基于MCMC-Gibbs采样的天波超视距雷达联合状态估计与模式辨识

天波超视距雷达(OTHR)目标跟踪面临着"三低"(低检测概率、低数据率、低测量精度)和"多路径"(多条传播路径)的严峻挑战,准确的传播模式辨识与精确的目标状态估计是改善跟踪能力的关键。针对上述问题,提出了一种基于马尔科夫蒙特卡洛吉布斯(MCMC-Gibbs)采样的OTHR联合状态估计与模式辨识算法,该算法通过MCMC-Gibbs采样求取当次迭代当前拍最优的关联矩阵,进而利用同时多量测滤波进行状态和协方差更新,最后引入联合估计与辨识风险函数寻求最优的模式辨识与状态估计结果。不同仿真参数下仿真结果表明该算法的有效性,同时该算法在径向距和方位角估计精度上均高于多路径概率数据关联算法(MPDA),但这是以计算量为代价的。     

基于目标战略优先级与精度自适应的效能函数的机载多传感器管理

传感器管理在数据融合系统中起着重要作用,机载多传感器系统通常面临着资源受限的状况,如传感器最大跟踪数量小于目标数量,无法保证所有传感器拥有相同精度与参照系等。因此,本文提出了一种基于目标战略优先级与精度自适应的效能函数的多传感器管理算法。首先,将空中目标的自身属性部分量化以得到目标战略优先级;再利用目标的跟踪精度、传感器测量精度和期望的跟踪精度之间的关系,定义了精度预提升矩阵和精度缺乏矩阵。再将上述两部分结合构建目标函数,运用整数线性规划的方法进行优化。最后,仿真试验设置了包括三个机载传感器对4个目标的跟踪任务,对比其他文献中的结果,显示出本文算法更为合理、有效。     

基于角速度估计自适应的IMM目标跟踪算法

为了解决目标强机动时目标跟踪算法模型集不匹配的问题,提出了一种基于角速度估计的自适应交互式多模型算法。通过对角速度的估计,在目标的不同运动模式下选取最优模型集,角速度估计精度高时,通过角速度估计值构造模型集,减小模型间竞争;角速度估计精度低时,采用标准IMM算法的模型集,提高模型集的覆盖范围,从而提高跟踪精度。仿真结果表明该方法能够明显提升目标跟踪性能,对强机动目标的跟踪效果尤其显著。     

基于模板匹配的集中式多传感器群内目标精细跟踪算法

为解决多传感器探测下群内目标精细跟踪的难题,基于非机动情况下各探测周期内群内目标真实回波位置相对固定的特性,提出了一种基于模板匹配的集中式多传感器群内目标精细跟踪算法。该算法通过预关联成功的群状态集合与群量测集合分别建立模板形状矩阵和待匹配形状矩阵,利用匹配搜索模型和匹配矩阵确认规则选出代价最小的匹配矩阵,并基于模板和对应的匹配矩阵利用 kalman滤波完成群内各目标航迹的状态更新。仿真表明,与传统多传感器多目标跟踪算法中性能优越的基于数据压缩的集中式多传感器多假设算法相比,该算法在跟踪精度、实时性、有效跟踪率方面的性能明显优越,能很好的满足群内目标精细跟踪的实际工程需求。     

基于混合滤波的无线传感器网络融合跟踪方法

 针对无线传感器网络(WSN)中的多传感器融合目标跟踪,提出一种混合滤波算法,称为无迹混合集中式粒子滤波(UM CPF)。该算法使用了一个混合的粒子传播方案。在使用集中式粒子滤波(CPF)对WSN中的节点测量信息进行融合时,粒子滤波器中的一部分粒子使用从无迹变换(UT)获得的高斯分布作为建议分布进行粒子传播,而剩余的另一部分粒子则简单地使用状态转移先验分布进行粒子传播。WSN中的融合跟踪仿真结果表明,和纯粒子滤波算法CPF相比,在仿真速率相当的情况下,混合滤波算法明显提高了跟踪精度和稳定性。     

面向再入目标跟踪的估计与辨识联合优化算法

准确的弹道系数辨识和精确的目标状态估计是再入目标高精度跟踪与高可靠识别的关键。一方面,状态估计的误差会造成模型参数(弹道系数)的辨识风险;另一方面,模型参数的辨识偏差又会导致模型失配从而降低目标状态的估计精度。因此,需要实现再入目标的状态估计和参数辨识的联合优化。针对再入目标弹道系数未知情形,提出了一种基于期望最大化(EM)框架并采用粒子滤波(PF)平滑器实现的PF-EM联合优化算法。在E步基于粒子平滑器得到目标状态的后验平滑估计,M步采用数值优化算法更新上一次迭代的弹道系数,通过E步和M步的不断迭代,以保证状态估计和弹道系数辨识的一致性。算法仿真对比表明:所提算法的状态估计和参数辨识精度均优于传统的状态增广算法。     

表面热流辨识结果的误差分析与估计

对于工程上的表面热流辨识问题,通常希望能够根据测量精度对辨识结果误差进行快速估计,用以优化测试方案。本文首先对给定单一频率的热流辨识误差进行定量分析,建立了辨识误差与热流频率和测量精度之间的响应面模型。然后对多个给定频率组合情况下的辨识误差规律进行分析,结果显示,频率组合热流中的低频分量能在辨识结果得到较好地复现,高频分量是导致辨识结果出现误差的主要原因。因此,辨识结果精度可以通过最高频率热流分量的辨识误差与测量精度之间的对应关系来进行大致估计。基于这一认识,本文利用时域不同频率组合的热流分量在频域可解耦的性质,通过Parseval定理得出高频分量的能量占比,建立了频率组合热流的辨识误差估计方法,并通过算例进行了验证。     

一种红外多传感器对中段弹道空间邻近目标的联合超分辨弹道估计方法

 分析红外焦平面(IR FRA)对中段弹道空间邻近目标(CSO)的成像特点,指出星载红外传感器为实现对空间邻近目标的跟踪必须对其进行超分辨。提出了一种中段弹道空间邻近目标联合超分辨与弹道估计新方法。该方法结合红外焦平面成像模型和中段弹道动力学模型,使得能够同时利用红外多传感器的多帧信息,基于最小二乘准则建立联合超分辨弹道估计目标函数,并分析选择各目标的起始状态参数作为模型参数。针对目标函数的高维非线性特点,推导最小化意义下等价的降维目标函数,采用量子粒子群优化算法最优化该降维目标函数直接求解模型参数,进而计算出各目标的弹道和辐射强度,实现中段弹道空间邻近目标的联合超分辨与弹道估计。仿真结果验证了该方法的有效性,且相比于传统的先单传感器单帧超分辨、然后多传感器多帧测角数据关联与滤波方法,新方法在避免数据关联复杂问题的同时,其弹道估计精度更高、分辨能力更强。     

基于多模型的低轨星座多目标跟踪传感器资源调度

针对低轨星座多目标持续跟踪传感器资源调度问题,首先将目标跟踪任务划分为高精度任务集合和低精度任务集合,并分析了跟踪任务状态转移过程;然后,为两任务集合分别建立了基于动态优先级的优化调度模型,提出了一种基于多模型的实时传感器调度算法。不同场景下仿真实验表明,所提算法较之以跟踪精度为优化目标和以跟踪精度为门限约束的方法具有更强的适用性,尤其对于目标分布较为集中的情况,其目标丢失率大大降低,尽管个别目标的跟踪误差略有增大。     

基于稀疏重建的MIMO-OTH雷达多模杂波抑制算法

针对由多模传播引起的多普勒扩展杂波(SDC)严重影响天波超视距雷达(OTHR)对慢速船舰目标的检测这一问题,在基于多输入多输出体制的新一代天波超视距(MIMO-OTH)雷达系统下,利用空域信息抑制多模SDC。传统处理方法需要目标角度先验信息,而在实际中,该信息往往很难获得。为此,提出一种新的基于稀疏重建的多模杂波抑制算法。该算法将二维的稀疏角度搜索转变为一维的角度搜索,使运算量大大降低,且估计出的收发角(DOA-DOD)自动配对,同时可获得各个传播路径下信号的时间多普勒信息,达到了理想的MIMO-OTH雷达多模杂波抑制效果。仿真结果证明了所提算法的有效性。     

A multipath data association tracker for over-the-horizon radar

A new algorithm, multipath probabilistic data association (MPDA), for initiation and tracking in over-the-horizon radar (OTHR) is described. MPDA is capable of exploiting multipath target signatures arising from discrete propagation modes that are resolvable by the radar. Nonlinear measurement models exhibiting multipath target signatures in azimuth, slant range, and Doppler are used. Tracking is performed in ground coordinates and therefore depends on the provision of estimates of virtual ionospheric heights to achieve coordinate registration. Although the propagation mode characteristics are assumed to be known, their correspondence with the detections is not required to be known. A target existence model is included for automatic track maintenance. Numerical simulations for four resolvable propagation modes are presented that demonstrate the ability of the technique to initiate and maintain track at probabilities of detection of 0.4 per mode in clutter densities for which conventional probabilistic data association (PDA) has a high probability of track loss, and suffers from track bias. A nearest neighbor version of MPDA is also presented     

PDAF with multiple clutter regions and target models

This paper presents the theory of a new multiple model probabilistic data association filter (PDAF). The analysis is generalized for the case of multiple nonuniform clutter regions within the measurement data that updates each model of the filter. To reduce the possibility of clutter measurements forming established tracks, the solution includes a model for a visible target. That is, a target that gives sensor measurements that satisfy one of the target models. Other features included in the algorithm are the selection of a fixed number of nearest measurements and the addition of signal amplitude to the target state vector. The nonuniform clutter model developed here is applicable to tracking signal amplitude. Performance of this algorithm is illustrated using experimentally recorded over-the-horizon radar (OTHR) data.     

基于DP-SA的机载外辐射源无源协同定位

针对强杂波背景下机载外辐射源无源协同定位（APCL）问题,提出一种基于动态规划和状态扩维（DP-SA）的单目标无源协同定位方法。首先,基于误差传播理论,构建了考虑外辐射源位置不确定性的状态转移范围,进而建立动态规划（DP）的值函数。其次,推导了基于极值理论的动态规划回溯阈值,提高了低可观测目标的检测概率。最后,对测量新息协方差进行在线修正,降低了外辐射源位置不确定性的影响,并利用状态扩维技术实现目标状态与外辐射源状态的联合估计。仿真验证了所提方法的有效性。     

Mixture Reduction Algorithms for Point and Extended Object Tracking in Clutter

The Bayesian solution to the problem of tracking a target with measurement association uncertainty gives rise to mixture distributions, which are composed of an ever increasing number of components. To produce a practical tracking filter, the growth of components must be controlled by approximating the mixture distribution. Two mixture reduction schemes (a joining algorithm and a clustering algorithm) have been derived for this purpose. If significant well spaced mixture components are present, these techniques can provide a useful improvement over the probabilistic data association filter (PDAF) approach, which reduces the mixture to a single Gaussian component at each time step. For the standard problem of tracking a point target in uniform random clutter, a Monte Carlo simulation study has been employed to identify the region of the problem parameter space where significant performance improvement is obtained over the PDAF. In the second part of this paper, the formal Bayesian filter is derived for an extended target consisting of an array of measurement sources with association uncertainty. A practical multiple hypothesis filter is implemented using mixture reduction and simulation results are presented.     

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.     

Loopy belief propagation based data association for extended target tracking

The data association problem of multiple extended target tracking is very challenging because each target may generate multiple measurements. Recently, the belief propagation based multiple target tracking algorithms with high efficiency have been a research focus. Different from the belief propagation based Extended Target tracking based on Belief Propagation (ET-BP) algorithm proposed in our previous work, a new graphical model formulation of data association for multiple extended target tracking is proposed in this paper. The proposed formulation can be solved by the Loopy Belief Propagation (LBP) algorithm. Furthermore, the simplified measurement set in the ET-BP algorithm is modified to improve tracking accuracy. Finally, experiment results show that the proposed algorithm has better performance than the ET-BP and joint probabilistic data association based on the simplified measurement set algorithms in terms of accuracy and efficiency. Additionally, the convergence of the proposed algorithm is verified in the simulations.     

密集杂波环境下的数据关联快速算法

基于联合概率数据互联（JPDA）的思想,提出了一种新的数据关联快速算法（Fast Al-gorithm for Data Association,简称FAFDA算法）.该方法不需象在最优JPDA算法中那样生成所有可能的联合互联假设,因而具有计算量小,易于工程实现的特点。仿真结果表明,与最优JPDA算法相比,FAFDA算法的跟踪性能令人满意,并且在密集杂波环境下可实时、有效地跟踪100批次以上的目标。     

High Range-Resolution Monopulse Tracking Radar

High range-resolution monopulse (HRRM) tracking radar which maintains wide instantaneous bandwidth through both range and angle error sensing channels provides range, azimuth, elevation, and amplitude for each resolved part of the target. The three-dimensional target detail can be used to improve and extend radar performance in several ways: for improved precision of target location, for target classification and recognition, to counter repeater-type ECM, to improve low-angle multipath tracking, to resolve multiple targets, as a miss-distance measurement capability, and for improved tracking in chaff and clutter. These have been demonstrated qualitatively except for the ECCM to repeater ECM and low-altitude tracking improvement. Initial results from an experimental HRRM radar with 3-ns pulse length show resolution of aircraft into its major parts and precise location of each resolved part accurately in range and angle. Realtime closed-loop tracking is performed on aircraft in flight using high-speed sampled, digitized, and processed HRRM range and angle video data. Clutter rejection capability is also demonstrated.     

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     

Threshold Control for Automatic Detection in Radar Systems

In automatic detection in radar systems an estimate of background clutter power is used to set the detection threshold. Usually detection cells surrounding the cell under test for the presence of a target are used to estimate the clutter power. In the research reported herein, the target location is taken to be uncertain and thus returns from a target could corrupt this clutter power estimate. It is shown how the threshold should be varied to compensate for the resulting degradation in detection performance. The threshold control procedure is based on a priori information about target location that could be supplied by the radar's tracking system. In addition, a simple procedure for calculating detection and false alarm probabilities for Swerling II target models is presented.