FM radio for indoor localization with spontaneous recalibration

The position of mobile users has become highly important information in pervasive computing environments. Indoor localization systems based on Wi–Fi signal strength fingerprinting techniques are widely used in office buildings with an existing Wi–Fi infrastructure. Our previous work has proposed a solution based on exploitation of a FM signal to deal with environments not covered with Wi–Fi signal or environments with only a single Wi–Fi access point. However, a general problem of indoor wireless positioning systems pertains to signal degradation due to the environmental factors affecting signal propagation. Therefore, in order to maintain a desirable level of localization accuracy, it becomes necessary to perform periodic calibrations of the system, which is either time consuming or requires dedicated equipment and expert knowledge. In this paper, we present a comparison of FM versus Wi–Fi positioning systems and a combination of both systems, exploiting their strengths for indoors positioning. We also address the problem of recalibration by introducing a novel concept of spontaneous recalibration and demonstrate it using the FM localization system. Finally, the results related to device orientation and localization accuracy are discussed.     

基于SLAM的虚实配准方法研究

研究和实现了一种基于SLAM技术的虚实配准方法,通过将SLAM分离为定位和构图,分别利用改进的pf做位姿估计和ukf进行位置估计,进而实现整个场景内目标的位姿计算,最终完成虚实配准。一定程度上解决了传统方法中存在的视域受限问题,并同时支持户内和户外应用,提高了增强现实系统的可用性。     

一种改进的室内无线定位算法

为提高室内无线定位精度并降低算法复杂度,提出一种改进的室内无线定位算法,包含2种分支算法。以视距路径数为标准,将室内无线环境划分为非视距污染较轻和较重2种环境。对污染较轻的环境,采用改进的Fang-Taylor级联算法;对污染较重的环境,采用新的迭代定位算法。仿真实验结果表明,该算法能达到甚至超过同类算法的定位精度,且适应多种室内无线环境,提高对环境的鲁棒性。     

PIRF-Nav 2.0: Fast and online incremental appearance-based loop-closure detection in an indoor environment

This paper presents a fast and online incremental solution for an appearance-based loop-closure detection problem in a dynamic indoor environment. Closing the loop in a dynamic environment has been an important topic in robotics for decades. Recently, PIRF-Nav has been reported as being successful in achieving high recall rate at precision 1. However, PIRF-Nav has three main disadvantages: (i) the computational expense of PIRF-Nav is beyond real-time, (ii) it utilizes a large amount of memory in the redundant process of keeping signatures of places, and (iii) it is ill-suited to an indoor environment. These factors hinder the use of PIRF-Nav in a general environment for long-term, high-speed mobile robotic applications. Therefore, this paper proposes two techniques: (i) new modified PIRF extraction that makes the system more suitable for an indoor environment and (ii) new dictionary management that can eliminate redundant searching and conserve memory consumption. The results show that our proposed method can complete tasks up to 12 times faster than PIRF-Nav with only a slight percentage decline in recall. In addition, we collected additional data from a university canteen crowded during lunch time. Even in this crowded indoor environment, our proposed method has better real-time processing performance compared with other methods.     

动态节点质心定位改进算法

为降低无线传感器网络的定位误差,提高动态节点的定位精度和定位覆盖度,使节点定位能够应用于动态环境下,基于传统的定位算法,提出了一种新的动态节点定位改进算法。该算法通过未知节点接收、保存的分组信息来循环组成虚拟三角形,同时依靠内点测试方法来判断未知节点自身位置,最后根据质心算法来进行最终定位。将仿真结果与传统算法进行比较,结果表明,改进算法可以大大提高无线传感器网络的定位精度和覆盖度。     

基于信息势能的鲁棒估计器及其在同时定位与地图构建问题中的应用

提出了一种基于信息势能鲁棒估计器来解决机器人室内的同时定位与地图构建（SLAM）问题．结构化的室内环境可以用线段近似表示．然而动态环境中,测距传感器测得的数据通常湮没在大量的噪声信号中．本文采用“分割与合并”（split—and．merge）方法进行线段的分类,根据信息势能的性能指标衡量每个采样数据对该线段的信息贡献量．按照信息优化理论设计估计器,选择信息量贡献大的样本点作为信息内点提取线段参数,构建局部地图．采用粒子滤波器进行地图及机器人路径的更新．采用递推的方法估计信息势能,降低了对样本点的信息量贡献做估计时的复杂度．仿真和实验结果证明,本文所提出的方法具有较强的鲁棒性,提高了SLAM策略的准确性和实时性．     

基于TMS320DM642麦克风阵列声源定位系统

麦克风声源定位是利用麦克风阵列拾取语音信号,并用数字信号处理技术对其进行分析和处理的声源定位技术.在麦克风阵列声源定位中,语音信号端点的拾取是重要的环节.语音端点检测是对接收到的信号利用端点检测算法分析,以确认麦克风阵列中语音信号到达的端点;并利用麦克风阵列中各麦克风接收到的语音信号的端点的先后,计算出麦克风阵列接收的...     

无线传感器网络节点自身定位算法研究

无线传感器网络中节点自身位置信息是实现其应用的最重要因素之一.针对节点自身定位的研究很多,但很大一部分都集中在算法的改进和新算法的开发上,很少给出纵向直观的仿真效果比较.研究选取无需测距算法中4种典型的算法,给出它们在不同参数下的仿真定位效果图,对于工程人员根据实际选取最优算法有很好的借鉴作用.在此基础上,利用曲线拟合...     

基于图像块等级模型的多重认证水印算法

提出了一种新的用于灵活图像认证的多重水印嵌入算法.不同于传统的块独立水印算法中每个图像块只嵌入一个水印信息,算法对每个图像块嵌入多重水印信息.提出了两个通用的图像块等级模型,形成图像块内部的等级结构,对每个图像块以及图像块内部的各等级子块进行独立的水印生成和嵌入.将图像特征值映射为混沌系统的初值,并将图像块的编号映射为混沌系统的迭代次数,经过混沌迭代生成图像块水印,再将水印信号替代图像块中选定像素点的最低有效位,完成水印的嵌入.实验结果表明,该算法可对图像进行多重认证,对篡改区域进行精确的检测与定位,并能选择不同的定位精度.     

量子逻辑电路中控制点失效错误的定位

确保可逆电路的正确性与可靠性,错误检测必不可少,错误定位难度更高.通过分析发现当可逆电路中规模为k的可逆门发生控制点失效时仅对2<'n-k>个输入向量的输出产生影响,据此给出了一种把当前错误集分成若干个子集的方法生成控制点失效错误定位树.传统的错误定位方法都是通过生成真值表和错误表来产生错误定位树;该方法不需要生成和存储真值表以及错误表就能够有效定位电路中控制点失效错误.与Rfault算法相比,空间复杂度和时间复杂度更小,算法效率更高,能应用于更大规模的电路.