A Poisson multi-Bernoulli mixture filter with spawning based on Kullback–Leibler divergence minimization

In the classical form, the Poisson Multi-Bernoulli Mixture (PMBM) filter uses a PMBM density to describe target birth, surviving, and death, which does not model the appearance of spawned targets. Although such a model can handle target birth, surviving, and death well, its performance may degrade when target spawning arises. The reason for this is that the original PMBM filter treats the spawned targets as birth targets, ignoring the surviving targets’ information. In this paper, we propose a Kullback–Leibler Divergence (KLD) minimization based derivation for the PMBM prediction step, including target spawning, in which the spawned targets are modeled using a Poisson Point Process (PPP). Furthermore, to improve the computational efficiency, three approximations are used to implement the proposed algorithm, such as the Variational Multi-Bernoulli (VMB) filter, the Measurement-Oriented marginal MeMBer/Poisson (MOMB/P) filter, and the Track-Oriented marginal MeMBer/Poisson (TOMB/P) filter. Finally, simulation results demonstrate the validity of the proposed filter by using the spawning model in these three approximations.     

幅度及多普勒信息辅助的多目标跟踪算法

针对现有的多目标跟踪（MTT）算法在强杂波环境下会出现严重的性能衰退,利用目标的幅度及多普勒特征,基于随机有限集（RFS）理论,提出了一种幅度及多普勒信息辅助的δ广义标签多伯努利（ADI-δ-GLMB）滤波器。首先,利用幅度及多普勒信息对目标状态进行扩展,在此基础上建立了新的量测似然函数。然后,基于δ-GLMB滤波器框架推导了新的更新方程。最后,进行仿真验证,结果表明：强杂波下,ADI-δ-GLMB滤波器跟踪性能明显优于δ-GLMB滤波器,估计精度及稳定性更高,且计算量更低;同信息辅助的概率假设密度（PHD）、多目标多伯努利（MeMBer）滤波器相比,ADI-δ-GLMB滤波器状态估计精度更高。     

Filtering of moving targets using SBIR sequential frames

In this paper three-dimensional (3-D) finite-impulse response (FIR) filters are proposed for moving target detection and tracking from multiframe space-based infrared (SBIR) data. An optimal, in the l_p sense, 3-D FIR filter design technique is proposed which is suitable for the above application. This technique is the first 3-D FIR design of its kind presented in the open literature. Directional, matched, and adaptive 3-D filtering techniques are proposed. Prior to the filtering, clutter mean estimation and mean subtraction are required. Real time implementation of directional and/or matched filters for processing maneuvering targets is discussed and filter design methods are proposed. Finally, performance comparisons of the proposed and other available 3-D FIR and infinite-impulse response (IIR) filters, on real SBIR data, are presented in which the advantages of the proposed 3-D filters are shown     

组合导航系统中一种基于特征值分解的自适应信息融合滤波算法

基于分散滤波理论的联合滤波算法,可以有效地降低组合导航系统的计算负担,并且增强系统的容错性能。给出了一种联合滤波算法中信息分配系数的自适应计算方法,能够使联合系统根据导航过程中各传感器的信息质量的变化合理地反馈全局信息。仿真结果表明,该算法可以有效地降低由于导航子系统降级带来的滤波误差。     

Nonlinear Estimation of Generalized Vector Shot Processes

A class of nonlinear filters for dynamical systems driven by generalized Poisson processes is developed. One of the filters, the maximum a posteriori (MAP) filter, is shown by a numerical example to be superior to other known predictors in getting the highest target hit probabilities, and it is relatively simple to implement. This filter has applications in both fire control and air traffic control of maneuvering piloted vehicles.     

基于改进联邦Kalman滤波的组合校准方法研究

联邦滤波器广泛应用于多传感器信息融合领域,联邦滤波中的信息分配原则影响滤波精度.针对联邦Kalman滤波器进行改进,采用基于估计协方差阵奇异值动态确定信息分配系数.对子滤波器进行重置时,采用新的重置方法,保证了子滤波器误差协方差阵的对称性,确保Kalman滤波器的一致收敛稳定性.新的联邦滤波算法允许每个状态分量拥有不同的动态信息分配因子,从而改进了联邦滤波信息融合的精度.设计了SINS/GPS/电子罗盘组合导航系统,仿真结果说明,与传统联邦滤波算法相比,改进的联邦滤波器估计精度得到了提高,可以更好地对SINS误差进行校准,提高系统的精度.     

基于联邦滤波结构的INS／GPS组合导航系统数据融合研究

为了研究平台式惯导INS（interial navigation system）和全球定位系统GPS（globe position system）组合导航联邦滤波器的实现,使用速度局部滤波器和位置局部滤波器,分别对INS／GPS组合导航系统的向东速度、向北速度,以及对经度和纬度进行卡尔曼滤波,然后将位置数据和速度数据输入主滤波进行数据融合。以无人机的向东匀速水平飞行为背景,运用联邦卡尔曼滤波器算法,使用matelab进行仿真分析。可以证明联邦滤波器算法简单,易于实现,并且可以提高导航系统精度．实际应用中此方法可行。     

自适应高阶容积卡尔曼滤波在目标跟踪中的应用

针对传统容积卡尔曼滤波(CKF)在系统状态发生突变时估计精度下降的问题,将强跟踪滤波(STF)算法与高阶容积卡尔曼滤波(HCKF)算法相结合,提出了一种自适应高阶容积卡尔曼滤波(AHCKF)方法。该算法采用高阶球面-相径容积规则,可获得高于传统CKF的估计精度,同时在HCKF算法中引入STF,通过渐消因子在线修正预测误差协方差阵,强迫残差序列正交,提高了算法的鲁棒性,增强了算法应对系统状态突变等不确定因素的能力。将提出的AHCKF算法应用于具有状态突变的机动目标跟踪问题并进行数值仿真,仿真结果表明,AHCKF算法在系统状态发生突变的情况下表现出良好的滤波性能,有效地避免了状态突变造成的滤波精度下降,较传统的CKF、HCKF、交互式多模型-容积滤波(IMM-CKF)和自适应容积卡尔曼滤波(ACKF)算法有更强的鲁棒性和系统自适应能力。     

Asynchronous distributed detection

Binary parallel distributed-detection architectures employ a bank of local detectors to observe a common volume of surveillance, and form binary local decisions about the existence or nonexistence of a target in that volume. The local decisions are transmitted to a central detector, the data fusion center (DEC), which integrates them to a global target or no target decision. Most studies of distributed-detection systems assume that the local detectors are synchronized. In practice local decisions are made asynchronously and the DFC has to update its global decision continually. In this study the number of local decisions observed by the central detector within any observation period is Poisson distributed. An optimal fusion rule is developed and the sufficient statistic is shown to be a weighted sum of the local decisions collected by the DFC within the observation interval. The weights are functions of the individual local detector performance probabilities (i.e., probabilities of false alarm and detection). In this respect the decision rule is similar to the one developed by Chair and Varshney for the synchronized system. Unlike the Chair-Varshney rule, however, the DFC's decision threshold in the asynchronous system is time varying. Exact expressions and asymptotic approximations are developed for the detection performance with the optimal rule. These expressions allow performance prediction and assessment of tradeoffs in realistic decision fusion architectures which operate over modern communication networks     

航空发动机部件性能故障融合诊断方法研究

提出一种对航空发动机部件性能蜕化进行融合诊断的模糊决策融合机制,以改善单独采用基于模型和基于数据的部件性能故障诊断的漏诊与误诊的问题.传感器测量值同时输入到基于自适应模型的和基于数据的诊断模块中,分别利用卡尔曼滤波算法和最小二乘支持向量机(LSSVM)对主要部件故障性能参数估计,再利用模糊逻辑调整决策权重以进行D-S(Dempster-Shafer)证据理论的决策融合诊断.以某型涡扇发动机为对象进行单部件和双部件蜕化仿真研究表明,与单独使用基于模型和基于数据的诊断方法相比,采用决策融合机制有效地提高了部件故障诊断精度.      

改进的量测一致性的联邦滤波器两级故障检测

当联邦滤波器故障为缓变故障或者故障幅值比较小时,传统的基于量测一致性的联邦滤波器故障检测算法由于相应的估计误差和方差同时增大,这使得算法需一定的时间后才能使这种增量大到使故障函数计算结果超出门限,因而会出现警告延迟甚至漏检现象.针对量测一致性算法的缺点,提出了改进的基于量测一致性的联邦滤波器两级故障检测算法.相比传统的基于量测一致性的故障检测算法,该方法增加了故障检测的冗余性,不仅可以区分硬故障和软故障,而且提高了故障检测的可靠性和灵敏度,使得联邦滤波器的故障检测方法更加成熟完善,这对提高整个系统的可靠性具有重要意义.仿真和实验结果表明,该方法准确度高,计算量小,便于工程实现.     

Detection and range/Doppler estimation for colocated sensors

The problem of multisensor detection and high resolution signal state estimation using joint maximum a posteriori detection and high order nonlinear filtering techniques is addressed. The model-based fusion approach offers the potential for increased target resolution in range/Doppler/azimuth space. The approach employs joint detection/estimation filters (JDEF) for target detection and localization. The JDEF approach segments the aggregate nonlinear model over the entire target resolution space into a number of localized nonlinear models by partitioning the resolution space into a number of resolution subcells. This partitioning leads to extremely accurate state estimation. The proposed JDEF approach has a built-in capability for automatic data alignment from multiple sensors, and can be used for centralized, decentralized, and distributed data fusion.     

Real-Time Tracking Filter Evaluation and Selection for Tactical Applications

Five important tracking filters that are often candidates for implementation in systems that must track maneuvering vehicles are compared in terms of tracking accuracy and computer requirements for tactical applications. A rationale for selecting among these filters, which include a Kalman filter, a simplified Kalman filter, an ?-? filter, a Wiener filter, and a two-point extrapolator, is illustrated by two examples taken from the authors' recent experience.     

基于改进容积卡尔曼滤波的惯性/光流组合自主测速方法

针对容积卡尔曼滤波算法在惯性/光流组合测速数据融合时出现由于各系统输出数据频率不一致导致融合精度有限的问题,提出了一种基于多速率残差校正的改进容积卡尔曼滤波算法.通过当前时刻误差估算组合导航系统残差,再使用估算后的残差对速度估计值进行补偿,最终实现惯性/光流组合系统速度测量值的数据融合.实验结果表明,通过提出的改进容积...     

An ECR-PCR rule for fusion of evidences defined on a non-exclusive framework of discernment

In the research of uncertain information processing, Dempster-Shafer Theory (DST) provides a framework for dealing with uncertain information, where evidence is defined on a Frame of Discernment (FOD) consisting of mutually exclusive elements. However, the requirement of exclusiveness on FOD sometimes is not satisfied, as shown in Dezert-Smarandache Theory(DSmT), a derivative of DST. In DSmT, the non-exclusiveness is expressed by propositions’ intersection and the fusion of evidence is realized through a Proportional Conflict Redistribution (PCR) rule. In order to handle non-exclusive FODs, a new framework called D Number Theory (DNT) has been proposed recently, which quantifies the non-exclusive degree between propositions different from DSmT. In previous studies, an Exclusive Conflict Redistribution (ECR) rule has been designed in DNT to implement the fusion of evidence defined on a non-exclusive FOD, but there are some deficiencies in the ECR rule. In this paper, a new rule called ECR-PCR rule is proposed by combining the ECR and PCR rules to better implement the fusion of evidence defined on a non-exclusive FOD. Within the proposed rule, the definition of conflict utilizes the idea of ECR’s exclusive conflict, and the disposal of conflict is following the idea of PCR’s proportional redistribution. Properties of the ECR-PCR rule are presented. The effectiveness of the proposed new rule is verified through numerical examples and applications, in comparison with other fusion methods.     

Decentralized CFAR signal detection

The authors develop the theory of CA-CFAR (cell-averaging constant false-alarm rate) detection using multiple sensors and data fusion, where detection decisions are transmitted from each CA-CFAR detector to the data fusion center. The overall decision is obtained at the data fusion center based on some k out of n fusion rule. For a Swerling target model I embedded in white Gaussian noise of unknown level, the authors obtain the optimum threshold multipliers of the individual detectors. At the data fusion center, they derive an expression for the overall probability of detection while the overall probability of false alarm is maintained at the desired value for the given fusion rules. An example is presented showing numerical results     

Model-based robust estimation and fault detection for MEMS-INS/GPS integrated navigation systems

In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.     

一种基于Gabor滤波的不同聚焦图像融合方法

提出了一种基于Gabor滤波对不同聚焦图像融合的方法。通过取不同方向的Gabor滤波器对待融合的图像进行多方向滤波,从而提取出不同方向的高频能量,再通过比较高频能量大小来判断属于清晰还是模糊像素,以此达到融合不同聚焦图像的目的。实验表明这种方法简单有效,给出了图像融合的实验结果,并与小波变换和简单取平均融合结果进行了比较。     

Real-time navigation using the global positioning system

This paper presents the results of an investigation of the application of the Global Positioning System (GPS) to real-time integrated missile navigation. We present quantifiable measures of navigation accuracy as a function of GPS user segment parameters. These user segment parameters include antenna phase response accuracy, single versus dual frequency, and Kalman filter structure and size. We also formulate some new phase-locked loop (PLL) filter designs for application in GPS receivers, and demonstrate their superiority over more conventional filters     

Enhanced variable dimension filter for maneuvering target tracking

An enhancement of the variable dimension (VD) filter for maneuvering-target tracking is presented. The use of measurement concatenation, a procedure whereby fast sampled measurements are stacked while maintaining their proper relationships with the states, leads to significant reduction in estimation error by low processing rate algorithms. The use of double decision logic (DDL) for the maneuver onset and ending detection as well as appropriate procedures for reinitialization of the estimation filters results in improved maneuver detection and filter adaptation. Simulation results show the performance of the proposed enhanced variable dimension (EVD) filter