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
室内无线传感器网络定位通常采用基于信号强度指示RSSI(Received Signal Strength Indicator)的测距方法,但由于室内信号反射、阻挡严重,同时不同硬件之间存在性能差异,导致RSSI随距离的衰减模型难以精确表述。本文提出一种基于广义延...室内无线传感器网络定位通常采用基于信号强度指示RSSI(Received Signal Strength Indicator)的测距方法,但由于室内信号反射、阻挡严重,同时不同硬件之间存在性能差异,导致RSSI随距离的衰减模型难以精确表述。本文提出一种基于广义延拓插值的RSSI测距模型,通过对实测数据进行拟合和插值,构造出能够反映实测环境下的衰减模型,避免了大尺度衰减模型中环境衰减因子难以由经验值给出的问题,同时也减小了不同硬件间的性能差异带来的影响,相比传统的大尺度衰减模型测距精度得到很大改善。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.
文摘室内无线传感器网络定位通常采用基于信号强度指示RSSI(Received Signal Strength Indicator)的测距方法,但由于室内信号反射、阻挡严重,同时不同硬件之间存在性能差异,导致RSSI随距离的衰减模型难以精确表述。本文提出一种基于广义延拓插值的RSSI测距模型,通过对实测数据进行拟合和插值,构造出能够反映实测环境下的衰减模型,避免了大尺度衰减模型中环境衰减因子难以由经验值给出的问题,同时也减小了不同硬件间的性能差异带来的影响,相比传统的大尺度衰减模型测距精度得到很大改善。
