一种单脉冲末制导雷达抗质心干扰算法

为了提高单脉冲末制导雷达的抗质心干扰能力,结合信号到达方向（DOA）的极大似然估计法和卡尔曼滤波,提出了一种新的算法。首先,在假设目标和干扰数量、参数已知的情况下,建立雷达多次接收信号匹配滤波后跟踪回波附近的多个相邻采样点的似然函数。其次,计算得到目标和干扰信号DOA、延迟和功率的极大似然估计,并应用最小描述长度（MDL）准则判断目标和干扰数量。最后,设计了合适的卡尔曼滤波器用于正确跟踪目标。仿真结果验证了算法的有效性。     

基于频域滤波的噪声识别去噪算法

在频域滤波的基础上,对图像的高频部分进行噪声识别,区分边缘与噪声,进而通过改进的空间滤波算法实施去噪,使图像边缘细节部分得到保护,再将处理后的高频图像与其对应的低频图像进行融合,以获得完整的高质量图像.实验结果表明通过新算法处理后的图像可以达到较好的降噪效果.     

Estimation of road profile variability from measured vehicle responses

When assessing the statistical variability of fatigue loads acting throughout the life of a vehicle, the question of the variability of road roughness naturally arises, as both quantities are strongly related. For car manufacturers, gathering information on the environment in which vehicles evolve is a long and costly but necessary process to adapt their products to durability requirements. In the present paper, a data processing algorithm is proposed in order to estimate the road profiles covered by a given vehicle, from the dynamic responses measured on this vehicle. The algorithm based on Kalman filtering theory aims at solving a so-called inverse problem, in a stochastic framework. It is validated using experimental data obtained from simulations and real measurements. The proposed method is subsequently applied to extract valuable statistical information on road roughness from an existing load characterisation campaign carried out by Renault within one of its markets.     

Adhesion estimation at the wheel–rail interface using advanced model-based filtering

The railway industry in the UK is currently expanding the use of condition monitoring of railway vehicles. These systems can be used to improve maintenance procedures or could potentially be used to monitor current vehicle running conditions without the use of cost prohibitive sensors. This paper looks at a novel method for the online detection of areas of low adhesion in the wheel/rail contact that cause significant disruption to the running of a network, particularly in the autumn season. The proposed method uses a Kalman–Bucy filter to estimate the creep forces in the wheel–rail contact area; post-processing is then applied to provide information indicative of the actual adhesion level. The algorithm uses data that, in practice, would be available from a set of modest cost inertial sensors mounted on the vehicle bogie and wheel-sets. The efficacy of the approach is demonstrated using simulation data from a nonlinear dynamic model of the vehicle and its track interface.     

Lane changing intention recognition based on speech recognition models

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.     

离子迁移谱信号数字滤波和谱峰检测技术

离子迁移谱信号处理软件主要完成离子迁移谱谱信号的数字滤波和谱峰检测。本文在分析了波形谱图的去噪、滤波和谱峰检测技术的基础上,提出了防脉冲干扰移动平均值法和最小二乘法相结合的数字滤波方法,并采用增加斜率法谱峰检测技术,通过matlab仿真软件验证了数字滤波效果和谱峰检测准确度。仿真试验的结果在离子迁移谱分析仪的应用中取得了较好的效果。     

Outlier Rejecting Multirate Model for State Estimation

IntroductionMeasured data is often contaminated by noisein state estimation.Kalman filter is a powerfultool for signal extracting.It is especially efficientin estimating spatially inhomogeneous signal whenthe noise is Gaussian.Due to process noise or non-stationary environment,the measured data is usu-ally corrupted by outliers.The performance is de-graded seriously.Generally,there are two kinds ofapproaches to handle this problem.Outlier can bedetected based on renovation[1],then be replace…     

卡尔曼时间序列模型预测路面平整度指数

路面平整度的发展趋势受交通量、温度及使用时间等许多因素的影响,很难建立综合全面的预测模型。而时间序列法利用历年的IRI值可解决这个问题。针对传统的时间序列法计算的不足,提出了将卡尔曼滤波应用于时间序列IRI预测模型的方法,能充分利用观测值对状态估值进行实时修正,可有效提高预测模型的预测精度,且无需储存大量的历史观测数据。最后,通过实例证明了该模型有效可行。     

捷联惯性姿态测量系统的扩展遗忘因子递推最小二乘算法研究

在实际大型动态测量船动态变形测量应用中准确确定捷联惯性姿态测量系统噪声模型和相关性比较困难,采用常规卡尔曼滤波(KF)会导致较大的状态估计误差,甚至使滤波器发散.针对这一问题,在分析扩展遗忘因子递推最小二乘(EFRLS)算法的稳定性基础上将EFRLS估计误差与带系统相关噪声的卡尔曼滤波器估计误差进行了比较,并针对大型测量船动态环境采用EFRLS算法对变形参数进行了估计,仿真结果验证了EFRLS算法在噪声信息未知情况下的有效性.     

考虑驾驶行为异质性的城市快速路合流区仿真模型

为研究城市快速路合流区车辆运行规律，基于车辆自然轨迹数据，提出考虑驾驶行为异质性的合流区元胞自动机仿真模型。模型将合流区分为上游区域、合流区域及下游区域，3个区域由11条路段组成。首先，利用Kalman滤波算法对自然轨迹数据进行降噪处理；然后，计算每辆车驾驶行为特征参数并进行K-means聚类分析，结合聚类效果评价指标Silhouette系数将驾驶行为分为：保守-谨慎型、激进-谨慎型、保守-轻率型及激进-轻率型这4种类型；最后，依据分类结果， 建立考虑加速度、随机慢化概率异质性的跟驰模型和考虑换道安全间距、换道决策的多级异质性换道模型。在各空间占有率的情境下，基于Matlab进行数值仿真，统计同质驾驶行为和异质驾驶 行为条件下，合流区域车道的流量、密度、速度、时空位置及换道频率等参数。仿真结果表明：在空间占有率为10%~20%时，同质交通流相比异质交通流更容易产生局部交通拥堵和交通流失效情境，并且同质交通流量峰值比异质交通量小27.1%；随着空间占有率的增加，同质车辆和异质车辆驾驶频率均呈现增加-稳定-下降的趋势，而异质驾驶行为换道频率的极大值比同质交通流高 20.74%。