A novel flocking control approach is proposed for multi-agent systems by integrating the variables of velocities, motion directions, and positions of agents. A received signal strength indicator （RSSI） is applied as...A novel flocking control approach is proposed for multi-agent systems by integrating the variables of velocities, motion directions, and positions of agents. A received signal strength indicator （RSSI） is applied as a variable to estimate the inter-distance between agents. A key parameter that contains the local information of agents is defined, and a multi-variable controller is proposed based on the parameter. For the position control of agents, the RSSI is introduced to substitute the distance as a control variable in the systems. The advantages of RSSI include that the relative distance between every two agents can be adjusted through the communication quality under different environments, and it can shun the shortage of the limit of sensors. Simulation studies demonstrate the effectiveness of the proposed control approach.展开更多
A limiting amplifier IC implemented in 65nm CMOS technology and intended for high-speed op- tical fiber communications is described in this paper. The inductorless limiting amplifier incorporates 5-stage 8 dB gain lim...A limiting amplifier IC implemented in 65nm CMOS technology and intended for high-speed op- tical fiber communications is described in this paper. The inductorless limiting amplifier incorporates 5-stage 8 dB gain limiting cells with active feedback and negative Miller capacitance, a high speed output buffer with novel third order active feedback, and a high speed full-wave rectifier. The re- ceiver signal strength indictor （RSSI） can detect input signal power with 33dB dynamic range, and the limiting amplifier features a programmable loss of signal （LOS） indication with external resistor. The sensitivity of the limiting amplifier is 5.5mV at BER = 10^ -12 and the layout area is only 0.53 × 0.72 mm^2 because of no passive inductor. The total gain is over 41dB, and bandwidth exceeds 12GHz with 56mW power dissipation.展开更多
针对基于ZigBee网络的节点接收信号强度指示(received signal strength indication,RSSI)在复杂环境测量会产生偏差的问题,提出一种基于混合滤波的无线网络测距算法。该方法在运用卡尔曼滤波的基础上融合了基于中值自适应加权高斯滤波...针对基于ZigBee网络的节点接收信号强度指示(received signal strength indication,RSSI)在复杂环境测量会产生偏差的问题,提出一种基于混合滤波的无线网络测距算法。该方法在运用卡尔曼滤波的基础上融合了基于中值自适应加权高斯滤波的混合滤波,首先用卡尔曼滤波算法去除波动性较大的RSSI值,再利用中位值抗差性原理和自适应函数降低RSSI数据的波动。仿真实验结果表明,混合滤波无线网络测距算法能够较大程度减小异常值带来的波动,有效提高RSSI采样精度。展开更多
针对基于接收信号强度指示(Received Signal Strength Indicator,RSSI)测距模型的室内定位算法在复杂多变的室内环境下难以根据当前室内环境实时更新模型参数而导致定位精度下降的问题,提出了一种粒子群优化的RSSI室内定位算法。该算法...针对基于接收信号强度指示(Received Signal Strength Indicator,RSSI)测距模型的室内定位算法在复杂多变的室内环境下难以根据当前室内环境实时更新模型参数而导致定位精度下降的问题,提出了一种粒子群优化的RSSI室内定位算法。该算法利用蓝牙低功耗网状(Bluetooth Low Energy Mesh,BLE Mesh)网络中各节点可以相互通信的特点及对数路径损耗模型参数对距离转换的直接影响,将各锚节点间预测距离和真实距离的均方误差作为约束条件,采用粒子群优化对对数路径损耗模型的参数进行迭代优化,最终获得符合当前室内环境的模型参数,以进行室内定位。实验结果表明,提出的算法具有较好的收敛性能,且定位误差在1 m以内,能够有效满足室内定位的实际需求。展开更多
针对基于RSSI和CSI的指纹定位技术易受环境干扰、定位精度较低的问题,提出了一种基于RSSI指纹和相位修正信道状态信息(phase correct based channel state information,PC-CSI)指纹的加权融合指纹定位技术。基于PC-CSI的指纹定位在传统...针对基于RSSI和CSI的指纹定位技术易受环境干扰、定位精度较低的问题,提出了一种基于RSSI指纹和相位修正信道状态信息(phase correct based channel state information,PC-CSI)指纹的加权融合指纹定位技术。基于PC-CSI的指纹定位在传统基于CSI幅值的指纹定位基础上增加相位信息对定位结果进行修正,之后对RSSI指纹和PC-CSI指纹的定位结果加权重定位。实验结果表明,提出的加权融合指纹定位算法与基于CSI的主动定位算法相比,平均定位误差(mean position error,MPE)降低了36.2%,能满足室内定位需求。展开更多
Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization...Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization is the process of identifying the target node’s location.In this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models.Initially,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D WSN.The position of the anchor nodes is fixed for detecting the location of the target.Further,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node localization.In the regular and irregular surfaces,this hybrid algorithm effectively performs the localization process.The suggested hybrid algorithm converges very fast in the three-dimensional(3D)environment.The accuracy of the proposed node localization process is 94.25%.展开更多
针对DV-HOP(distance vector hop)算法的定位精度对节点间跳数信息依赖性较强的特点,提出一种基于接收信号强度指示(received signal strength indicator,RSSI)每跳分级和平均跳距修正的DV-HOP改进算法RADV-HOP(RSSI and average hoppin...针对DV-HOP(distance vector hop)算法的定位精度对节点间跳数信息依赖性较强的特点,提出一种基于接收信号强度指示(received signal strength indicator,RSSI)每跳分级和平均跳距修正的DV-HOP改进算法RADV-HOP(RSSI and average hopping distance modifying DV-HOP)。仿真结果表明:在相同的网络环境里,与传统DV-HOP算法相比,RADV-HOP定位算法仅需节点通信芯片带有RSSI指示功能及增加少量的计算和通信开销,不需要额外的硬件开销,将每跳分为3个子级时,归一化定位误差能下降65%;与其他DV-HOP修正算法相比,RADV-HOP算法以相同的通信开销和稍微增加的计算开销使定位误差下降了45%。展开更多
基金supported by the National Basic Research Program of China (973Program) under Grant No. 2010CB731800the National Natural Science Foundation of China under Grant No. 60934003 and 61074065the Key Project for Natural Science Research of Hebei Education Departmentunder Grant No. ZD200908
文摘A novel flocking control approach is proposed for multi-agent systems by integrating the variables of velocities, motion directions, and positions of agents. A received signal strength indicator （RSSI） is applied as a variable to estimate the inter-distance between agents. A key parameter that contains the local information of agents is defined, and a multi-variable controller is proposed based on the parameter. For the position control of agents, the RSSI is introduced to substitute the distance as a control variable in the systems. The advantages of RSSI include that the relative distance between every two agents can be adjusted through the communication quality under different environments, and it can shun the shortage of the limit of sensors. Simulation studies demonstrate the effectiveness of the proposed control approach.
基金Supported by the National High Technology Research and Development Programme of China(No.2011AA010301)the National Natural Science Foundation of China(No.60976029)
文摘A limiting amplifier IC implemented in 65nm CMOS technology and intended for high-speed op- tical fiber communications is described in this paper. The inductorless limiting amplifier incorporates 5-stage 8 dB gain limiting cells with active feedback and negative Miller capacitance, a high speed output buffer with novel third order active feedback, and a high speed full-wave rectifier. The re- ceiver signal strength indictor （RSSI） can detect input signal power with 33dB dynamic range, and the limiting amplifier features a programmable loss of signal （LOS） indication with external resistor. The sensitivity of the limiting amplifier is 5.5mV at BER = 10^ -12 and the layout area is only 0.53 × 0.72 mm^2 because of no passive inductor. The total gain is over 41dB, and bandwidth exceeds 12GHz with 56mW power dissipation.
文摘针对基于ZigBee网络的节点接收信号强度指示(received signal strength indication,RSSI)在复杂环境测量会产生偏差的问题,提出一种基于混合滤波的无线网络测距算法。该方法在运用卡尔曼滤波的基础上融合了基于中值自适应加权高斯滤波的混合滤波,首先用卡尔曼滤波算法去除波动性较大的RSSI值,再利用中位值抗差性原理和自适应函数降低RSSI数据的波动。仿真实验结果表明,混合滤波无线网络测距算法能够较大程度减小异常值带来的波动,有效提高RSSI采样精度。
文摘针对基于接收信号强度指示(Received Signal Strength Indicator,RSSI)测距模型的室内定位算法在复杂多变的室内环境下难以根据当前室内环境实时更新模型参数而导致定位精度下降的问题,提出了一种粒子群优化的RSSI室内定位算法。该算法利用蓝牙低功耗网状(Bluetooth Low Energy Mesh,BLE Mesh)网络中各节点可以相互通信的特点及对数路径损耗模型参数对距离转换的直接影响,将各锚节点间预测距离和真实距离的均方误差作为约束条件,采用粒子群优化对对数路径损耗模型的参数进行迭代优化,最终获得符合当前室内环境的模型参数,以进行室内定位。实验结果表明,提出的算法具有较好的收敛性能,且定位误差在1 m以内,能够有效满足室内定位的实际需求。
文摘针对基于RSSI和CSI的指纹定位技术易受环境干扰、定位精度较低的问题,提出了一种基于RSSI指纹和相位修正信道状态信息(phase correct based channel state information,PC-CSI)指纹的加权融合指纹定位技术。基于PC-CSI的指纹定位在传统基于CSI幅值的指纹定位基础上增加相位信息对定位结果进行修正,之后对RSSI指纹和PC-CSI指纹的定位结果加权重定位。实验结果表明,提出的加权融合指纹定位算法与基于CSI的主动定位算法相比,平均定位误差(mean position error,MPE)降低了36.2%,能满足室内定位需求。
基金appreciation to King Saud University for funding this research through the Researchers Supporting Program number(RSPD2024R918),King Saud University,Riyadh,Saudi Arabia.
文摘Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization is the process of identifying the target node’s location.In this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models.Initially,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D WSN.The position of the anchor nodes is fixed for detecting the location of the target.Further,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node localization.In the regular and irregular surfaces,this hybrid algorithm effectively performs the localization process.The suggested hybrid algorithm converges very fast in the three-dimensional(3D)environment.The accuracy of the proposed node localization process is 94.25%.
文摘针对DV-HOP(distance vector hop)算法的定位精度对节点间跳数信息依赖性较强的特点,提出一种基于接收信号强度指示(received signal strength indicator,RSSI)每跳分级和平均跳距修正的DV-HOP改进算法RADV-HOP(RSSI and average hopping distance modifying DV-HOP)。仿真结果表明:在相同的网络环境里,与传统DV-HOP算法相比,RADV-HOP定位算法仅需节点通信芯片带有RSSI指示功能及增加少量的计算和通信开销,不需要额外的硬件开销,将每跳分为3个子级时,归一化定位误差能下降65%;与其他DV-HOP修正算法相比,RADV-HOP算法以相同的通信开销和稍微增加的计算开销使定位误差下降了45%。
