基于无迹卡尔曼滤波的双选信道估计方法

针对高速移动场景中由于信道存在时频域选择性衰落(双选衰落)导致正交频分复用(orthogonal frequency division multiplexing, OFDM)通信系统受到子载波间干扰而使通信质量下降的问题,采用基扩展信道模型,在消除子载波间干扰(inter carrier interference, ICI)影响的同时节约信道估计算法的空间复杂度。针对基扩展模型(basis expansion model, BEM)下的非线性信道状态空间模型,采用无迹卡尔曼滤波(unscented Kalman filter, UKF)方法进一步跟踪信道响应提升信道估计精度。仿真分析表明,本研究所提方法能够有效提升信道估计的精度,相对于传统算法具有更好的误码率性能和鲁棒性。     

再入弹道估计中动力学模型和“当前”统计模型的性能比较

飞行器在再入段的弹道可用较为精确的模型描述,利用UKF,分别结合动力学模型和＂当前＂统计模型,对再入弹道（弹道式和机动式）进行了估计,并就其滤波性能进行了对比分析.仿真结果表明,在对弹道式再入飞行器弹道的实时滤波中,BRV-Exp模型要比CS模型更为合适,弹道估计精度得到明显提高;在对机动式再入飞行器弹道的实时滤波中,MRV-Wiener模型并不优于CS模型.     

基于改进Sage-Husa的自适应无迹卡尔曼滤波的锂离子电池SOC估计

扩展卡尔曼滤波(EKF)算法估计锂离子动力电池荷电状态(SOC)时,由于系统噪声的不确定,可能导致估计算法不精确,并且算法中的线性化处理受电池模型的影响很大。为了解决上述两个问题,本文采用改进Sage-Husa的自适应无迹卡尔曼滤波法(AUKF)来动态地估计多元复合锂离子电池的SOC。与EKF相比,改进Sage-Husa的自适应卡尔曼滤波法提高了SOC估计的精度,并能够实时修正微小的模型误差带来的SOC估计误差,实时的工况模拟证明了该算法更适用于多元复合锂离子电池的动态SOC估计。     

Application of Adaptive Reduced Sigma Points Unscented Kalman Filter to the Tracking of Maneuvering Target

Based on the principle of statistical linear regression, a set of n 2 sigma points instead of 2n 1 sigma points used in the unscented Kalman filter (UKF), is constructed to approximate the system state. And filter accuracy is second order. Real-time of modified UKF is improved. In order to describe accurately the maneuvering target, the "current" statistical model is used. And the equation of acceleration error covariance is modified at every sample time of the filter. The modified adaptive UKF is presented for estimating the position, velocity and acceleration of maneuvering target. Monte Carlo simulations show the modified adaptive UKF acquires good performance for tracking position of maneuvering target. The modified adaptive UKF has better computational efficiency than UKF.     

二维修正弹双核控制系统设计

为提升二维弹道修正精度,保证修正机构的实时性和可靠性,基于DSP和STM32二维弹道修正双核控制系统设计了一款控制器,对信息进行实时处理和计算,保证弹道修正控制功能的实现。首先详细介绍了控制器总体结构及关键功能模块,并依据控制系统工作流程进行软件设计,采用无迹卡尔曼滤波(UKF)对弹道数据进行处理,提升数据测量精度。接着对修正机构进行神经网络PID控制,提升系统实时性和可靠性。最后通过实验仿真进行验证。实验结果表明,该控制系统能满足系统各项设计要求。     

液压支腿回路性能衰退预测方法研究

以某装备液压支腿回路为研究对象,分析回路常见故障机制,提取故障特征参数,基于无迹卡尔曼滤波算法,建立了双向液压锁性能衰退预测模型,构建工作指数指标,确定工作指数的异常阈值,估算双向液压锁工作寿命。基于MATLAB/Simulink模块,搭建双向液压锁仿真模型,设置装备相关作业参数,仿真双向液压锁在其寿命周期内性能随压力、温度的变化趋势。研究结果可为该元件的维修保障方案提供科学指导。     

基于“当前”统计模型的自适应不敏卡尔曼滤波算法

基于机动目标"当前"统计模型在直角坐标系下建立了三坐标雷达跟踪系统的状态方程和观测方程。针对非线性自适应滤波这一问题,提出了一种基于"当前"统计模型的自适应不敏卡尔曼滤波算法(CS-UKF),并对算法作了说明。通过计算机仿真验证了CS-UKF算法的有效性,并且该算法跟踪效果良好,精度好于基于"当前"统计模型的自适应扩展卡尔曼滤波算法(CS-EKF)算法。     

Neural network-based learning and estimation of battery state-of-charge: A comparison study between direct and indirect methodology

Faced with the ever-increasing urban environmental pollution, the electric vehicles (EVs) have received increasing attention in the automotive industry. Lithium-ion batteries, serving as electrochemical power storage, have been extensively used in EVs because of the lightweight, no local pollution and high power density. The increasing awareness on the safe operation and reliability of the battery requires an efficient battery management system (BMS), among the parameters monitored by which, state-of-charge (SOC) is critical in preventing overcharge, deep discharge, and irreversible damage. This article investigates the neural network (NN)-based modeling, learning, and estimation of SOC by comparing two different methodologies, that is, direct structure with SOC as network output and indirect structure with voltage as output. Firstly, the nonlinear autoregressive exogenous neural network (NARX-NN) is introduced, in which SOC is directly deemed as an NN output for learning and estimation. Secondly, a radial basis function (RBF)-based NN with unscented Kalman filter (RBFNN-UKF) is proposed, in which the terminal voltage is used as output. Instead, SOC is deemed as an internal state which would be estimated indirectly based on the feedback error of voltage. Experimental results demonstrate that both estimators can achieve accurate SOC estimation for regular cases, in spite of the inaccurate initial conditions. However, the direct NN structure is revealed as not capable of dealing with the cases with sensor bias, which, however, can be well accommodated in the indirect structure by extending the sensor bias as an augmented state. Benefiting from the uncertainty augmentation and feedback compensation, the indirect RBFNN-UKF shows superiority over the direct estimation in the practical experiments, depicting a promising prospect in the future onboard EV-BMS application.     

基于UKF-RSS在线建模的WSN节点跟踪定位算法

在传感器网络中（WSN）锚节点负责接收GPS定位信号,但其使用寿命受能量约束,为了提高传感器网络的生存周期和定位精度,提出基于无迹Calman滤波（UKF）和传感器网络锚节点RSS在线建模的WSN定位算法,实现高效资源管理和利用方式。该算法主要包括位置预测和目标定位两个步骤,利用UKF算法对目标节点的下一位置进行预测,选择开启距离预测位置最近的几个锚节点,关闭无用锚节点,有效降低网络能耗。利用锚节点之间相互信号强弱基于RSS对开启锚节点周围的距离与RSS信号强弱关系进行建模,降低RSS算法对环境的依赖度。实验结果表明该算法能够有效对锚节点的开启/睡眠进行管理,并可降低环境依赖性,从而实现负载均衡降低能耗和提高定位精度的效果。     

一种新的进化裂变粒子滤波算法

针对经典自举粒子滤波中的重要性函数选取和重采样所导致的样本枯竭问题,提出了一种基于进化裂变的改进粒子滤波算法．该算法首先采用无迹卡尔曼滤波算法产生重要性函数,然后对重要性采样粒子进行裂变通过进化策略更新粒子集以增加粒子多样性,从而克服经典自举滤波重采样过程中的粒子退化问题．仿真实验表明,该算法能有效地提高跟踪精度, 跟踪性能优于经典粒子滤波算法．