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。     

Tracker design based on target perceivability

Theoretical design of a tracker with respect to the so-called target perceivability is presented. Basic rules and analytic formulas for the determination of tracker parameters are presented for making better tracking decisions (i.e., track initiation, confirmation, and termination) toward enhancing tracking performance. Simulation results are provided that support the theoretical design and demonstrate the enhancement of the tracker in tracking performance     

纯方位二维目标跟踪的航迹起始算法

 针对传统航迹起始算法在纯方位目标定位和跟踪系统应用中存在的弊端,提出了一种完全基于角度量测的快速航迹起始算法。该方法通过深入分析目标在角度坐标下的运动特性,给出了全新的关联门构造方法。该波门技术有效提高了纯方位二维目标真实量测的确认效率,极大限制了虚假航迹随密集杂波的扩张。利用此波门,通过基于逻辑的方法对仅有角度量测的目标航迹进行扩展。该方法有效地解决了角度坐标系下机动目标的航迹起始分辨率下降的问题,为基于单个被动传感器纯方位跟踪系统进行快速、准确的航迹起始提供了新的思路。仿真结果及实际应用表明了此算法的有效性和实用性。     

基于相对位置矢量的群目标灰色精细航迹起始算法

为解决群内目标精细航迹起始的难题,基于对传统航迹起始算法及现有群目标航迹起始算法优缺点的分析,给出了完整的群目标航迹起始框架,并提出了一种基于相对位置矢量的群目标灰色精细航迹起始算法。首先基于循环阈值模型、群中心点进行群的预分割、预关联,然后对预关联成功的群搜索对应坐标系,建立群中各量测的相对位置矢量,基于灰色精细互联模型完成群内量测的互联,最后基于航迹确认规则得到群目标状态矩阵。经仿真数据验证,与修正的逻辑法、基于聚类和Hough变换的多编队航迹起始算法相比,该算法在起始真实航迹、抑制虚假航迹及杂波鲁棒性等方面综合性能更优。     

Diffusion analysis of track loss in clutter

In tracking a target through clutter, the selection of incorrect measurements for track updating causes track divergence and eventual loss of track. The plot-to-track association algorithm is modeled as a Markov process and the tracking error is modeled as a diffusion process in order to study the mechanism of track loss analytically, without recourse to Monte Carlo simulations, for nearest-neighbor association in two space dimensions. The time evolution of the error distribution is examined, and the connection of the approach with diffusion theory is discussed. Explicit results showing the dependence of various performance parameters, such as mean time to lose track and track half-life, on the clutter spatial density are presented. The results indicate the existence of a critical density region in which the tracking performance degrades rapidly with increasing clutter density. An optimal gain adaptation procedure that significantly improves the tracking performance in the critical region is proposed     

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     

基于相位相关的部分可辨编队精细起始算法

针对部分可辨条件下编队目标的精细起始难题,提出了一种基于相位相关的部分可辨编队精细起始算法。首先,采用基于坐标映射距离差分的快速群分割与基于编队中心点的预互联对雷达量测进行预处理;然后,利用图像匹配中相位相关特性,将相邻时刻编队结构进行补偿对准,解决了低目标发现概率情况下的编队结构对准问题;最后,采用增加虚拟量测并后验判决的方式,结合最近邻法做编队航迹精细互联,在填补航迹缺失、增加正确航迹的同时抑制虚假航迹的产生。经仿真验证,与修正的逻辑法、基于相对位置矢量的灰色编队精细起始算法相比,本文所提算法在提高航迹正确起始率、抑制虚假航迹方面性能优势显著,且对环境杂波与雷达精度具有较好的鲁棒性,对目标发现概率具有较好的适应性。     

SMALL TARGET TRACKING TECHNIQUE WITH DATA FUSION OF DISTRIBUTED SENSOR NET

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基于运动状态的航迹起始算法研究

 针对传统的基于逻辑的航迹起始方法在量测扩展过程中存在的弊端,提出了基于目标运动状态的航迹起始算法,并给出了更为精确的起始波门构造方法。利用目标的位置信息形成候选目标航迹,在候选目标航迹扩展过程中,采用提出的修正Hough变换提取目标状态信息,并根据目标〖JP2〗的状态信息对候选航迹进行检验。仿真结果表明,该算法比其他基于逻辑的方法有更低的虚警概率,并且对存储空间要求低,适于工程应用。     

Architectures and algorithms for track association and fusion

Target tracking using multiple sensors can provide better performance than using a single sensor. One approach to multiple target tracking with multiple sensors is to first perform single sensor tracking and then fuse the tracks from the different sensors. Two processing architectures for track fusion are presented: sensor to sensor track fusion, and sensor to system track fusion. Technical issues related to the statistical correlation between track estimation errors are discussed. Approaches for associating the tracks and combining the track state estimates of associated tracks that account for this correlation are described and compared by both theoretical analysis and Monte Carlo simulations     

发动机性能量与输入量的相关性研究

展开了相关性研究，讨论了一种相关性分析的算法，并在此基础上进行了大量的数字仿真实验，结果表明通过相关性分析不但可减少计算工作量，而且即使在传感器有限的情况下，模型也可很好的跟踪发动机，两者的匹配效果较好     

Aided Tracking as Applied to High Accuracy Pointing Systems

Two basic concepts of rate aided tracking and position aided tracking are applied to a conventional pointing system in order to improve performance. The aided track signals are derived in an inertial space format and are generated from a Kalman filter algorithm. Computational results are included to show the interplay between the conventional pointing system and the aided track filter.     

On joint track initiation and parameter estimation under measurement origin uncertainty

The problem of joint detection and estimation for track initiation under measurement origin uncertainty is studied. The two well-known approaches, namely the maximum likelihood estimator with probabilistic data association (ML-PDA) and the multiple hypotheses tracking (MHT) via multiframe assignment, are characterized as special cases of the generalized likelihood ratio test (GLRT) and their performance limits indicated. A new detection scheme based on the optimal gating is proposed and the associated parameter estimation scheme modified within the ML-PDA framework. A simplified example shows the effectiveness of the new algorithm in detection performance under heavy clutter. Extension of the results to state estimation with measurement origin uncertainty is also discussed with emphasis on joint detection and recursive state estimation.     

Evaluating a multiple-hypothesis multitarget tracking algorithm

The performance evaluation of multiple-hypothesis, multitarget tracking algorithm is presented. We are primarily interested in target-detection/track-initiation capabilities as measures of performance. Through Monte Carlo simulations, a multiple-hypothesis tracking algorithm was evaluated in terms of 1) probability of establishing a track from target returns and 2) false track density. A radar was chosen as the sensor, and a general multiple-hypothesis, multitarget tracking algorithm was used in the Monte Carlo simulations. The simulation results predict the probability of establishing a track from returns of a target as well as the false track density per scan volume per unit time. The effects of the target radar cross section and the radar power, measured through the mean signal-to-noise ratio (SNR) were studied, as were the effects of detection threshold and track quality threshold. Computational requirements were also investigated     

Knowledge-based system for multi-target tracking in a littoral environment

The paper addresses how to efficiently exploit the knowledge-base (KB), e.g. environmental maps and characteristics of the targets, in order to gain improved performance in the tracking of multiple targets via measurements provided by a ship-borne radar operating in a littoral environment. In this scenario, the nonhomogeneity of the surveillance region makes the conventional tracking systems (not using the KB) very sensitive to false alarms and/or missed detections. It is demonstrated that an effective use of the KB can be exploited at various levels of the tracking algorithms so as to significantly reduce the number of false alarms, missed detections, and false tracks and improve true target track life. The KB is exploited at two different levels. First, some key parameters of the tracking system are made dependent upon the track location, e.g., sea, land, coast, meteo zones (i.e., zones affected by meteorological phenomena) etc. Second, modifications are introduced to cope with a priori identified regions nit hi high clutter density (e.g. littoral areas, roads, meteo zones etc.). To evaluate the behavior of the proposed knowledge-based tracking systems, extensive results are presented using both simulated and real radar data     

基于最近邻的集中式多传感器多目标跟踪算法

经典的集中式多传感器多目标跟踪算法通常计算量较大,经常难以满足系统的实时性要求,工程上实现起来比较困难,为进一步扩大集中式多传感器的应用范围,使其在对算法实时性要求较高、跟踪精度要求较小的实际场合中广泛应用。文章基于最近邻域思想,研究了并行处理结构的集中式多传感器最近邻域算法,并从算法跟踪精度、实时性、有效跟踪率3个方面对其与经典的顺序多传感器联合概率数据互联算法进行了仿真比较。经仿真验证,并行处理结构的集中式多传感器最近邻域算法实时性提高了60%以上,且在跟踪背景杂波适中的情况下能够有效跟踪目标。     

Interacting multiple model tracking with target amplitude feature

A recursive tracking algorithm is presented which uses the strength of target returns to improve track formation performance and track maintenance through target maneuvers in a cluttered environment. This technique combines the interacting multiple model (IMM) approach with a generalized probabilistic data association (PDA), which uses the measured return amplitude in conjunction with probabilistic models for the target and clutter returns. Key tracking decisions can be made automatically by assessing the probabilities of target models to provide rapid and accurate decisions for both true track acceptance and false track dismissal in track formation. It also provides the ability to accurately continue tracking through coordinated turn target maneuvers     

Directed Subspace Search ML-PDA with Application to Active Sonar Tracking

The maximum likelihood probabilistic data association (ML-PDA) tracking algorithm is effective in tracking Very Low Observable targets (i.e., very low signal-to-noise ratio (SNR) targets in a high false alarm environment). However, the computational complexity associated with obtaining the track estimate in many cases has precluded its use in real-time scenarios. Previous ML-PDA implementations used a multi-pass grid (MPG) search to find the track estimate. Two alternate methods for finding the track estimate are presented-a genetic search and a newly developed directed subspace (DSS) search algorithm. Each algorithm is tested using active sonar scenarios in which an autonomous underwater vehicle searches for and tracks a target. Within each scenario, the problem parameters are varied to illustrate the relative performance of each search technique. Both the DSS search and the genetic algorithm are shown to be an order of magnitude more computationally efficient than the MPG search, making possible real-time implementation. In addition, the DSS search is shown to be the most effective technique at tracking a target at the lowest SNR levels-reliable tracking down to 5 dB (postprocessing SNR in a resolution cell) using a 5-frame sliding window is demonstrated, this being 6 dB better than the MPG search.     

反潜鱼雷靶试水下被动跟踪定位精度

跟踪重构鱼雷水下运动轨迹，对准确评估鱼雷状况及性能至关重要。文章研究了靶试环境下通过被动声纳浮标阵列跟踪某反潜鱼雷的定位精度。根据靶试对被动声纳浮标阵列要求，采用改进的代数方程方法定位鱼雷目标，通过蒙特卡洛仿真，研究对水下鱼雷的跟踪定位精度，为开展鱼雷靶试工作提供指导。     

Taxonomic performance evaluation for multitarget tracking systems

A scoring scheme based on an object track classification which facilitates performance evaluation for multiple-target tracking systems is derived and applied to specific test cases involving a comparison of operational and modified tracking system performance. The performance measures derived can be applied both to comparative evaluations of tracking system alternatives where relative performance is the issue, and to evaluations of system performance relative to what can be achieved in any given target object environment