The relationship between RSSI (Received Signal Strength Indication) values and distance is the foundation and the key of ranging and positioning technologies in wireless sensor networks. Log-normal shadowing model (LN...The relationship between RSSI (Received Signal Strength Indication) values and distance is the foundation and the key of ranging and positioning technologies in wireless sensor networks. Log-normal shadowing model (LNSM), as a more general signal propagation model, can better describe the relationship between the RSSI value and distance, but the parameter of variance in LNSM is depended on experiences without self-adaptability. In this paper, it is found that the variance of RSSI value changes along with distance regu- larly by analyzing a large number of experimental data. Based on the result of analysis, we proposed the relationship function of the variance of RSSI and distance, and established the log-normal shadowing model with dynamic variance (LNSM-DV). At the same time, the method of least squares(LS) was selected to es- timate the coefficients in that model, thus LNSM-DV might be adjusted dynamically according to the change of environment and be self-adaptable. The experimental results show that LNSM-DV can further reduce er- ror, and have strong self-adaptability to various environments compared with the LNSM.展开更多
为了实现对赛车手进行实时定位,并保障赛车手安全,提出基于接收信号强度指示RSSI(Received Signal Strength Index)测距的场地自行车的跟踪算法TCTR(Track Cycling Tracking based on received signal strength Index Ranging)。TCTR算...为了实现对赛车手进行实时定位,并保障赛车手安全,提出基于接收信号强度指示RSSI(Received Signal Strength Index)测距的场地自行车的跟踪算法TCTR(Track Cycling Tracking based on received signal strength Index Ranging)。TCTR算法的目的就是估计移动节点(自行车)和锚节点(教练)间的距离。依据对数正态衰落模型LNSM(Log-Normal Shadowing Model)和锚节点所接收的RSSI值,TCTR算法测量自行车和教练间的距离。为了提高测距精度,建立室内、室外的场地自行车实验,获取RSSI值和距离数据,再通过拟合,最终估计LNSM参数。仿真结果表明,通过优化LNSM参数,降低了测距的均方根误差。展开更多
文摘The relationship between RSSI (Received Signal Strength Indication) values and distance is the foundation and the key of ranging and positioning technologies in wireless sensor networks. Log-normal shadowing model (LNSM), as a more general signal propagation model, can better describe the relationship between the RSSI value and distance, but the parameter of variance in LNSM is depended on experiences without self-adaptability. In this paper, it is found that the variance of RSSI value changes along with distance regu- larly by analyzing a large number of experimental data. Based on the result of analysis, we proposed the relationship function of the variance of RSSI and distance, and established the log-normal shadowing model with dynamic variance (LNSM-DV). At the same time, the method of least squares(LS) was selected to es- timate the coefficients in that model, thus LNSM-DV might be adjusted dynamically according to the change of environment and be self-adaptable. The experimental results show that LNSM-DV can further reduce er- ror, and have strong self-adaptability to various environments compared with the LNSM.
文摘为了实现对赛车手进行实时定位,并保障赛车手安全,提出基于接收信号强度指示RSSI(Received Signal Strength Index)测距的场地自行车的跟踪算法TCTR(Track Cycling Tracking based on received signal strength Index Ranging)。TCTR算法的目的就是估计移动节点(自行车)和锚节点(教练)间的距离。依据对数正态衰落模型LNSM(Log-Normal Shadowing Model)和锚节点所接收的RSSI值,TCTR算法测量自行车和教练间的距离。为了提高测距精度,建立室内、室外的场地自行车实验,获取RSSI值和距离数据,再通过拟合,最终估计LNSM参数。仿真结果表明,通过优化LNSM参数,降低了测距的均方根误差。
