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
The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A a...The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A and signal constant n in traditional signal propagation path loss models.This algorithm utilizes the adaptive whale optimization algorithm to iteratively optimize the parameters of the backpropagation(BP)neural network,thereby enhancing its prediction performance.To address the issue of low accuracy and large errors in traditional received signal strength indication(RSSI),the algorithm first uses the extended Kalman filtering model to smooth the RSSI signal values to suppress the influence of noise and outliers on the estimation results.The processed RSSI values are used as inputs to the neural network,with distance values as outputs,resulting in more accurate ranging results.Finally,the position of the node to be measured is determined by combining the weighted centroid algorithm.Experimental simulation results show that compared to the standard centroid algorithm,weighted centroid algorithm,BP weighted centroid algorithm,and whale optimization algorithm(WOA)-BP weighted centroid algorithm,the proposed algorithm reduces the average localization error by 58.23%,42.71%,31.89%,and 17.57%,respectively,validating the effectiveness and superiority of the algorithm.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 Natural Science Foundation of China(Nos.62265010,62061024)Gansu Province Science and Technology Plan(No.23YFGA0062)Gansu Province Innovation Fund(No.2022A-215)。
文摘The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A and signal constant n in traditional signal propagation path loss models.This algorithm utilizes the adaptive whale optimization algorithm to iteratively optimize the parameters of the backpropagation(BP)neural network,thereby enhancing its prediction performance.To address the issue of low accuracy and large errors in traditional received signal strength indication(RSSI),the algorithm first uses the extended Kalman filtering model to smooth the RSSI signal values to suppress the influence of noise and outliers on the estimation results.The processed RSSI values are used as inputs to the neural network,with distance values as outputs,resulting in more accurate ranging results.Finally,the position of the node to be measured is determined by combining the weighted centroid algorithm.Experimental simulation results show that compared to the standard centroid algorithm,weighted centroid algorithm,BP weighted centroid algorithm,and whale optimization algorithm(WOA)-BP weighted centroid algorithm,the proposed algorithm reduces the average localization error by 58.23%,42.71%,31.89%,and 17.57%,respectively,validating the effectiveness and superiority of the algorithm.
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%。
