单被动传感器BOT鲁棒跟踪算法研究

对系统建立了仅有角度测量信息的单传感器跟踪(BOT)模型和算法,从机动目标鲁棒跟踪的角度给出了单传感器仅有角测量信息时的状态估计方法.BOT的状态估计采用修正增益扩展卡尔曼滤波器(MGEKF),目标加速度采用周期递推估计.该算法的模型简单、无噪声假定、无模型切换,并对目标机动具有良好的自适应能力.仿真结果验证了该方法的有效性.     

被动多传感器自适应曲线模型跟踪新算法

针对被动多传感器机动目标跟踪系统中,由于目标机动性能的不确定以及存在的非线性而导致系统模型与目标实际运动模式难以匹配的问题,提出一种新的自适应曲线模型跟踪算法．该算法通过建立新的方向角模型,设计一种自适应的转弯角速度估计方法,实时计算每个采样时刻目标的切向加速度,以获得与目标实际运动模式相匹配的运动模型,并与扩展卡尔曼滤波相结合,有效提高了被动多传感器下机动目标的跟踪精度．     

强跟踪输入估计概率假设密度多机动目标跟踪算法

针对多机动目标跟踪中,目标数目未知及加速度不确定的问题,提出一种强跟踪输入估计（modifiedinputestimation,MIE）概率假设密度多机动目标跟踪算法．在详细分析算法的基础上,通过引入强跟踪多重渐消因子,以不同速率实时调节滤波器各个通道的预测协方差及相应的滤波器增益,从而实现MIE算法对加速度未知或发生人幅度突变的机动目标白适应跟踪能力;并将该算法与概率假设密度滤波算法有效结合,町以较好地跟踪未知数目的多机动目标．仿真结果表明,新算法比传统的多机动目标跟踪算法具有更岛的跟踪精度,且具有较好的实时性．     

基于卡尔曼滤波与模糊逻辑的目标跟踪

针时卡尔曼滤波器时系统模型依赖性强、鲁棒性差和跟踪机动目标能力有限的问题,提出了一种新的利用混合模糊逻辑和标准卡尔曼滤波器的联合算法.此算法将前一目标航向与当前观测目标航向之差的绝对值和测量残留绝对值作为模糊控制器输入变量,充分利用模糊逻辑与卡尔曼滤波的各自优点,进一步提高估计器的估计性能.分别与增广方法和联合模糊逻辑方法进行了分析对比,仿真结果表明新算法的有效性,可实现对机动目标稳定可靠地跟踪.     

AR预测模型的IMM跟踪算法

针对LOS/NLOS混合条件下对机动目标的鲁棒跟踪问题,提出一种基于AR预测模型的交互式多模型（Interacting Multiple Model,IMM）跟踪算法（ARIMM）。该算法利用AR预测模型对运动状态建模,针对LOS与NLOS条件下观测噪声的分布不同分别使用无迹卡尔曼滤波器（Unscented Kalman Filter,UKF）和改进的无迹卡尔曼滤波器（Robust Unscented Kalman Filter,RUKF）,通过IMM方法估计出移动台的位置,利用该位置更新AR模型的参数,使AR模型与真实运动状态更加匹配,实现精确跟踪。仿真结果表明,在LOS/NLOS混合条件下,与传统的UKF和RUKF算法相比,该算法对机动目标跟踪的鲁棒性更好。     

基于强跟踪滤波器的MACA-MIE 模型及跟踪算法

结合自适应常加速模型(ACA)、改进输入估计(MIE)和强跟踪滤波器,提出一种新的自适应目标跟踪模型和算法.该算法通过扩展 ACA 模型状态矢量和改进状态噪声协方差调整方法,利用 MIE 和强跟踪滤波器,实现了机动加速度方差和状态预测协方差依据残差信息的实时完全自适应调整,在缺乏目标加速度先验知识的情况下,能够实时高精度跟踪目标突变状态、弱机动和非机动状态.仿真实验表明,相比 ACA 模型和 MIE,该算法具有更好的机动状态和非机动状态跟踪性能.     

基于新息偏差的自适应机动目标跟踪算法

证明了在某一目标机动输入下为保持跟踪最优,标准卡尔曼滤波器的过程噪声协 方差阵的调整公式,并利用观测新息在目标机动中发生正偏或负偏的信息,设计了一种自适 应跟踪算法．仿真说明该算法具有运算量小,跟踪精度高的特点．     

基于粒子滤波的机动目标跟踪

在单机动目标跟踪中,目标的机动情况是未知的,提出的算法用粒子滤波器求加速度的估计,由Kalman滤波得到加速度的重要性概率密度函数。仿真实验结果表明,该算法可较好地跟踪目标状态（包括加速度）的变化。     

新型粒子滤波算法及其在纯方位目标跟踪中的应用

针对基本粒子滤波算法没有融合当前时刻观测值的缺点,提出了一种卡尔曼粒子滤波算法。该算法针对每一个粒子使用卡尔曼滤波器进行更新,在更新过程中融合最新的观测信息,提高粒子滤波器的估计精度。针对纯方位目标跟踪问题进行实验,与基本粒子滤波算法及卡尔曼滤波进行了对比。实验结果表明,卡尔曼粒子滤波算法的跟踪性能明显优于其他两种算法。     

角闪烁噪声测量条件下的剩余飞行时间估计

针对雷达导引头角闪烁噪声测量条件下的机动目标,研究剩余飞行时间计算方法;建立了闪烁噪声计算模型;在粒子滤波算法和扩展卡尔曼滤波算法的基础上,推导了扩展卡尔曼粒子滤波算法的实现过程;根据估计结果建立了剩余飞行时间计算模型,在剩余飞行时间表达式中考虑了目标机动加速度的影响;仿真结果表明,基于机动目标当前统计模型的扩展卡尔曼粒子滤波算法对闪烁噪声测量条件下的机动目标具有良好的跟踪性能,对剩余飞行时间具有较高的估计精度。     

IMM Kalman滤波前馈补偿技术在搜索跟踪系统中的应用

空中低慢小目标存在机动和角速率运动较大的情况,对地面搜索跟踪系统的跟踪精度提出很高要求,为了提高跟踪系统的跟踪精度,需加入伺服前馈补偿技术,精确的目标速度和加速度估计成为前馈补偿控制的难点.鉴于此,提出采用IMM卡尔曼滤波技术估计目标运动速度和加速度信息,并作为伺服前馈补偿的输入量,以消除由于目标速度和加速度运动引起的脱靶量误差.实际系统测试实验表明,搜索跟踪系统采用IMM卡尔曼滤波前馈补偿技术使得系统跟踪精度较常规卡尔曼滤波补偿提高3倍以上,模型验证有效.     

Tracking a maneuvering target using neural fuzzy network

A fast target maneuver detecting and highly accurate tracking technique using a neural fuzzy network based on Kalman filter is proposed in this paper. In the automatic target tracking system, there exists an important and difficult problem: how to detect the target maneuvers and fast response to avoid miss-tracking? The traditional maneuver detection algorithms, such as variable dimension filter (VDF) and input estimation (IE) etc., are computation intensive and difficult to implement in real time. To solve this problem, neural network algorithms have been issued recently. However, the normal neural networks such as backpropagation networks usually produce the extra problems of low convergence speed and/or large network size. Furthermore, the way to decide the network structure is heuristic. To overcome these defects and to make use of neural learning ability, a developed standard Kalman filter with a self-constructing neural fuzzy inference network (KF-SONFIN) algorithm for target tracking is presented in this paper. By generating possible target trajectories including maneuver information to train the SONFIN, the trained SONFIN can detect when the maneuver occurred, the magnitude of maneuver values and when the maneuver disappeared. Without having to change the structure of Kalman filter nor modeling the maneuvering target, this new algorithm, SONFIN, can always find itself an economic network size with a fast learning process. Simulation results show that the KF-SONFIN is superior to the traditional IE and VDF methods in estimation accuracy.     

基于修正的卡尔曼滤波自适应跟踪算法

针对卡尔曼滤波对匀速运动目标能有效的跟踪,但是当目标出现转弯时,很难达到跟踪精度的要求,甚至丢失目标的现象.对卡尔曼滤波算法进行了改进,在观测向量中引入了两个加速度误差变量,它们动态地修正状态估计误差从而减少跟踪精度误差,形成了修正的Kalman算法.但是由于状态变量维数增加,使得计算量增加,实时性下降,将卡尔曼滤波算法与修正的卡尔曼滤波算法两种算法相结合,提出了基于修正的卡尔曼滤波自适应跟踪算法.仿真结果表明,具有良好的稳定性和精确度,优于一般的卡尔曼滤波算法.     

Tracking of multiple target types with a single neural extended Kalman filter

The neural extended Kalman filter is an adaptive state estimation routine that can be used in target‐tracking systems to aid in the tracking through maneuvers without prior knowledge of the targets' dynamics. Within the neural extended Kalman filter, a neural network is trained using a Kalman filter training paradigm that is driven by the same residual as the state estimator. The difference between the a priori model used in the prediction steps of the estimator and the actual target dynamics is approximated. An important benefit of the technique is its versatility because little if any a priori knowledge of the target dynamics is needed. This allows the technique to be used in a generic tracking system that will encounter various classes of targets. In this paper, the neural extended Kalman filter is applied simultaneously to three separate classes of targets, each with different maneuver capabilities. The results show that the approach is well suited for use within a tracking system with multiple possible or unknown target characteristics. © 2010 Wiley Periodicals, Inc.     

Applying a novel extended Kalman filter to missile–target interception with APN guidance law: A benchmark case study

This paper considers the estimation of the target acceleration with unknown dynamics along with other states of a benchmark example of a nonlinear 2D missile–target engagement system in presence of model uncertainties and measurement noises. The objective is to implement the augmented proportional navigation (APN) guidance law for the missile–target interception to minimize the distance between the missile and the target. The estimated target acceleration can be treated as an unknown input to the nonlinear 2D missile–target engagement system. A novel analytical recursive approach referred to as extended Kalman filter with unknown inputs without direct feedthrough (EKF-UI-WDF) is derived with the weighted least squares estimation for an extended state vector including states and unknown inputs which can be any type of signals without prior information. By applying the proposed EKF-UI-WDF approach to a 2D missile–target interception control system, simulation results demonstrate that this approach is capable of (i) estimating the states and unknown input (target acceleration) well, and (ii) achieving more reasonable interception performance comparing with the traditional extended Kalman filter (EKF) approach.     

姿态解算与外力加速度同步估计算法

针对惯性导航应用中,姿态解算与外力加速度估计互相干扰的问题,提出一种基于四元数和扩展卡尔曼滤波器的姿态解算与外力加速度同步估计算法。首先,利用估计的外力加速度修正传感器加速度数据得到准确的反向重力加速度,再结合地磁场向量通过梯度下降算法解算得到旋转四元数的测量值;其次,构建扩展卡尔曼滤波模型,对旋转四元数和外力加速度进行更新,得到旋转四元数的预测值和外力加速度的预测值;最后,用旋转四元数的测量值和测量得到的加速度数据对预测值通过扩展卡尔曼滤波的方法进行校正,最终得到准确的旋转四元数和参考坐标系下三轴方向上的外力加速度。实验表明,通过扩展卡尔曼滤波同时对姿态和外力加速度估计的方法,能够迅速收敛并准确得机体姿态信息以及外力加速度信息,欧拉角误差为±1.95°,加速度误差为±0.12 m/s²,并且该算法能有效抑制外力加速度对姿态解算的影响,准确估计外力加速度。     

Simplified adaptive estimation

A simplified adaptive scheme is suggested for the estimation of the state vector of linear systems driven by white process noise that is added to an unknown deterministic signal. The design approach is based on embedding the Kalman filter (KF) within a simplified adaptive control loop that is driven by the innovation process. The simplified adaptive loop is idle during steady-state phases that involve white driving noise only. However, when the deterministic signal is added to the driving noise signal, the simplified adaptive control loop enhances the KF gains and helps in reducing the resulting transients. The stability of the overall estimation scheme is established under strictly passive conditions of a related system. The suggested method is applied to the target acceleration estimation problem in a Theater Missile Defence scenario.     

机动目标跟踪双滤波器模型及自适应算法

现代机动目标跟踪的困难来自跟踪的快速生成精度在一定计算负荷约束下的协调难以令人满意,考虑依次处理快速性与精度的方案。采用滑动均值均匀分布描述目标的随机机动特性,分别采用宽带的均值预估滤波器和窄带的跟踪滤波器串联,实现机动速度大变动或突变的精确,快速跟踪,双滤波器的计算量适中,易于工程实现,对各种运动形式进行计算机模拟表明,这类算法对高度机动或弱机动或无机动均可给出较好的目标位置,速度及加速度估值。