At present, pyroelectric infrared, voice signal, vibration signal, and magnetic signal sensors are commonly used for detection. The sensor signal acquisition is significantly important, and the type of sensors concerns the detection result. Detection based on WSN processes object information collected by sensors, sends to the control center, and then analyses the data to realize the detection. Object detection is a popular area of research. Experimental results show the uncertainty of RSSI change at the moment of human through the network area and confirm the validity of the detection method. A high-quality network based on ZigBee is obtained, and then, RSSI can be calculated from the receive sensor modules. The experimental measurements are conducted in laboratory. The intermittent Karman algorithm is used to filter RSSI fluctuation caused by environment and other factors in data packets loss situation, and online learning is adopted to set algorithm parameters considering environmental changes. Compared to conventional solutions which utilize a complex set of sensors for detection, the proposed approach achieves the same only by RSSI volatility. Then, a device-free human detection method is proposed. So this paper firstly raises a real-time RSSI link quality evaluation method based on the exponential smoothing method. The packet reception rate (PRR) based on a large number of data packets cannot reflect the real-time link quality accurately. Packet loss is observed to be a familiar phenomenon with transmissions of WSNs. This paper presents a device-free human detection method for using Received Signal Strength Indicator (RSSI) measurement of Wireless Sensor Network (WSN) with packet dropout based on ZigBee.
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