Sequential track extraction

Sensors like radar or sonar usually produce data on the basis of a single frame of observation: target detections. The detection performance is described by quantities like detection probability Pd and false alarm density f. A different task of detection is formation of tracks of targets unknown in number from data of multiple consecutive frames of observation. This leads to quantities which are of a higher level of abstraction: extracted tracks. This again is a detection process. Under benign conditions (high Pd, low f and well separated targets) conventional methods of track initiation are recommended to solve a simple task. However, under hard conditions the process of track extraction is known to be difficult. We here concentrate on the case of well separated targets and derive an optimal combinatorial method which can be used under hard operating conditions. The method relates to MHT (multiple hypothesis tracking), uses a sequential likelihood ratio test and derives benefit from processing signal strength information. The performance of the track extraction method is described by parameters such as detection probability and false detection rate on track level, while Pd and f are input parameters which relate to the signal-to-noise interference ratio (SNIR), the clutter density, and the threshold set for target detection. In particular the average test lengths are analyzed parametrically as they are relevant for a user to estimate the time delay for track formation under hard conditions     

一种基于新三维随机Hough变换的航迹起始算法

针对雷达跟踪多目标时,目标点迹受杂波、噪声等因素影响,航迹起始难度大的问题,利用三维空间直线表示方法提出了一种4参数三维Hough变换算法.该算法是对传统二维Hough变换的拓展,它结合了传统的二维随机Hough变换理论,是一种新的三维随机Hough变换算法.通过该算法对理论数据和实测数据进行验证,结果表明,对于航迹区别较大的目标,该算法的航迹起始成功概率为98.5％;对于空间航迹相近的目标,该算法可成功将目标航迹从杂波中提取出来,虽然可能会出现航迹混淆,但利用目标先验信息可解决该问题,实现航迹起始.     

Multi-Target Tracking in Clutter without Measurement Assignment

When tracking targets using radars and sonars, the number of targets and the origin of data is uncertain. Data may be false measurements or clutter, or they may be detections from an unknown number of targets whose possible trajectories and detection processes can only be described in a statistical manner. Optimal all-neighbor multi-target tracking (MTT) in clutter enumerates all possible joint measurement-to-track assignments and calculates the a posteriori probabilities of each of these joint assignments. The numerical complexity of this process is combinatorial in the number of tracks and the number of measurements. One of the key differences between most MTT algorithms is the manner in which they reduce the computational complexity of the joint measurement-to-track assignment process. We propose an alternative approach, using a form of soft assignment, that enables us to bypass this step entirely. Specifically, our approach treats possible detections of targets followed by other tracks as additional clutter measurements. It starts by approximating the a~priori probabilities of measurement origin. These probabilities are then used to modify the clutter spatial density at the location of the measurements. A suitable single target tracking (STT) filter then uses the modified clutter intensity for updating the track state. In effect, the STT filter is transformed into an MTT filter with a numerical complexity that is linear in the number of tracks and the number of measurements. Simulations show the effectiveness of this approach in a number of different multi-target scenarios.     

Track monitoring when tracking with multiple 2D passive sensors

A fast method of track monitoring is presented which determines what tracks are good and what tracks have had data association problems and should be eliminated. The philosophy of tracking in a dense target environment with limited central processing unit (CPU) time is to acquire the targets, track them with as simple a filter as will meet requirements, and monitor the tracks to determine if they are still tracking a target or are tracking incorrect returns and should be terminated. After termination the true targets are reacquired. However, it is difficult to determine from simple track monitoring the correct interpretation of a poor track. Poor tracks can be a result of a sensor failure, target maneuver, or incorrect data association. The author describes track monitoring and provides a solution to this dilemma when tracking with multiple two-dimensional passive sensors. The method is much faster than other monitoring methods.<>     

Bayesian tracking of two possibly unresolved maneuvering targets

The paper studies the problem of maintaining tracks of two targets that may maneuver in and out formation flight, whereas the sensor and measurement extraction chain produces false and possibly unresolved or missing measurements. If the possibility of unresolved measurements is not modelled then it is quite likely that either the two tracks coalesce or that one of the two tracks diverges on false measurements. In literature a robust measurement resolution model has been incorporated within an interacting multiple model/multiple hypothesis tracking (IMM/MHT) track maintenance setting. A straightforward incorporation of the same model within an IMM and probabilistic data association (PDA)-like hypothesis merging approach suffers from track coalescence. In order to improve this situation, the paper develops a track-coalescence avoiding hypotheses merging version for the two target problem considered. Through Monte Carlo simulations, the novel filters are compared with applying hypotheses merging approaches that ignore the possibility of unresolved measurements or track-coalescence.     

Tracking in Clutter using IMM-IPDA?Based Algorithms

We describe three single-scan probabilistic data association (PDA) based algorithms for tracking manoeuvering targets in clutter. These algorithms are derived by integrating the interacting multiple model (IMM) estimation algorithm with the PDA approximation. Each IMM model a posteriori state estimate probability density function (pdf) is approximated by a single Gaussian pdf. Each algorithm recursively updates the probability of target existence, in the manner of integrated PDA (IPDA). The probability of target existence is a track quality measure, which can be used for false track discrimination. The first algorithm presented, IMM-IPDA, is a single target tracking algorithm. Two multitarget tracking algorithms are also presented. The IMM-JIPDA algorithm calculates a posteriori probabilities of all measurement to track allocations, in the manner of the joint IPDA (JIPDA). The number of measurement to track allocations grows exponentially with the number of shared measurements and the number of tracks which share the measurements. Therefore, IMM-JIPDA can only be used in situations with a small number of crossing targets and low clutter measurement density. The linear multitarget IMM-IPDA (IMM-LMIPDA) is also a multitarget tracking algorithm, which achieves the multitarget capabilities by integrating linear multitarget (LM) method with IMM-IPDA. When updating one track using the LM method, the other tracks modulate the clutter measurement density and are subsequently ignored. In this fashion, LM achieves multitarget capabilities using the number of operations which are linear in the: number of measurements and the number of tracks, and can be used in complex scenarios, with dense clutter and a large number of targets.     

空基多雷达航迹抗差关联算法

基于高斯随机矢量统计特性,推导出一种适用于探测距离较远、系统误差时变、雷达上报目标不一致等复杂环境的空基多雷达航迹抗差关联算法。分解航迹距离矢量,对消系统误差矢量得出适用于3个及3个以上雷达的航迹抗差关联条件和流程。分别设置了目标密集程度、随机误差和系统误差适应性实验验证算法性能。仿真结果表明所提算法的关联准确性和复杂环境适应性相比现有的基于参照拓扑特征的航迹关联算法（RET算法）和基于距离检测的可信关联算法（confidential算法）有较大幅度的提升。     

Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter

The Gaussian mixture probability hypothesis density (GM-PHD) recursion is a closed-form solution to the probability hypothesis density (PHD) recursion, which was proposed for jointly estimating the time-varying number of targets and their states from a sequence of noisy measurement sets in the presence of data association uncertainty, clutter, and miss-detection. However the GM-PHD filter does not provide identities of individual target state estimates, that are needed to construct tracks of individual targets. In this paper, we propose a new multi-target tracker based on the GM-PHD filter, which gives the association amongst state estimates of targets over time and provides track labels. Various issues regarding initiating, propagating and terminating tracks are discussed. Furthermore, we also propose a technique for resolving identities of targets in close proximity, which the PHD filter is unable to do on its own.     

Multi-Target/Multi-Sensor Tracking using Only Range and Doppler Measurements

A new approach is described for combining range and Doppler data from multiple radar platforms to perform multi-target detection and tracking. In particular, azimuthal measurements are assumed to be either coarse or unavailable, so that multiple sensors are required to triangulate target tracks using range and Doppler measurements only. Increasing the number of sensors can cause data association by conventional means to become impractical due to combinatorial complexity, i.e., an exponential increase in the number of mappings between signatures and target models. When the azimuthal resolution is coarse, this problem will be exacerbated by the resulting overlap between signatures from multiple targets and clutter. In the new approach, the data association is performed probabilistically, using a variation of expectation-maximization (EM). Combinatorial complexity is avoided by performing an efficient optimization in the space of all target tracks and mappings between tracks and data. The full, multi-sensor, version of the algorithm is tested on simulated data. The results demonstrate that accurate tracks can be estimated by exploiting spatial diversity in the sensor locations. Also, as a proof-of-concept, a simplified, single-sensor range-only version of the algorithm is tested on experimental radar data acquired with a stretch radar receiver. These results are promising, and demonstrate robustness in the presence of nonhomogeneous clutter.     

基于序贯关联算法的多目标无源跟踪

基于序贯关联算法,对多目标无源跟踪问题进行了研究。在只有角度信息可以利用的情况下,首先,利用波门技术对各个无源传感器角度测量数据进行关联和滤波,形成参数航迹;然后,将各个无源传感器的参数航迹送到融合中心进行关联配对,并在关联过程中通过构造关联质量函数对参数航迹的关联历史情况进行度量,解决参数航迹关联模糊问题;最后,通过对关联成功的参数航迹进行交叉定位,给出多个不同目标的位置信息,实现分布式无源系统对多目标的数据关联和跟踪,并通过仿真分析,对算法的有效性和可行性进行验证。     

非合作目标动态RCS仿真方法

针对非合作目标难以开展动态测量的问题,根据空气动力学原理提出了一种非合作目标动态雷达散射截面（RCS）仿真方法。该方法首先建立测量背景下典型飞行航路模型,然后计算雷达视线在机体坐标系上的时变姿态角。根据姿态角开展电磁计算,获得F-117A隐身攻击机在侧站平飞、背站拉起、对站俯冲、侧站盘旋4种航路下的动态RCS数据。着重分析了动、静态RCS特性在起伏目标检测性能评估上的差异。结果表明：静态RCS特性难以反映目标运动时真实的雷达特性,利用静态数据描述目标特性可能导致错误结论,而文中方法获取的动态RCS数据可以提高结论的完整性和可信度。     

基于高斯混合模型的航迹抗差关联算法

针对雷达系统误差时变、上报目标不完全一致等复杂场景下目标航迹关联问题,采用高斯混合模型（GMM）与航迹间拓扑信息相结合的方法实现航迹抗差关联。将航迹关联问题转化为图像匹配中的非刚性点集匹配问题,建立对非同源航迹具有鲁棒性的高斯混合模型,根据航迹间的邻域拓扑信息决定高斯混合模型中各高斯组成部分的权重,利用期望最大值（EM）算法求解高斯混合模型的最优闭合解,在期望步（E-step）阶段求解航迹的对应关系,在最大化步（M-step）阶段求解非同源航迹比例,最后进行航迹关联判决以获得关联结果。仿真结果表明,该算法在不同系统误差、目标分布密度、探测概率等环境下具有较好有效性和鲁棒性。     

Track Biases and Coalescence with Probabilistic Data Association

It is shown that probabilistic data association yields biased tracks in a multitarget environment, and the magnitude of the bias is investigated. A critical target separation distance exists, within which the tracks tend to coalesce completely. The problem of track initiation on new targets is also discussed.     

A multi-frame track-before-detect algorithm based on root label clustering for multiple targets

In this paper, a novel multi-frame track-before-detect algorithm is proposed, which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase. A criterion of track extrapolation is used to construct state transition set, root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space, then measurement plots of multi-frame are divided into sever...     

基于傅里叶变换的航迹对准关联算法

研究了在组网雷达存在系统误差情况下的目标航迹关联问题,理论分析了雷达系统误差对目标航迹的影响,并将该影响表示为目标航迹的旋转和平移量。在此基础上,提出了一种基于傅里叶变换的系统误差配准前航迹对准关联算法,该算法将组网雷达的航迹数据看做为一种整体信息,采用傅里叶变换理论来估计和补偿组网雷达目标航迹数据到融合中心航迹数据的相对旋转量和平移量,将雷达网中雷达上报的目标航迹数据对准到融合中心,从而不依赖于估计雷达网系统误差,实现了误差配准前的航迹准确关联,能够为后端的系统误差配准提供可靠的关联目标航迹数据。     

Detection of weak, subpixel targets using mesh-connected cellularautomata

Effective use of military cellular automata such as military data array processor (MilDAP) and geometric arithmetic parallel processor (GAPP), in weak, subpixel target detection is shown to be possible by using new signal processing regimes based on binary ranking filter theory. By using binary ranking filters, the MilDAP can furnish 6 dB of processing gain against white Gaussian noise while monitoring from one to four million potential target tracks at 10-40 frames/s. GAPP is shown to be capable of monitoring 3.7 million tracks over 216×384 detectors at 14000 frames/s and, in a time sharing mode, 15 million tracks over 432×768 detectors at 24 frames/s. The special case of threatening targets is discussed, as well as alternate cellular architectures which use multidimensional binary ranking filters in multidimensional coordinate systems     

Association of tracks from over the horizon radar

In Over-The-Horizon Radar (OTHR), multiple ionospheric layers cause several tracks per target to be observed. This effect causes ambiguities in target identification and track coordinate registration. A pattern classification approach is proposed for associating tracks. Neural networks and statistical methods are applied to combine track affinities and associate pairs of tracks. The proposed approach provides a practical and efficient way of dealing with multiple track association when the number of targets is large and optimal. Bayesian hypothesis testing is not practical     

Multiple hypothesis track confirmation for infrared surveillancesystems

An overall methodology is described for the application of a multiple hypothesis tracking (MHT) algorithm to the infrared (IR) surveillance system problem of establishing tracks on dim targets in a heavy clutter or false alarm background. The authors discuss the manner in which the detection and tracking systems are jointly designed to optimize performance. They present approximate methods that can conveniently be used for preliminary system design and performance prediction. They discuss the use of a detailed Monte Carlo simulation for final system evaluation and present results illustrating the proposed methods and comparing predicted and simulation performance     

Multiple hypothesis track maintenance with possibly unresolved measurements

In surveillance problems dense clutter/dense target situations call for refined data association and tracking techniques. In addition, closely spaced targets may exist which are not resolved. This phenomenon has to be considered explicitly in the tracking algorithm. We concentrate on two targets which temporarily move in close formation and derive a generalization of MHT methods on the basis of a simple resolution model.