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Based on the recent advances on device-free wireless sensing, this paper presents a novel technique to estimate the speed of a target by analyzing its influence on surrounding wireless signals from low-cost wireless communication infrastructures, such as Zigbee or Wi-Fi Based on the shadow effect caused by the target, this paper estimate the moving speed of the target by using the changes of the Received Signal Strength (RSS) when the target crosses wireless links at the different times. The experimental results demonstrate the effectiveness of the proposed method.

As one of the main methods of device free localization (DFL) that can locate a target without attaching any devices, the radio tomographic imaging (RTI) method based on the low-cost wireless sensor network (WSN) has wide application prospects. The current RTI positioning methods mainly determine a target position by searching the maximum brightness pixel in a RTI map. However, due to the existence of environmental noises and interference, the brightness of the pseudo-target is often greater than that of the real target, resulting in a misjudgment of the target and even a great positioning error. In this paper, an enhanced RTI (E-RTI) positioning method that combines target shape features with the target brightness feature is proposed for overcoming this problem. This method can effectively reduce the negative influence of pseudo-targets and environmental noises by using the target shape prior information. The experimental results show that the positioning accuracy of this method is better than the existing RTI method.

With the recent advances in the theory of compressive sensing (CS), spatial sparsity has been applied for device-free localization (DFL) to reduce the number of measurements required by DFL systems while maintaining the high localization accuracy. However, few works considered the problem of basis mismatch in CS-based DFL. In this paper, a new CS-based DFL approach with basis self-calibration (called DFL-SC) is proposed to overcome the problem incurred by basis errors. The novel feature of this method is to simultaneously estimate the perturbations of the basis matrix and the sparse solution based on a joint optimization framework under sparsity constraints. Meanwhile, we add prior information on the support region obtained from last target's location estimation into the sparse reconstruction process for enhancing reconstruction performance. Experimental results verify the effectiveness of the proposed method on the location accuracy.

Breathing frequency not only indicates the progression of the disease but also an important vital sign predicting a rapid decline in health. For this purpose, noncontact monitoring systems are increasingly prevalent to clearly enhance patient comfort. As a low cost solution for noninvasive respiratory monitoring, respiratory rate measurement based on inexpensive transceiver based received signal strength (RSS) is widely used. In this research, respiration rate monitoring method based on ready-made pair of transceivers has been proposed. We propose a method to estimate and improve accuracy and evaluate its effect. Furthermore, respiration monitoring and frequency estimation are performed in the frequency band of 915 MHz. Experimental results show reliable detection and high frequency estimation accuracy.