MIMO-OFDM时变信道修正KALMAN跟踪

在多输入多输出正交频分复用系统(MIMO-OFDM)中,双选择性时变信道的跟踪对系统性能很关键.在接收端未知移动台运动速度的情况下,经典KALMAN跟踪算法的状态方程转移系数没法确定.文中提出的修正KALMAN滤波,能够同时跟踪时变信道和状态转移系数.通过计算机仿真试验表明,跟踪算法在高信噪比时,系统性能略低于已知信道状态转移系数算法的性能;算法在低信噪比时,系统性能优于已知信道状态转移系数算法的性能.     

Exact non-linear Bayesian parameter estimation for autonomous compliant motion

This paper presents theoretical and experimental results for the estimation of large position and orientation inaccuracies during force-controlled compliant motion. This is a significant improvement over previous results. The estimation is based on position, velocity and force measurements. For large position and orientation inaccuracies, the non-linear estimation problem is not satisfactorily solved by existing Kalman filters. Therefore, a new Bayesian estimator is derived. The filter is derived independently of our application and is valid for static systems (parameter estimation) with any kind of non-linear measurement equation subject to Gaussian measurement uncertainty and for a limited class of dynamic systems. Experimental results for the estimation of the inaccurately known positions and orientations of contacting objects during autonomous compliant motion are presented.     

A Novel EKF-Based Recovery in DS Scheme for DS/CDMA Systems Tracking Code Delay

Classical delay-lock loops (DLL) have beenwidely considered for code synchronization purposes inDS/CDMA systems, although they have not been devised foroperation when channel fading is present. This paper describes a new code synchronizationscheme based on a previously proposed extended Kalmanfilter (EKF) approach. The scheme proposed in this paperis able to operate under low signal-to-interference ratios (SIR), usual at the receiver input incellular CDMA mobile environments, and it outperformsthe behavior of previously proposed EKF-based schemes,which failed in such environments. Performance results under realistic mobile environment conditionsare shown in terms of the mean time to lose lock (MTLL)and the tracking error variance ² for a wide range ofSIR values and under Rayleigh fading. Moreover, the Cramer-Rao lower boundon ² is also computed inorder to validate the results obtained viasimulations.     

General Feature Extraction for Mapping and Localization of a Mobile Robot Using Sparsely Sampled Sonar Data

This study introduces a method of general feature extraction for building a map and localization of a mobile robot using only sparsely sampled sonar data. Sonar data are acquired by using a general fixed-type sensor ring that frequently provides false returns on the locations of objects. We first suggest a data association filter that can classify sets of sonar data that are associated with the same hypothesized feature into one group. A feature extraction method is then introduced to decide the exact geometric parameters of the hypothesized feature in the group. We also show the possibility of extracting a circle feature consistently as well as a line or a point feature by using the proposed filter. These features are then assembled to build a global map and applied to extended Kalman filter-based localization of the robot. We demonstrate the validity of the proposed filter with the results of mapping and localization produced by real experiments.     

A Localization Algorithm for Autonomous Mobile Robots via a Fuzzy Tuned Extended Kalman Filter

The capability to acquire the position and orientation of an autonomous mobile robot is an important element for achieving specific tasks requiring autonomous exploration of the workplace. In this paper, we present a localization method that is based on a fuzzy tuned extended Kalman filter (FT-EKF) without a priori knowledge of the state noise model. The proposed algorithm is employed in a mobile robot equipped with 16 Polaroid sonar sensors and tested in a structured indoor environment. The state noise model is estimated and adapted by a fuzzy rule-based scheme. The proposed algorithm is compared with other EKF localization methods through simulations and experiments. The simulation and experimental studies demonstrate the improved performance of the proposed FT-EKF localization method over those using the conventional EKF algorithm.     

MIMO-OFDM时变信道修正KALMAN跟踪

研究了在多输入多输出正交频分复用系统(MIMO-OFDM)中，时变信道的跟踪。在实际的通信系统中，接收端并不知道基站与移动台的相对运动速度，就不能得知时变信道多普勒频移的值，也就不知道时变信道构成的状态矢量的转移系数。普通的KALMAN跟踪算法，只能在假定的状态转移系数下对时变信道进行跟踪。本文提出的修正KALMAN滤波，能够同时跟踪时变信道以及时变信道构成的状态矢量的转移系数。     

CAMSHIFT结合HOMOGRAPHY变换的算法

提出了一种基于CAMSHIFT算法结合HOMOGRAPHY变换的球员轨迹提取算法。在求取球员位置的过程中,CAMSHIFT算法通过球员的颜色直方图模型实现对球员的跟踪。在跟踪过程中引入KALMAN滤波进一步提高算法的跟踪效率。最后利用HOMOGRAPHY变换将场景视频帧转换为俯视投影图,真实反映球员在球场的位置信息。实验结果表明该算法能够较好的提取出球员的运动轨迹。     

基于数字锁相环的晶振频率同步模块设计

为满足现代通信技术、雷达技术、电子测量以及光电应用领域对高稳定度高准确度时钟的要求,设计了一种基于数字锁相环的晶振同步系统.系统以基于FPGA数字延迟线的高分辨率鉴频鉴相器以及在MicroBlaze核中实现的卡尔曼数字环路滤波器为核心,通过16 bit DAC微调本地晶振振荡频率,使其同步于GPS秒脉冲,从而获得了高准...     

Terrain-Based Navigation for Underwater Vehicles Using an Ultrasonic Scanning System

In this paper an approach to the field of outdoor robotic navigation with a focus on underwater simultaneous localization and mapping (SLAM) is proposed that utilizes ultrasonic scanning images. Experimental results from the implementation of a SLAM algorithm with real data are presented. The projected landmark detection process constructs a map of the environment and generates navigation estimates based on an adaptive delayed nearest-neighbor algorithm. The feature extraction and validation processes are resolved at the sensor level using a simple local maximum-level detection algorithm on the range data. This paper presents experimental results from our research efforts in the above area, using data from water tank trials and a remotely operated vehicle operating in a shallow water environment.     

基于KALMAN滤波和深度学习的机器人飞拍方法研究

针对传统机器人飞拍系统存在视-控分离,导致系统实时性和效率不高的问题,提出视-控一体的设计方案,并对机器人飞拍的相关算法展开研究。为提高系统的实时性,操作系统采用基于Windows的硬实时系统,机器人控制模块和视觉模块在同一操作系统里通过共享内存的方式实现数据交互;基于改进型卡尔曼滤波算法,提出一种无传感器的精准时间触发算法,减小相机触发时机器人在同一位置的重复度误差;结合深度学习算法,提高图像处理的处理速度,保证在机器人下一节拍前完成纠偏值的计算,最终实现精准的机器人飞拍定位。实验表明:较之传统视控分离方案,该机器人飞拍纠偏系统具有简捷易用、精度和效率高等优势。