Optimization of waveform and detection threshold for range and range-rate tracking in clutter

We address an optimization problem to obtain the combined sequence of waveform parameters (pulse amplitudes and lengths, and FM sweep rates) and detection thresholds for optimal range and range-rate tracking in clutter. The optimal combined sequence minimizes a tracking performance index under a set of parameter constraints. The performance index includes the probability of track loss and a function of estimation error covariances. The track loss probability and the error covariances are predicted using a hybrid conditional average algorithm. The effect of the false alarms and clutter interference is taken into account in the prediction. A measurement model in explicit form is also presented which is developed based on the resolution cell in the delay-Doppler plane for a single Gaussian pulse. Numerical experiments were performed to solve the optimization problem for several examples.     

Tracking in clutter with nearest neighbor filters: analysis andperformance

The measurement that is “closest” to the predicted target measurement is known as the “nearest neighbor” (NN) measurement in tracking. A common method currently in wide use for tracking in clutter is the so-called NN filter, which uses only the NN measurement as if it were the true one. The purpose of this work is two fold. First, the following theoretical results are derived: the a priori probabilities of all three data association events (updates with correct measurement, with incorrect measurement, and no update), the probability density functions (pdfs) of the NN measurement conditioned on the association events, and the one-step-ahead prediction of the matrix mean square error (MSE) conditioned on the association events. Secondly, a technique for prediction without recourse to expensive Monte Carlo simulations of the performance of tracking in clutter with the NN filter is presented. It can quantify the dynamic process of tracking divergence as well as the steady-state performance. The technique is a new development along the line of the recently developed general approach to the performance prediction of algorithm with both continuous and discrete uncertainties     

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。     

Detection threshold selection for tracking performance optimization

A technique for the evaluation of the track loss probability and the estimation error during track maintenance in clutter has been developed recently by the authors. This work overcomes the limitation of an earlier technique that does not handle the transient process of tracking divergence. Track loss, being a “runaway” phenomenon, clearly requires transient evaluation capability. The new technique provides, without the need for expensive Monte Carlo simulations, the probability that a hack is maintained in the presence of all sources of uncertainty encountered In a tracking process. This technique is of a hybrid nature; it involves explicit probabilistic accounting of both the continuous and the discrete uncertainties. Here it is demonstrated how this technique can be used for the selection of the detection threshold to optimize the overall performance of a detection-trading system     

区域杂波估计的多目标跟踪方法

高斯粒子概率假设密度（PHD）滤波往往假定杂波密度参数已知,这种做法对于实际应用是不现实的。此外,杂波的参数值通常依赖于环境条件,可能随时间发生变化。因此,多目标跟踪算法中需要实时准确估计杂波密度的参数。基于此,提出了一种多目标跟踪的区域杂波估计方法。首先根据量测信息在线估计出场景中的杂波数目,然后估计落入目标附近感兴趣区域的杂波数,并估计每个目标感兴趣区域杂波强度。仿真结果表明,在复杂场景下算法的跟踪性能明显优于未进行杂波估计的多目标跟踪算法,提高了跟踪的实时性和跟踪精度。     

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.     

基于量测一致性的分布式多传感器多目标跟踪算法

针对无线传感器网络跟踪多目标过程中传感器能搭载的计算负荷有限,不宜采用复杂算法实现数据处理的问题,提出了一种基于量测一致性的分布式多传感器多目标跟踪算法。算法采用计算相对简易的最近邻域法处理多目标跟踪中的数据互联问题,针对最近邻域法容易受杂波干扰的情况,通过量测的平均一致性迭代来改进算法的性能。仿真结果证明,算法具备有效抑制因误判产生的错误量测对跟踪过程干扰的性能,实现了良好的传感器网络跟踪精度和估计信息一致性。     

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     

修正的概率数据互联算法

阐明了概率数据互联(PDA)算法能很好地解决密集环境下的目标跟踪问题,在该算法基础上,人们又提出了联合概率数据互联(JPDA)算法和一些基于 PDA 的修正算法。在概率数据互联算法中,有一个很重要的参数就是杂波数密度(或波门内虚假量测期望数)。然而在许多实际情况中,这个参数是很难获取的。针对这一问题,文中提出了一种修正的概率数据互联算法,该算法通过实时地调整这一参数来获得对目标较为准确的估计结果。最后,给出了算法的仿真分析。     

Neural solution to the multitarget tracking data associationproblem

The problem of tracking multiple targets in the presence of clutter is addressed. The joint probabilistic data association (JPDA) algorithm has been previously reported to be suitable for this problem in that it makes few assumptions and can handle many targets as long as the clutter density is not very high. However, the complexity of this algorithm increases rapidly with the number of targets and returns. An approximation of the JPDA that uses an analog computational network to solve the data association problem is suggested. The problem is viewed as that of optimizing a suitably chosen energy function. Simple neural-network structures for the approximate minimization of such functions have been proposed by other researchers. The analog network used offers a significant degree of parallelism and thus can compute the association probabilities more rapidly. Computer simulations indicate the ability of the algorithm to track many targets simultaneously in the presence of moderately dense clutter     

An Algorithm for Tracking a Maneuvering Target in Clutter

We consider the problem of tracking a maneuvering target in clutter. In such an environment, missed detections and false alarms make it impossible to decide, with certainty, the origin of received echoes. Processing radar returns in cluttered environments consists of three functions: 1) target detection and plot formation, 2) plot-to-track association, and 3) track updating. Two inadequacies of the present approaches are 1) Optimization of detection characteristics have not been considered and 2) features that can be used in the plot-to-track correlation process are restricted to a specific class. This paper presents a new approach to overcome these limitations. This approach facilitates tracking of a maneuvering target in clutter and improves tracking performance for weak targets.     

基于先验门限优化准则的探测阈值自适应选择

针对 2维测量和 4 -sigma确认门 ,把先验检测门限优化准则和修正 Riccati方程的解析近似表示相结合 ,得到了在瑞利起伏环境下使跟踪性能优化的信号探测阈值解析表示式 ,从而使在线求解自适应信号探测阈值能比较容易地实现。通过研究和仿真发现 :在滤波稳定阶段 ,本文给出的自适应信号检测门限方法的跟踪性能优于固定虚警率方法的跟踪性能 ;基于先验检测门限优化准则实现检测 -跟踪的联合优化要求信噪比要大于一定的门限 ,在瑞利起伏环境下 ,对 2维测量和 4 -sigma确认门 ,该门限为 1 .57     

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。     

Multiple target detection using modified high order correlations

This work is concerned with the problem of multiple target track detection in heavy clutter. Using the “modified high order correlation” (HOC) process and a track scoring mechanism a new method is developed to perform data association and track identification in the presence of heavy clutter. Using this new scheme any number of very close, crossing or splitting target tracks can be resolved without increasing the computational complexity of the algorithm. The applicability of the method for continuous detection of target tracks that can originate and terminate at any scan is also demonstrated, In addition, the operating characteristics as a function of the clutter density are also provided. Simulation results on all the cases are presented     

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     

Clutter map information for data association and track initialization

This paper considers the problem of forming and maintaining tracks when measurements have both uncertain origin and are corrupted by additive sensor noise. The spatial clutter measurement density is assumed nonhomogeneous and known. The PPDA-MAP algorithm provides a set of recursive formulae for data association and probability of target existence, thus enabling automatic track initiation, track maintenance, and track termination. New values for initial probability of target existence for IPDA-type algorithm are also derived. Simulation results compare the performance of IPDA-MAP with IPDA, IMM-PDA, IMM-PDA-MAP, EB-PDA and EB-PDA-MAP in a heavy and nonuniform clutter situation.     

Waveform selective probabilistic data association

An adaptive, waveform selective probabilistic data association (WSPDA) algorithm for tracking a single target in clutter is presented. The assumption of an optimal receiver allows the inclusion of transmitted waveform specification parameters in the tracking subsystem equations, leading to a waveform selection scheme where the next transmitted waveform parameters are selected so as to minimize the average total mean-square tracking error at the next time step. Semiclosed form solutions are given to the local (one-step-ahead) adaptive waveform selection problem for the case of one-dimensional target motion. A simple simulation example is given to compare the performance of a tracking system using a WSFDA based tracking filter with that of a conventional system with a fixed waveform shape and probabilistic data association (PDA) tracking filter.     

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     

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     

Integrated track maintenance for the PMHT via the hysteresis model

Unlike other tracking algorithms the probabilistic multi-hypothesis tracker (PMHT) assumes that the true source of each measurement is an independent realisation of a random process. Given knowledge of the prior probability of this assignment variable, data association is performed independently for each measurement. When the assignment prior is unknown, it can be estimated provided that it is either time independent, or fixed over the batch. This paper presents a new extension of the PMHT, which incorporates a randomly evolving Bayesian hyperparameter for the assignment process. This extension is referred to as the PMHT with hysteresis. The state of the hyperparameter reflects each model's contribution to the mixture, and thus can be used to quantify the significance of mixture components. The paper demonstrates how this can be used as a method for automated track maintenance in clutter. The performance benefit gained over the standard PMHT is demonstrated using simulations and real sensor data