Direction Sensing RFID Reader for Mobile Robot Navigation

A self-contained direction sensing radio frequency identification (RFID) reader is developed employing a dual-directional antenna for automated target acquisition and docking of a mobile robot in indoor environments. The dual-directional antenna estimates the direction of arrival (DOA) of signals from a transponder by using the ratio of the received signal strengths between two adjacent antennas. This enables the robot to continuously monitor the changes in transponder directions and ensures reliable docking guidance to the target transponder. One of the technical challenges associated with this RFID direction finding is to sustain the accuracy of the estimated DOA that varies according to environmental conditions. It is often the case that the robot loses its way to the target in a cluttered environment. To cope with this problem, the direction correction algorithm is proposed to triangulate the location of the transponder with the most recent three DOA estimates. Theoretical simulation results verify the reliability of the proposed algorithm that quantifies the potential error in the DOA estimation. Using the algorithm, we validate mobile robot docking to an RFID transponder in an office environment occupied by obstacles.     

基于混合型MUSIC算法对相干信源DOA估计

在实际通信环境中,由于传播环境的复杂性使空间中存在大量的相干信号,从而导致信源协方差矩阵的秩亏缺。为使得矩阵的秩恢复到等于信号源数并解决相干信源波达方向（direction of arrival ,DOA）估计问题,提出了一种混合型MUSIC算法。该算法通过前后向空间平滑技术对天线阵列进行预处理,并将得到的新协方差矢量矩阵应用于改进的IMUSIC算法进行信号数据处理分析,得到相干信号的DOA角度估计。仿真结果表明,在信噪比低的情况下,信号间隔很小且存在相关信号时,混合型MUSIC算法能准确地估计出信源的DOA,验证了该算法的高分辨率和高性能。     

Efficient geo-location estimation system using two-dimensional MUSIC algorithm

In this paper, we propose an efficient two-dimensional geo-location estimation system for mobile social security robots, where the location is obtained by direction of arrival (DOA) and time of arrival (TOA) of the radio signal. The proposed system requires only one reference signal while the conventional systems generally demand more than three reference signals. For estimating TOA and DOA information simultaneously, we employ a two-dimensional multiple signal classification (2-D MUSIC) algorithm. In addition, the performance analysis of proposed system is provided in terms of location accuracy and computational complexity by comparing it with two-dimensional Matrix Pencil (2-D MP) algorithm. The simulation results show that the proposed geo-location estimation system achieves the positioning accuracy within 3 meters in 2 kilo-meters coverage which is usable for mobile robots.     

一种新的应答着陆系统测角算法

应答着陆系统是一种基于应答机的精密进近着陆引导系统,通过询问并接收飞机应答机的应答信号来估计飞机的方位、仰角和距离等信息;文章分析了应答着陆系统传统的测角算法原理、误差来源和影响因素;通过扩大不模糊角度测量的范围,利用虚拟技术实现了系统天线阵列的虚拟均匀排列;提出了基于MUSIC算法的空间平滑技术进行多目标飞机角度估计的设想;通过计算机仿真对新测角算法的估计能力,成功概率以及精度的分析,验证了新算法的可行性和有效性。     

纯方位角目标跟踪及移动平台可观性控制方法

为了解决未知环境下的单目视觉移动机器人目标跟踪问题,提出了一种将目标状态估计与机器人可观性控制相结合的机器人同时定位、地图构建与目标跟踪方法。在状态估计方面,以机器人单目视觉同时定位与地图构建为基础,设计了扩展式卡尔曼滤波框架下的目标跟踪算法;在机器人可观性控制方面,设计了基于目标协方差阵更新最大化的优化控制方法。该方法能够实现机器人在单目视觉条件下对自身状态、环境状态、目标状态的同步估计以及目标跟随。仿真和原型样机实验验证了目标状态估计和机器人控制之间的耦合关系,证明了方法的准确性和有效性,结果表明:机器人将产生螺旋状机动运动轨迹,同时,目标跟踪和机器人定位精度与机器人机动能力成正比例关系。     

多波束天线通道幅相误差的自校正算法

多波束天线利用多信号分类(MUSIC)算法对目标参数进行精确估计时,存在多波束天线的通道幅度和相位误差的失配现象,使MUSIC算法的性能严重下降;针对基于MUSIC的数字波束通道不一致问题,给出了一种新的基于MUSIC算法的最小化代价函数的波束无源自校正算法,利用阵列结构的先验知识对接收数据进行预处理,得到校正矩阵后自校正,并根据多波束阵列天线形成网络天线实测参数,加入随机幅度和相位误差进行计算机模拟仿真,验证仿真算法的正确性。     

基于均匀圆阵的改进UCA-ESPRIT算法

波达方向(DOA)估计是阵列信号处理的研究热点和难点之一。基于阵列天线, 利用空间谱估计理论进行DOA估计,提出一种改进算法——I-UCA-ESPRIT。首先根据均匀圆阵(UCA)的中心对称性,将输入信号进行重新排列;然后将重新排列的信号进行实值转换,并对相关矩阵进行特征值分解;最后引入SVD算法,以求解来波信号的方位角和俯仰角。仿真结果表明,该算法不仅适用于DOA估计的非相干信号和相干信号,且优于UCA-RB-MUSIC算法和UCA-ESPRIT算法。     

一种V型阵列的二维波达方向估计的新方法

波达方向估计是阵列信号处理的一个重要研究方向,在雷达、通信、声纳、地震勘测等领域都有着广泛的应用前景.它已成为阵列无源探测和智能天线中的关键技术.针对二维信号,本文研究一种基于V型阵二维波达方向估计的新算法.该算法根据阵列结构的特点形成多个需要的相关矩阵,构造一个特殊大矩阵并经特征分解获得信号子空间的估计,最后利用2D-ESPRIT方法实现二维角度估计,可以解决β角兼并信号的波达方向估计问题,无需谱峰搜索且信号参数自动配对.最后用计算机仿真验证了该算法的有效性.     

Joint direction of arrival estimation and array calibration for coprime MIMO radar

Compared to large-scale MIMO radar, coprime MIMO radar can achieve approximate estimation performance with reduced antenna number. In this paper, joint direction of arrival (DOA) estimation and array calibration for coprime multiple-input multiple-output (MIMO) radar is considered, and an iterative method for the estimations of DOA and array gain-phase errors is proposed. Based on the received data structure of coprime MIMO radar, trilinear decomposition is firstly adopted to obtain the estimations of transmit and receive direction matrices, which are perturbated by the gain-phase errors. Through equation transformation, the un-perturbated direction matrices and gain-phase errors can be iteratively updated based on Least squares (LS). Finally, the unique DOA estimation is determined from the intersection of transmit and receive direction matrices. The proposed algorithm achieves better DOA estimation and array calibration performance than other methods including estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm, multiple signal classification (MUSIC)-like algorithm and joint angle and array gain-phase error estimation (JAAGE) method, and it performs close to the method with ideal arrays. Multiple simulation results verify the algorithmic effectiveness of the proposed method.     

基于改进块稀疏贝叶斯学习算法的波达方向估计

针对传统的基于稀疏表示的DOA估计算法单纯利用信号的空域稀疏性,导致在低信噪比时稀疏性能变差,影响信号稀疏重构效果的问题,使用分块稀疏理论对信号进行稀疏分解。随着目标增多及作战任务改变,DOA估计往往呈现目标群测向的特点,为了能够更好地利用信号的结构特征和统计特征,提出了基于空时联合的块稀疏DOA估计算法,使用块稀疏理论挖掘信号的内部结构,充分利用了信号的块内稀疏性和块间相关性,提高稀疏重构性能,进而对DOA估计效果有很大的提升。仿真实验表明,相比于经典的DOA方法,本方法有更好的估计效果。     

Direction of arrival estimation for smart antenna in multipath environment using convolutional neural network

This article aims to present a novel direction of arrival (DOA) estimation strategy for smart antenna in multipath environment. The smart antenna is composed of 2 main parts: the DOA estimator and the switched‐beam system. In the first part, a DOA estimation method based on convolutional neural network (CNN) has been implemented. The CNN is capable to select the desired radiation beams of the switched‐beam antenna without knowing the number of source signals coming from different directions, and in the case of noncoherent and coherent signals. Simulation results have been presented to show the effectiveness of the proposed intelligent approach.     

Obstacle arrangement detection using multichannel ultrasonic sonar for indoor mobile robots

In the last few years, mobile robot systems that perform complicated tasks have been studied. To work in complicated environments, the robot has to avoid collisions with obstacles. Therefore the robot needs to detect the arrangement of any surrounding obstacles. We considered a simple distance estimation algorithm using ultrasonic sonar. Since the algorithm was able to estimate distance accurately, we also attempted stereo reception using two ultrasonic microphones. The stereo reception sonar was able to detect the direction of obstacles. In order to make precise measurements, we attempted to use the signal coherence of ultrasonic waves. In order to install a small system into mobile robots and to detect any surrounding obstacles, we designed a multichannel sonar signal processing system using a high-performance embedded microcontroller. This article describes our ideas for the distance estimation algorithm for ultrasonic sonar, and a design for a signal processing system using a high-performance microcontroller.     

一种实用的空间平滑ESPRIT 算法

针对移动通信环境中多径传播的信号到来方向估计问题，结合空间平滑技术，提出一种空间平滑的ESPRIT算法，并给出了多径信号数目的推定方法．仿真结果证明，与ESPRIT算法相比，所提出的算法不仅适用于独立信号源，而且适用于相关信号源，具有分辨率高、计算量小等特点。     

空间特征的累量域估计算法及其在智能上行多波束形成中的应用

在智能天线系统中,空间特征估计是实现空分多址的关键问题之一。本文提出了一种基于累量的多用户空间特征估计算法,并将该算法用于智能天线中智能上行的实现。该算法既不依赖于信号的的具体结构也不依赖于信道的模型假设,构造了累量域空间特征矩阵,通过对空间特征矩阵的特征分解得到各用户信号的空间特征估计。并以此为基础,设计了用于上行多波束成形的空间滤波器组,实现了上行多用户信号的分离。仿真实验表明,空间滤波器的响应可用于多用户相干源的波达方向估计。     

基于迁移学习的移动机器人单帧图像坡度检测算法

针对未知环境下移动机器人平稳上坡控制对坡度感知精度的要求,本文提出了一种基于迁移学习的移动机器人单帧图像坡度检测算法.利用室内图像标准数据集训练深度卷积神经场-全连接超像素池化网络(deep convolutional neural field-fully connected superpixel pooling ne...     

一种改进的基于定向天线的移动传感器网络定位算法

针对在无线传感器网络定位中,由于全向天线信号受环境影响而造成定位误差较大的问题,提出了一种改进的基于定向天线的移动无线传感器网络定位算法(DADLP),使用一个带有定向天线的移动锚节点广播位置信息,未知节点接收到信号后,将接收功率平均分为若干等级,缩小定位估计区域.仿真结果表明DADLP算法相对于Ssu、BLI、GGDI和RROI移动锚节点算法提高了定位精度,并且在非规则环境下有更低的平均定位误差.     

State estimation of mobile robot using GPS signal that includes reflected and diffracted waves

We propose a method to improve the state estimation accuracy of mobile robots placed near high-rise buildings using the statistical property of the reflected and diffracted waves of global positioning system (GPS) signals. First, it is assumed that a GPS signal that contains a reflected and diffracted wave is denoted by the sum of the true position information and noise that follows a time-varying Gaussian distribution. On the basis of this assumption, the time-varying bias of a GPS signal is tracked using a Kalman filter. In addition, a particle filter, which executes sampling and likelihood evaluation using the estimated bias, is developed. With the proposed method, a GPS signal that contains the rejected noise introduced by the conventional method can be used efficiently, and the state estimation accuracy of the robot in a shadow area of GPS satellite can be improved. Furthermore, a control system for an autonomous mobile robot incorporating the proposed state estimation mechanism is developed, and its effectiveness is evaluated via simulation.     

Reinforcement based mobile robot navigation in dynamic environment

In this paper, a new approach is developed for solving the problem of mobile robot path planning in an unknown dynamic environment based on Q-learning. Q-learning algorithms have been used widely for solving real world problems, especially in robotics since it has been proved to give reliable and efficient solutions due to its simple and well developed theory. However, most of the researchers who tried to use Q-learning for solving the mobile robot navigation problem dealt with static environments; they avoided using it for dynamic environments because it is a more complex problem that has infinite number of states. This great number of states makes the training for the intelligent agent very difficult. In this paper, the Q-learning algorithm was applied for solving the mobile robot navigation in dynamic environment problem by limiting the number of states based on a new definition for the states space. This has the effect of reducing the size of the Q-table and hence, increasing the speed of the navigation algorithm. The conducted experimental simulation scenarios indicate the strength of the new proposed approach for mobile robot navigation in dynamic environment. The results show that the new approach has a high Hit rate and that the robot succeeded to reach its target in a collision free path in most cases which is the most desirable feature in any navigation algorithm.     

基于稀疏表示的OFDM信号的DOA估计

针对正交频分复用（OFDM）, 宽带信号波达方向(DOA)估计问题, 提出一种基于宽带信号协方差矩阵稀疏表示的DOA估计方法。该方法是在协方差矩阵主对角线下对左下角三角形元素按各条对角线取平均值后形成一个新的向量, 然后将该向量写成冗余字典形式。在冗余字典下对信号进行稀疏性约束形成二阶锥约束优化问题, 再用工具箱SeDuMi来实现DOA估计。理论分析和仿真结果表明, 该方法在低信噪比和少快拍数下分辨率很高, 是一种有效的宽带信号DOA估计算法, 此方法优于基于高阶累积量算法和宽带聚焦算法的DOA估计方法。