旁瓣消隐技术在雷达中的应用

旁瓣消隐技术（SLB）是雷达抗有源干扰的有效方法。它采用空间滤波技术，通过比较主、辅天线接收机的输出信号幅度，实现对干扰信号的抑制。介绍了旁瓣消隐的原理，建立雷达旁瓣消隐技术的数学模型。并给出基于DSP的SLB工程实施方案。与传统的对消系统相比，该旁瓣消隐系统具有更好的抗干扰性能。     

应用于列车完整性检测系统的改进型滤波器

列车完整性检测系统是新型车载设备,对防止列车抛车有重要意义。其硬件构架上以MSP430微处理器为主控制器,由ADXL202传感器实现加速度信息采集。作为判据的加速度信息在复杂的行车条件下被干扰。本文以加速度信息为研究对象,进行理论分析、数学建模和结果仿真。文中设计了一个二维的改进型滤波器。主要采用最小二乘滤波思想,选取适当的加权矩阵和遗忘因子,它不需要被估计量和观测量的统计规律,不易发散,完全符合观测数据被随机干扰,难以建立精确模型的特点。为了进一步提高滤波精度在滤波器中设计了一个野值剔除环节,在前期去掉粗大误差。对系统采集的数据进行上述的滤波,显著提高了数据的可靠性。     

合成孔径雷达图像船舶自动检测新方法

针对传统SAR图像船舶自动检测方法在检测近岸目标时存在的普适性不强、计算量大、自动识别差的缺陷,提出了一种新的迭代阈值分割算法,通过消除噪声、分离目标检测出船舶目标,对比验证该方法能自动、快速、准确地检测近岸船舶目标。     

Application of H∞ filtering in the initial alignment of strapdown inertial navigation system

In this paper , the principle of H∞ filtering is discussed and H∞ filter is constructed, which is used in the initial alignment of the strapdown inertial navigation systems(SINS). The error model of SINS is derived. By utilizing constructed H∞ filter, the filtering calculation to that system has been conducted. The simulation results of the misalignment angle are given under the condition of unknown noises. The results show that the process of alignment with H∞ filter is much faster and with excellent robustness.     

143.5 m高墩液压翻模施工技术

随着高墩施工技术的发展,特大桥建设越来越多,墩身施工采用何种方法,许多施工单位都在进行不同方式的尝试。143.5 m高墩采用液压翻模技术施工,取得了成功。详细介绍了143.5 m高墩施工工艺。     

基于扩展卡尔曼滤波的船舶纵向运动受扰力与力矩的估计

基于递推最小二乘法设计并实现海浪干扰力和干扰力矩的成型波滤器,对有色的次优卡尔曼滤波和扩展卡尔曼滤波(EKF)的效果进行对比.通过引入成型滤波器,采用EKF,提高了状态估计的精度,实现对随机海浪扰动力和力矩的估计.仿真结果表明,通过扩展卡尔曼滤波对海浪扰动力和力矩进行估计是可行的.     

机动目标滤波的最小二乘方法

基于最小二乘滤波精度分析的解析表达式，得出了最小二乘滤波器记忆长度的优化方法，提出一种目标运动加速度的在线实时估计方法，据此可实现最小二乘滤波器记忆长度的实时优化，形成一种适用于机动目标的基于最小二乘的自适应滤波方法。最后进行数值仿真，验证理论分析与算法的正确性。     

基于卡尔曼滤波算法的公交车辆行程时间预测

通过分析公交车辆的行驶特性,利用卡尔曼滤波算法建立行程时间预测模型,并用该模型预测未来时段的公交车辆路段行程时间.预测结果表明,该方法预测精度较高,可有效地改善公交车辆动态调度效果,提高交通资源利用率,因此该方法具有推广意义.     

An innovative localisation algorithm for railway vehicles

In modern railway automatic train protection and automatic train control systems, odometry is a safety relevant on-board subsystem which estimates the instantaneous speed and the travelled distance of the train; a high reliability of the odometry estimate is fundamental, since an error on the train position may lead to a potentially dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, data fusion of different inputs coming from a redundant sensor layout may be used. The aim of this work has been developing an innovative localisation algorithm for railway vehicles able to enhance the performances, in terms of speed and position estimation accuracy, of the classical odometry algorithms, such as the Italian Sistema Controllo Marcia Treno (SCMT). The proposed strategy consists of a sensor fusion between the information coming from a tachometer and an Inertial Measurements Unit (IMU). The sensor outputs have been simulated through a 3D multibody model of a railway vehicle. The work has provided the development of a custom IMU, designed by ECM S.p.a, in order to meet their industrial and business requirements. The industrial requirements have to be compliant with the European Train Control System (ETCS) standards: the European Rail Traffic Management System (ERTMS), a project developed by the European Union to improve the interoperability among different countries, in particular as regards the train control and command systems, fixes some standard values for the odometric (ODO) performance, in terms of speed and travelled distance estimation. The reliability of the ODO estimation has to be taken into account basing on the allowed speed profiles. The results of the currently used ODO algorithms can be improved, especially in case of degraded adhesion conditions; it has been verified in the simulation environment that the results of the proposed localisation algorithm are always compliant with the ERTMS requirements. The estimation strategy has good performance also under degraded adhesion conditions and could be put on board of high-speed railway vehicles; it represents an accurate and reliable solution. The IMU board is tested via a dedicated Hardware in the Loop (HIL) test rig: it includes an industrial robot able to replicate the motion of the railway vehicle. Through the generated experimental outputs the performances of the innovative localisation algorithm have been evaluated: the HIL test rig permitted to test the proposed algorithm, avoiding expensive (in terms of time and cost) on-track tests, obtaining encouraging results. In fact, the preliminary results show a significant improvement of the position and speed estimation performances compared to those obtained with SCMT algorithms, currently in use on the Italian railway network.     

Terrain-based road vehicle localisation using particle filters

This work develops a particle filter algorithm to localise a vehicle in the direction of travel without the use of GPS. The inputs to the algorithm include a terrain map of road grade, pitch measurements from an in-vehicle pitch sensor, and wheel odometry. Simulations and experiments at The Thomas D. Larson Transportation Institute test track are used to demonstrate the algorithm, observe the speed of convergence, and to determine key parameters for practical implementation. The results indicate that the method can quickly localise a vehicle with 1 m accuracy or better. Experiments over 5 km along Highway 322 in State College, Pennsylvania, were also used to demonstrate the algorithm.