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。     

滤波方法在反舰导弹高度测量系统中的应用

为了得到更加准确的导弹飞行高度信息,通过组合无线电高度表和加速度计建立一种具有较高精度的导弹高度测量系统。首先对各传感器的特性进行分析;其次,根据传感器测量模型,采用噪声淹没技术建立导弹高度测量系统的状态方程和量测方程;最后,用卡尔曼滤波的方法进行仿真实验,得到高度估计。仿真结果表明,相比用单个传感器的高度测量方式,该方法具有更高的精度。     

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.     

基于扩展卡尔曼滤波的船舶电力推进系统仿真

针对船舶电力推进系统中采用传统直接转矩控制存在磁链和转矩脉动较大,以及速度传感器的使用降低了系统可靠性,增加了系统成本等问题.提出采用扩展卡尔曼滤波器精确估计电机的定子磁链和电机转速,间接估计转矩,从而实现永磁同步电机的无速度传感器直接转矩控制.保持了直接转矩控制的转矩快速响应和系统鲁棒性强的优点,降低磁链和转矩脉动,减小了系统因电机参数的变化和负载扰动带来的影响.仿真结果表明,基于扩展卡尔曼滤波的船舶电力推进系统具有良好的转速和转矩控制性能.     

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.     

Road roughness estimation based on discrete Kalman filter with unknown input

ABSTRACT

The road roughness acts as a disturbance input to the vehicle dynamics, and causes undesirable vibrations associated with the ride and handing characteristics. Furthermore, the accurate measurement of road roughness plays a key role in better understanding a vehicle dynamic behaviour and active suspension control systems. However, the direct measurement by laser profilometer or other distance sensors are not trivial due to technical and economic issues. This study proposes a new road roughness estimation method by using the discrete Kalman filter with unknown input (DKF-UI). This algorithm is built on a quarter-car model and uses the measurements of the wheel stroke (suspension deflection), and the acceleration of the sprung mass and unsprung mass. The estimation results are compared to the measurements by laser profilometer in-vehicle test.     

全球定位系统和全球卫星导航系统伪距综合法

论述了综合GPS、GLONASS伪距测量值获取定位信息的滤波方法。GPS、GLONASS的坐标系、时间系统、工作频率不同，不同卫星的方位、高度各不相同，因而其测距的系统误差也各不相同，通常的方法是用GPS和GLONASS的钟差2个参数来描述不同系统卫星的系统误差，需用5颗卫星来定位，且精度受限。采用扩展状态维数，对不同卫星的系统误差进行在线估计的滤波方法，可以有效地改进卫星定位的精度。同时既可以用多星定位，充分利用测量信息，也可在任一系统的l颗卫星可用时，给出测者的最优估计位置信息，改进了双星系统的导航性能。     

大型船舶航迹多变量随机最优控制

基于随机控制理论，研究了大型船舶航迹的随机最优控制。以Mariner号船舶为研究对象，建立了该船的运动方程和干扰模型，在此基础上，利用卡尔曼滤波器对航向角Ψ、舵角δ、船位信息(经纬度)等进行最优估计，采用LQG随机控制方法对航迹进行随机最优控制。仿真结果表明航迹控制性能良好，能精确保持航迹。