Evaluating the Impacts of Raw Data Preprocessing on the Accuracy of RSS based Ranging

In wireless sensor networks (WSN), location technology services are often very important. The positioning technology of WSN includes ranging based and non-ranging based positioning, among which ranging based positioning technology based on received signal strength (RSS) is widely used due to its simplicity. However, due to the influence of shadow fading in the environment, the measurement of RSS has a large variance and is easily affected by occlusion and interference signals. Therefore, how to effectively reduce the variance of RSS measurement values to reduce the error and standard deviation of ranging and positioning has always been a widely discussed topic. This article is based on the single slope logarithmic distance path loss model and derives the Cramer-Rao Lower Bounds (CRLB) for its distance estimation value. This article reduces the mean and standard deviation of distance measurement errors by averaging the measurement data on both temporal and spatial scales, and compares it with CRLB. The results indicate that both temporal and spatial averages can reduce the standard deviation of distance measurement error based on RSS, and spatial averaging can also reduce the mean of its distance measurement error.