Ground target tracking with variable structure IMM estimator

In this paper we present the design of a Variable Structure Interacting Multiple Model (VS-IMM) estimator for tracking groups of ground targets on constrained paths using Moving Target Indicator (MTI) reports obtained from an airborne sensor. The targets are moving along a highway, with varying obscuration due to changing terrain conditions. In addition, the roads can branch, merge or cross-the scenario represents target convoys along a realistic road network with junctions, changing terrains, etc. Some of the targets may also move in an open field. This constrained motion estimation problem is handled using an IMM estimator with varying mode sets depending on the topography, The number of models in the IMM estimator, their types and their parameters are modified adaptively, in real-time, based on the estimated position of the target and the corresponding road/visibility conditions. This topography-based variable structure mechanism eliminates the need for carrying all the possible models throughout the entire tracking period as in the standard IMM estimator, significantly improving performance and reducing computational load. Data association is handled using an assignment algorithm. The estimator is designed to handle a very large number of ground targets simultaneously. A simulated scenario consisting of over one hundred targets is used to illustrate the selection of design parameters and the operation of the tracker. Performance measures are presented to contrast the benefits of the VS-IMM estimator over the Kalman filter and the standard IMM estimator, The VS-IMM estimator is then combined with multidimensional assignment to gain “time-depth.” The additional benefit of using higher dimensional assignment algorithms for data association is also evaluated     

Data fusion for ground moving target tracking

The aim of ground surveillance is the large scale, continuous and near real time determination of a dynamical ground picture. This task comprises detection and tracking of moving single targets and convoys, mobile weapon systems, and military equipment. The sensors of choice are airborne Ground Moving Target Indicator (GMTI) radar and synthetic aperture radar (SAR). As ground target tracking often suffers from dense target situations, high clutter, and low visibility, the integration and fusion of external background information is essential for providing precise and continuous tracks. We present Multi Hypotheses techniques for tracking several targets in complex ground situations with clutter. Methods to incorporate topographic information, in particular digital road maps, are described and demonstrated.     

Application of Road Information in Ground Moving Target Tracking

A new algorithm is developed to achieve accurate state estimation in ground moving target tracking by means of using road information. It is an adaptive variable structure interacting multiple model estimator with dynamic models modification （DMM VS-IMM for short）. Firstly, road information is employed to modify the target dynamic models used by filter, including modification of state transition matrix and process noise. Secondly, road information is applied to update the model set of a VS-IMM estimator. Predicted state estimation and road information are used to locate the target in the road network on which the model set is updated and finally IMM filtering is implemented. As compared with traditional methods, the accuracy of state estimation is improved for target moving not only on a single road, but also through an intersection. Monte Carlo simulation demonstrates the efficiency and robustness of the proposed algorithm with moderate computational loads.     

Road-map assisted ground moving target tracking

Tracking ground targets with airborne GMTI (ground moving target indicator) sensor measurements proves to be a challenging task due to high target density, high clutter, and low visibility. The exploitation of nonstandard background information such as road maps and terrain information is therefore highly desirable for the enhancement of track quality and track continuity. The present paper presents a Bayesian approach to incorporate such information consistently. It is particularly suited to deal with winding roads and networks of roads. The target dynamics is modeled in quasi one-dimensional road coordinates and mapped onto ground coordinates using linear road segments taking road map errors into account. The case of several intersecting roads with different characteristics, such as mean curvature, slope, or visibility, is treated within an interacting multiple model (IMM) scheme. Targets can be masked both by the clutter notch of the sensor and by terrain obstacles. Both effects are modeled using a sensor-target state dependent detection probability. The iterative filter equations are formulated within a framework of Gaussian sum approximations on the one hand and a particle filter approach on the other hand. Simulation results for single targets taken from a realistic ground scenario show strongly reduced target location errors compared with the case of neglecting road-map information. By modeling the clutter notch of the GMTI sensor, early detection of stopping targets is demonstrated     

一种无人机定距盘旋跟踪制导律及稳定性证明

对地面目标的定距盘旋跟踪问题是无人机（UAVs）在任务应用阶段需要面临的重要问题之一,如何在传感器信息受限的情况下完成跟踪任务是目前的研究热点。首先针对地面固定目标设计了一种仅依赖测距传感器的制导律,不再需要传统的视线角信号以及目标和无人机自身的定位信息;其次,设计了李雅普诺夫函数对该制导律的稳定性进行了严格数学证明;最后,将该制导律推广到对地面匀速和变速移动目标的跟踪制导。相比于现有制导律,所提出的制导律结构更为简洁,仅有一个设计参数,并且制导策略更为合理。仿真结果表明所提出的制导律能够实现无人机对地面固定和移动目标的稳定跟踪。     

Tracking with classification-aided multiframe data association

In most conventional tracking systems, only the target kinematic information from, for example, a radar or sonar or an electro-optical sensor, is used in measurement-to-track association. Target class information, which is typically used in postprocessing, can also be used to improve data association to give better tracking accuracy. The use of target class information in data association can improve discrimination by yielding purer tracks and preserving their continuity. In this paper, we present the simultaneous use of target classification information and target kinematic information for target tracking. The approach presented integrates target class information into the data association process using the 2-D (one track list and one measurement list) as well as multiframe (one track list and multiple measurement lists) assignments. The multiframe association likelihood is developed to include the classification results based on the "confusion matrix" that specifies the accuracy of the target classifier. The objective is to improve association results using class information when the kinematic likelihoods are similar for different targets, i.e., there is ambiguity in using kinematic information alone. Performance comparisons with and without the use of class information in data association are presented on a ground target tracking problem. Simulation results quantify the benefits of classification-aided data association for improved target tracking, especially in the presence of association uncertainty in the kinematic measurements. Also, the benefit of 5-D (or multiframe) association versus 2-D association is investigated for different quality classifiers. The main contribution of this paper is the development of the methodology to incorporate exactly the classification information into multidimensional (multiframe) association.     

基于神经网络的机动目标信息融合与并行自适应跟踪

基于“当前”统计模型和 BP神经网络 ,提出一种新的机动目标神经网络信息融合与并行自适应跟踪算法 ( NIFPAT)。该算法采用双滤波器并行结构 ,利用全状态反馈 ,通过 BP网络调整系统方差以适应目标的运动变化 ,具有对目标各种运动状态的良好自适应跟踪能力     

Beam resource scheduling for a VHF-MIMO radar network in low-angle tracking

The low-angle tracking in multipath interference is a challenging problem for the Very High Frequency (VHF) radar. The colocated Multi-Input Multi-Output (MIMO) technique can remedy such a defect. In this paper, a Joint Beam-Target Assignment and Power Allocation (JBTAPA) strategy is proposed for the VHF-MIMO radar network tracking low-angle targets. The core of the JBTAPA strategy is to improve the worst tracking accuracy among multiple targets by assigning appropriate beams to targets and allocating the power resource in each beam using the feedback information in the tracking cycle. Taking into account the transmit multipath and receive multipath, we derive the Cramér-Rao Lower Bound (CRLB) on angle estimate, which is then incorporated in the Predicted Conditional CRLB (PC-CRLB). A more accurate and consistent lower bound is provided as the optimization metric since the PC-CRLB is based on the most recently realized measurements. A two-stage-based technique is proposed to solve the JBTAPA problem, which is originally NP-hard. Simulation results verify the effectiveness and efficiency of the proposed method. The results also imply that the target reflectivity plays one of the important roles in resource allocation.     

基于注意力机制和特征匹配的空对地多目标检测与跟踪算法

多目标跟踪算法是实现无人机自主导航的关键技术，为解决现有方法存在的小目标检测能力弱、计算能耗大、鲁棒性差等问题，提出一种基于注意力机制和特征匹配的多目标空对地跟踪算法，以实现航拍视角下对目标的精准高效跟踪。首先，引入通道可分离卷积，实现目标检测模型的轻量化；其次，构造融合空间注意力机制的小目标检测分支，提高对小微目标的检测精度，最后，优化目标跟踪算法的外观重识别网络，提高多目标跟踪效率。使用Visdrone2019-MOT数据集对所提算法进行验证，实验结果表明，所提算法的MOTA值提高了0.６%，FPS值为21.31帧/s，在模型大小和跟踪精度上实现了较好的平衡。     

Track labeling and PHD filter for multitarget tracking

Multiple target tracking requires data association that operates in conjunction with filtering. When multiple targets are closely spaced, the conventional approaches (as, e.g., MHT/assignment) may not give satisfactory results. This is mainly because of the difficulty in deciding what the number of targets is. Recently, the probability hypothesis density (PHD) filter has been proposed and particle filtering techniques have been developed to implement the PHD filter. In the particle PHD filter, the track labeling problem is not considered, i.e., the PHD is obtained only for a frame at a time, and it is very difficult to perform the multipeak extraction, particularly in high clutter environments. A track labeling method combined with the PHD approach, as well as considering the finite resolution, is proposed here for multitarget tracking, i.e., we keep a separate tracker for each target, use the PHD in the resolution cell to get the estimated number and locations of the targets at each time step, and then perform the track labeling ("peak-to-track" association), whose results can provide information for PHD peak extraction at the next time step. Besides, by keeping a separate tracker for each target, our approach provides more information than the standard particle PHD filter. For example, in group target tracking, if we are interested in the motion of a specific target, we can track this target, which is not possible for the standard particle PHD filter, since the standard particle PHD filter does not keep track labels. Using our approach, multitarget tracking can be performed with automatic track initiation, maintenance, spawning, merging, and termination     

Use of map information for tracking targets on airport surface

A generic and novel approach for integrating airport map information with sensor measurements in the track estimation process is proposed and evaluated. The surface restrictions imposed by the network of roads, taxiways, and runways, represented by a simplified geometric model, are included in both the target observation and the dynamic models, to derive the target state estimates. The performance of the methods proposed is illustrated in representative airport surface scenarios, taking as a reference for comparison other tracking alternatives such as VS-IMM (variable structure interacting multiple model estimator) ground target tracking, or standard ones that do not make use of ground information.     

防空导弹攻击目标的红外成像特性分析

通过结合对目标的红外成像特性研究,首先阐述了在复杂背景条件下防空导弹对目标的红外成像寻的制导的组成和原理。然后详细分析了地面杂散背景的辐射和散射特征,综合分析了军用飞机被防空导弹攻击目标的红外辐射特性。在对复杂背景辐射特征和目标红外特性分析的基础上,给出了一组典型地表光谱辐射亮度曲线和太阳对地表的全波段辐射能流(辐照度)随时间的变化曲线;绘制出了红外成像导引头在整个红外波段内的探测效能曲线。分析结果对研究防空导弹红外成像导引头如何从复杂背景中将目标的典型特征信息提取出来并进行有效识别和跟踪奠定了基础。     

一种基于修正Hough变换的航迹起始算法

为了在重杂波区内检测出运动的目标,提出了一种修正的Hough变换算法用于初始航迹的建立。与传统的算法相比,修正算法充分利用目标的运动学信息,选取了更为合适的变换参量,仅利用较少拍数的量测就可以完成起始,能够在检测概率较高的环境下具有良好的起始性能。计算机仿真结果表明,算法能够克服传统算法的多拍扫描,大大缩短实际的计算量和起始时间,实现对航迹起始的在线改进。     

Automatic target recognition using sequences of high resolutionradar range-profiles

In this paper, we address the problem of joint tracking and recognition of a target using a sequence of high resolution radar (HRR) range-profiles. The likelihood function for the scene configuration combines a dynamics-based prior on the sequence of target orientations with a likelihood for range-profiles given the target orientation. A deterministic model and a conditionally Gaussian model for range-profiles are introduced, and the likelihood functions under each model are compared. Simulations are presented demonstrating recognition of mobile aircraft and ground targets, and results showing performance of the algorithm are given in terms of the expected angular estimation error and the rate of correct recognition     

基于人工神经网络的多模型目标跟踪算法

针对在目标跟踪中单模型跟踪算法难以应对目标运动形式的变化,而多模型跟踪算法存在结构固定、跟踪精度被非匹配模型削弱且模型切换缓慢的矛盾,文章提出了一种基于人工神经网络的多模型目标跟踪算法。通过分析目标几种基本运动模式的轨迹特点,归纳出目标运动轨迹的特征向量。利用训练好的BP神经网络对滑窗里的轨迹段进行运动模型识别,按结果进行跟踪模型切换,达到使跟踪算法实时适应目标运动状态的目的。仿真结果证明了该算法的有效性,且与传统的多模型算法相比,具有结构更加简单、更强的灵活性和拓展性的特点。     

基于Kohonen网络的动目标实时定位融合算法

为完成对动目标的实时定位测量,有效减小测控装备组网测量实时跟踪定位误差,在建立多定位子系统组网混合式融合结构模型的基础上,利用Kohonen网络的自组织特征映射模型特点,确定各组交会数据的权值,并将其输入1个Kohonen网络进行在线竞争、融合求得目标的位置信息,增强了目标实时位置信息的连续性、完整性,减小了实时定位误差。     

机载雷达目标跟踪与航迹信息提取

针对机动目标跟踪中航迹信息提取精度不高的问题,提出一种ECEF坐标系下基于交互多模型的多机协同跟踪算法。首先,各载机以ECEF坐标系为融合中心对目标量测进行无偏转换处理,以有效减小量测转换误差对目标跟踪的影响;然后,利用交互多模型的方法对目标进行融合跟踪,以进一步提高目标机动时的跟踪精度;最后,通过二次滤波的方法,来有效实现目标航迹信息的精确提取。仿真结果表明,该算法可较好地提高目标机动时的跟踪精度和航迹信息提取精度。     

Data association for deghosting in Y-shaped passive linear array sonars

This paper deals with data association using three sets of passive linear array sonars (PLAS) geometrically positioned in a Y-shaped configuration, fixed in an underwater environment. The data association problem is directly transformed into a 3D assignment, which is known to be NP hard. For generic passive sensors, it can be solved using conventional algorithms, while in PLAS, it becomes a formidable task due to the presence of bearing ambiguity. Thus, the central issue of the problem in PLAS is how to eliminate the bearing ambiguity without increasing tracking error. To solve this problem, the 3D assignment algorithm used the likelihood value of only those observed bearing measurements is modified by incorporating frequency information in consecutive time-aligned scans. The region of possible ghost targets is first established by the geometrical relation of PLAS with respect to target. The ghost targets are then confirmed and eliminated by generating multiple observations in consecutive scans. Representative simulations demonstrate the effectiveness of the proposed approach.     

A Gaussian Mixture PHD Filter for Jump Markov System Models

The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. The PHD filter admits a closed-form solution for a linear Gaussian multi-target model. However, this model is not general enough to accommodate maneuvering targets that switch between several models. In this paper, we generalize the notion of linear jump Markov systems to the multiple target case to accommodate births, deaths, and switching dynamics. We then derive a closed-form solution to the PHD recursion for the proposed linear Gaussian jump Markov multi-target model. Based on this an efficient method for tracking multiple maneuvering targets that switch between a set of linear Gaussian models is developed. An analytic implementation of the PHD filter using statistical linear regression technique is also proposed for targets that switch between a set of nonlinear models. We demonstrate through simulations that the proposed PHD filters are effective in tracking multiple maneuvering targets.     

基于RBF网络的跟踪特性角对接收信号影响模型研究

在航天发射任务中,地面遥外测设备跟踪特性角（α角、β角）对接收信号质量的影响非常大,由于α角、β角影响接收信号的机理非常复杂,一直以来只进行定性分析.在研究RBF（Rradial Basis Function,径向基函数）网络的基础上,利用RBF网络建立跟踪α角、β角对接收信号的影响模型,并用已知样本数据对模型进行训练和验证,详细论证模型参数的选取方法,给出跟踪特性角对接收电平影响的量化分析和预测的一种方法.利用模型对某次发射任务设备的接收电平进行了预测,预测结果与实测结果基本相符,满足应用要求,对提高航天发射任务地面测控系统风险评估能力有重要意义.