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Yang Zhao Zheng Gao Yongtao Ma Yangguang Zang Jianfu Teng Kaihua Liu 《International Journal of Wireless Information Networks》2018,25(1):72-78
Pattern matching localization algorithms are commonly proposed in complex indoor environments. Received signal strength indicator (RSSI) of radio signals backscattered from passive ultra high frequency radio frequency identification (RFID) tags is commonly used as the pattern in matching process. Considering RSSI is severely affected by multipath propagation and noise, etc., more types of patterns will be a better choice for further study. In recent years, phase information is becoming more and more important for RFID-based systems. As for the period ambiguity problem of wrapped phase information, phase difference of arrival (PDOA) of double-frequency signals can be utilized as a type of pattern instead of phase of arrival corresponding to the propagation distance. In this paper, we combine RSSI and PDOA as a brand new type of pattern named RP. In previous study, only localization accuracy is considered to prove effective. It is hard to show the matching performance of the type of pattern with the following process of localization algorithms. Here we take advantage of the linear relationship between pattern Euclidean distance and geographic locations Euclidean distance of tags fixed in reference points to evaluate the distinctiveness performance of RP in matching. The linear correlation of the model is tested at each reference point with experimental data captured from indoor scenario. By evaluating these models statistically, we think RP is better than RSSI and PDOA as a pattern. 相似文献
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基于接收信号强度指示(received signal strength indication, RSSI)测距的研究和应用领域很广泛,一直是物联网研究的热点. 为降低传统基于反向传播(back propagation,BP)神经网络的RSSI测距误差,文中提出一种基于K-means聚类算法对样本数据进行预处理的BP神经网络测距算法,来解决由于RSSI值衰减程度不同引起的不同距离区间RSSI值和真实距离之间映射关系不均匀的问题. 将K-means聚类算法应用于BP神经网络模型中,对样本数据进行距离区间划分,然后将已经分类好的数据分别输入BP神经网络建立网络模型并进行实验仿真. 结果显示:传统基于BP神经网络的RSSI测距算法的均方根误差为1.425 7 m;而经过K-means算法改进后的BP神经网络测距算法的均方根误差为1.288 7 m,降低了测距误差,并优化了目标RSSI值与真实距离的映射关系. 相似文献
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定位技术是无线传感器网络数据采集的基础服务,而定位精度的高低在很大程度上取决于距离测量的精度。基于RSSI(接收信号强度)测距技术无须添加任何硬件设施、用较少的通信开销和较低的实现复杂度,十分适应于能量受限的无线传感器网络。通过对RSSI测距模型进行分析,并提出一种针对室内环境的参数修正方案。通过自行研发的传感器节点Ubicell上进行验证分析,实验表明,采用环境参数修正方案后,明显提高了测距的精度。 相似文献
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针对接收信号强度指示(Received Signal Strength Indication,RSSI)定位模型易受环境影响导致测距误差较大的问题,提出了采用天牛须搜索(Beetle Antennae Search,BAS)优化后向传播(Back Propagation,BP)神经网络拟合测距模型,克服了对数衰减模型易受环境干扰、参数取经验值等问题。首先,利用卡尔曼滤波对RSSI值进行校正,将校正后的数据输入BAS-BP网络拟合出测距模型并通过测距模型输出距离值;然后,利用极大似然估计法求解未知节点的坐标。实验结果表明,与BP模型和粒子群优化的BP模型相比,改进方法收敛速度快,定位精度提高更加明显。 相似文献
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基于RSSI修正的改进DV-Hop测距算法 总被引:2,自引:1,他引:1
针对DV-Hop算法测距误差受节点分布不均匀影响较大的问题和RSSI算法受环境因素影响较大的问题,提出了一种基于RSSI修正的改进型DV-Hop测距算法。该算法在保持DV-Hop算法环境适应性强的优点的同时,有效降低了由于节点分布不均而引起的测距误差,从而提高了定位精度。仿真结果表明该修正算法在不同的锚节点数和不同的通信半径下,均能够有效降低测距误差;同时该算法的定位精度与原DV-Hop算法以及另外3种以DV-Hop为基础的改进型算法相比均得以提高。 相似文献
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In complex environment, issues such as reflection, multipath propagation, non-line of sight and antenna gain, etc. would result in significant propagation losses as for the same distance. In order to effectively reduce ranging error and location error caused by received signal strength indication (RSSI) measurement distance, a location algorithm based on chaos particle swarm optimization ranging (CPSOR) is proposed for indoor location and navigation applications. By setting reference beacon nodes within location region, the relationship between distance and RSSI which is measured from target node to each beacon node is automatically corrected, and RSSI ranging error is effectively reduced, thus the objective of improving location accuracy is achieved. Numerical results show that the processing time of CPSOR location algorithm is reduced by 62% and the location accuracy of CPSOR is improved by 72% in contrast that of back propagation (BP) neural network location algorithm. Besides, practicality experiment results show that when the distance between beacon nodes is 50 m, the average location error of CPSOR location algorithm is 1.21 m and the location error of BP location algorithm is 3.36 m, thus the location accuracy is improved by 63%. 相似文献
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针对目前对高精度室内定位算法的需求,提出一种基于接收信号强度识别(RSSI)和惯性导航的融合室内定位算法。基于无线传感网中ZigBee节点的RSSI值,采用位置指纹识别算法,对网络中的未知节点进行定位。结合惯性传感单元(IMU)提供的惯性数据,对RSSI定位结果进行融合修正。利用Kalman滤波器,采用状态方程描述待定位节点位置坐标的动态变化规律,从而实现一种以无线传感网络定位为主、IMU为辅的融合定位方法。仿真结果表明,提出的融合定位算法既能改善单独使用RSSI定位受环境干扰较大的问题,又能避免单独使用惯性导航带来的累积误差,极大地提高了定位精度。 相似文献
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无线传感器网络混合定位技术研究 总被引:1,自引:0,他引:1
在大规模复杂无线传感器网络中往往采用多种节点定位技术,在此结合现有无线传感器定位技术的现状,提出了一种混合定位技术以实现不同定位方法之间的互补。一方面利用RSSI定位弥补TDOA定位覆盖范围小的缺点;另一方面将测距信息引入到非测距定位DV—Hop算法中,用RSSI测距模型来提高DV-Hop算法中定位节点与信标节点间有效距离的精度。实验结果表明,该混合定位技术实现了TDOA,RSSI以及DV-HOP等定位技术的融合,有效地提高了复杂大规模无线传感器网络的节点定位精度。 相似文献
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将机器人作为无线传感器网络的(WSN)的移动节点,可实现节点动态自定义部署并扩大其监测范围。建立了接收信号强度(RSSI)势场量化的坐标系描述机器人状态及导航空间,有效避免将RSSI值转换为距离时带来的模型误差。由处于机器人可通信区域内的若干信标节点组成一个分布式导航网络,每个节点都会对机器人做出独立的导航决策,最后由决策控制中心融合各信标节点的输出决定机器人的航向。采用分布式处理技术,绝大部分导航信息数据处理都由信标节点完成,因此很大程度上简化了机器人的设计和硬件成本,仿真和现场实验都表明该系统的有效性。 相似文献
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A new approach to the design of IWSR systems is proposed in this paper. This involves a dynamic matching strategy based on the nature of the input speech segment. This is called signal-dependent matching. The computational complexity in the implementation of the proposed algorithm is significantly reduced by adopting a two stage approach in matching. In the first stage, the warping path between the test utterance and a reference utterance is determined. In the second stage, the distance between the utterances is computed along the path. There will be a slight degradation in the performance of a two stage approach as compared to the single stage approach, but this can be tolerated in view of the significant computational advantage. The performance degradation is more than compensated by the signal-dependent matching strategy in the second stage. To measure the improvement in the recognition performance, a new index of performance is defined, that reflects the characteristics of the distance matrix for a given vocabulary, rather than the characteristics of the confusion matrix. The performance of the signal-dependent matching algorithm is significantly better than the standard dynamic time warping matching algorithm for confusable as well as nonconfusable vocabulary.We also develop a signal-dependent matching algorithm, which takes into account some distortions in the input speech. As an example we offer the agorithm twice the same test utterance, once undistorted, once after a distortion. Our research until now indicates a improvement in automatic isolated word speech recognition systems while using signal-dependent parameter measuring and signal dependent matching. 相似文献
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《Mechatronics》2014,24(1):12-22
Tendon-sheath mechanism has “revolutionized” the use of flexible endoscopic systems, by its many advantages of high maneuverability, lightweight, low cost, and simple design. However, nonlinear phenomena such as friction and backlash hysteresis present major challenges for motion control of the tool tips. This paper introduces a new mathematical model and a control scheme for the tendon-sheath mechanism for use in endoscopic systems. The asymmetric backlash hysteresis model that characterizes the transmission phenomena of the tendon-sheath mechanism in the loading and unloading phases is presented and discussed. An efficient parameter identification method is used to estimate the model parameters. Comparisons between the proposed model and experimental data validate the adoption of this new approach. A feedforward compensation method based on the asymmetric backlash hysteresis model is proposed and explored. The proposed model and control scheme are validated by experimental studies using a suitable experimental setup. The results show that the proposed model and the control scheme can improve the accuracy of tendon-sheath mechanism without using any output feedback and can be easily implemented in surgical robots using tendon-sheath mechanism as the main mode of transmission. 相似文献
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王自力 《太赫兹科学与电子信息学报》2020,18(5):831-836
通过移动无人机(UAV)收集无线传感网络数据的方案已受到广泛关注,将感测的数据与产生此数据的传感节点位置关联起来是十分必要的。为此提出了基于无人机的强健节点定位算法(UAV-NL)。UAV-NL算法将UAV位置作为未知信息。传感节点接收由UAV在随机位置传输的beacon包,并记录接收信号强度指示(RSSI)矢量;通过理论推导2个RSSI矢量的范数距离与这2节点距离的线性关系;最后,通过RSSI值测距,并利用半定规划(SDP)算法估计节点位置。仿真结果表明,提出的UAV-NL算法即使在噪声信道条件下仍具有高的定位精确度。 相似文献
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Mobile ad hoc networks (MANETs) are characterized by multiple entities, a frequently changing network topology and the need for efficient dynamic routing protocols. In MANETs, nodes are usually powered by batteries. Power control is tightly coupled with both the physical and medium access layers (MACs). However, if we increase the transmission power, at the same time we increase the interference to other nodes which diminish the transport capacity of wireless systems. Thus, the routing protocols based on hop count metric suffer from performance degradation when they operate over MANET. Routing in ad hoc wireless networks is not only a problem of finding a route with shortest length, but it is also a problem of finding a stable and good quality communication route in order to avoid any unnecessary packet loss. Cross-layer design of ad hoc wireless networks has been receiving increasing attention recently. Part of these researches suggests that routing should take into account physical layer characteristics. The goal of this paper is to improve the routing reliability in MANET and to reduce power consumption through cross-layer approach among physical, MAC and network layers. The proposed cross-layer approach is based on signal to interference plus noise ratio (SINR) and received signal strength indication (RSSI) coming from the physical layer. This solution performs in one hand the ad hoc on-demand distance vector routing protocol by choosing reliable routes with less interferences using SINR metric and in another hand; it permits to reduce the power transmission when sending the data packets by using RSSI metric. 相似文献
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针对传统DV-Hop算法中,跳数信息无法如实反应节点实际距离关系,而导致节点在无线传感网络定位过程中存在较大误差的问题,提出一种对跳数进行水平及垂直修正的改进算法。在相邻节点间,跳数进行水平修正,修正过程引入RSSI技术,对1跳按节点间距离与节点通信半径比值分段,细化跳数。同时在水平修正的基础上,分析节点间可能性分布,对不相邻节点引入修正角度进行垂直修正。仿真结果证明,在相同网络情况下,与传统DV-HOP算法相比,改进算法在增加少量计算量的前提下有效提高了定位精度。 相似文献
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Deployment of an efficient cellular network is considered as a challenging task as it affects the performance measures like data rate, bit error rate, spectrum efficiency and energy efficiency etc. For this, the foremost important step is developing an accurate path loss model for the network in the deployment region. In this paper, an empirical path loss model is estimated for an IEEE 802.16e standardised WiMAX network operating on a carrier frequency of 2.65 GHz deployed in a sub-urban area. An experimental setup is designed for collecting the parameters such as carrier to interference plus noise ratio (CINR), received signal strength indicator (RSSI) for the concerned network and with the help of regression technique, the path loss model is formulated. The relationships between CINR, RSSI, and the distance between base station and customer premise equipment are formulated. The distributions of RSSI, CINR and path loss for the concerned network are also found out. Then by using the proposed path loss model, link budget analysis is performed. From the analysis, it is concluded that the proposed path loss model closely approximates to Stanford University Interim model with path loss exponent value of 3.45. 相似文献