. Substantial research has focused on target classification of pre-processed radar surveillance data. Unfortunately, many non-aircraft tracks still pass through the clutter-reduction processing built into the aviation surveillance radars used by the Federal Aviation Administration. This paper demonstrates an approach to radar track classification that uses only post-processed position reports and does not require features that are typically only available during the pre-processing stage. Gaussian mixture models learned from recorded data are shown to perform well without the use of features that have been traditionally used for target classification, such as radar crosssection measurements.     

集中交互式多传感器联合概率数据互联算法

为了解决杂波环境下多传感器多机动目标跟踪问题,本文提出了一种集中交互式多传感器联合概率数据互联算法。本文提出的算法首先应用广义S-D分配的规则对每个传感器送来的观测数据进行排列组合,并对所有的测量组合进行有效性判断,然后应用数据压缩的方法将每个有效量测组合压缩成一个等效量测点并根据每个等效量测点的联合似然函数计算其联合互联概率,最后在此基础上应用交互式多模型算法的思想以处理目标出现机动的问题。本文最后给出了该算法的分析,仿真结果表明,本文算法能够很好地解决杂波环境下多传感器多机动目标的跟踪问题。     

H 8 fuzzy control for time‐delay affine Takagi‐Sugeno fuzzy models: An ILMI approach

Abstract

This paper deals with the fuzzy based H₈ control problem for time‐delay affine Takagi‐Sugeno (T‐S) fuzzy models. A class of nonlinear time‐delay systems is approximated by a time‐delay affine T‐S fuzzy model in this paper. Based on Lyapunov‐Razumikhin theorem and S‐procedure, the stability and stabilization problems are solved by employing a Parallel Distributed Compensation (PDC) type H₈ fuzzy controller. The synthesis for the time‐delay affine T‐S fuzzy models is a Bilinear Matrix Inequality (BMI) problem and it can not be solved via a convex optimization algorithm. Hence, an Iterative Linear Matrix Inequality (ILMI) algorithm is used to solve the BMI problems in this paper. Finally, a numerical simulation for a delayed pendulum system is given to show the applications of the present approach.     

多坐标系下目标跟踪的联合滤波

针对雷达目标观测和处理在不同的坐标系下完成，本文提出了联合滤波算法来跟踪机动目标。该算法以卡尔曼滤波器为基础，直角坐标系下和极坐标系下的算法相联合，不仅克服了两种坐标系下滤波算法的不足，而且对机动目标有很好的跟踪效果。仿真实验结果表明了该算法的有效性。     

Cluster-based centralized data fusion for tracking maneuvering targets using interacting multiple model algorithm

The interacting multiple model (IMM) algorithm has proved to be useful in tracking maneuvering targets. Tracking accuracy can be further improved using data fusion. Tracking of multiple targets using multiple sensors and fusing them at a central site using centralized architecture involves communication of large volumes of measurements to a common site. This results in heavy processing requirement at the central site. Moreover, track updates have to be obtained in the fusion centre before the next measurement arrives. For solving this computational complexity, a cluster-based parallel processing solution is presented in this paper. In this scheme, measurements are sent to the data fusion centre where the measurements are partitioned and given to the slave processors in the cluster. The slave processors use the IMM algorithm to get accurate updates of the tracks. The master processor collects the updated tracks and performs data fusion using ‘weight decision approach’. The improvement in the computation time using clusters in the data fusion centre is presented in this paper     

基于YOLOv3和ASMS的目标跟踪算法

为了解决传统算法在全自动跟踪过程中遇到遮挡或运动速度过快时的目标丢失问题,本文提出一种基于YOLOv3和ASMS的目标跟踪算法。首先通过YOLOv3算法进行目标检测并确定跟踪的初始目标区域,然后基于ASMS算法进行跟踪,实时检测并判断目标跟踪效果,通过二次拟合定位和YOLOv3算法实现跟踪目标丢失后的重新定位。为了进一步提升算法运行效率,本文应用增量剪枝方法,对算法模型进行了压缩。通过与当前主流算法进行对比,实验结果表明,本算法能够很好地解决受到遮挡时跟踪目标的丢失问题,提高了目标检测和跟踪的精度,且具有计算复杂度低、耗时少,实时性高的优点。     

An algorithm for finger-vein segmentation based on modified repeated line tracking

Abstract

Image segmentation is an important step for finger-vein identification technique. However, it is difficult to extract precise details of the image because of the irregular noise and shades around the finger-vein. The repeated line tracking algorithm achieves good segmentation performance for low quality images of finger-vein, but it has some drawbacks such as low robustness and efficiency. In this paper, a modified repeated line tracking algorithm is proposed for image segmentation of finger-vein. Firstly, we propose a segmentation method called threshold image to execute rough segmentation and obtain binary and skeleton image of finger-vein. Secondly, the width of finger-vein is estimated based on the binary and skeleton image. The parameters are revised according to the width. Then, the modified repeated line tracking algorithm is executed to figure out the locus space of finger-vein based on the revised parameters. Finally, processing results are obtained by using Otsu algorithm which executes exact segmentation on the locus space. Experiments show that the proposed algorithm is more robust and efficient than traditional repeated line tracking algorithm.