Research on RSSI-Based Event Detection Algorithm

Wireless sensing technology based on received signal strength indication (RSSI) has shown important application value in intelligent security, traffic monitoring and other fields. However, RSSI signals are easily disturbed by ambient noise, which leads to decreased detection reliability, which needs to be solved urgently. In this paper, a Self-Adaptive Event Detection (SAED) algorithm is proposed to improve the detection performance in complex dynamic environments by optimizing the signal processing mechanism. The algorithm constructed an adaptive sliding window model, adopted dynamic window length adjustment strategy for signal preprocessing, combined with environmental initial parameter calibration and wave coefficient compensation technology, effectively suppressed steady-state noise interference, and enhanced signal mutation characteristics, so as to achieve accurate and rapid detection of anomalies in RSSI signals. The experimental results show that the SAED algorithm is superior to the traditional mean sliding window algorithm in terms of detection accuracy and false detection rate, which can significantly reduce false positives. This algorithm provides an efficient solution for RSSI based event detection, and has a wide application prospect in the field of intelligent perception.