Limitations of optical devices for motion sensing such as small coverage, sensitivity to obstacles, and privacy exposure result in the need for improvement. As motion sensing based on radio frequency signals is not constrained by the limitation above, channel state information (CSI) from Wi-Fi devices could be used to improve sensing performance under the above circumstances. Unfortunately, CSI phase cannot be practically obtained due to the temporal phase rotation generated from Wi-Fi chips. Therefore, it would be rather complicated to realize motion analysis, especially the direction of motion. To mitigate the issue, this paper proposes a CSI calibration method that employs a back-to-back channel between Wi-Fi transceivers for phase rotation removal while preserving the original CSI phase. Through experiment, calibrated CSI showed a high similarity to the channel without phase rotation measured using a Vector Network Analyzer (VNA). Another experiment was conducted to observe Doppler frequency due to simple hand gestures using the Wavelet transform. A visual analysis revealed that the Doppler frequency of calibrated CSI could correctly capture the motion pattern. To the best of the authors’ knowledge, this is the first calibration method that maintains the original CSI and is applicable for in-depth motion analysis.

No abstract

No abstract

No abstract

No abstract

No abstract

No abstract

Post-disaster situation requires quick and effective rescue efforts by the first responders. Generally the rescue teams use wireless radios for intra-agency communications. Lack of collaboration among different rescue agencies may create interference among the emergency radios. Identification of some physical parameters of these active radios is necessary for collaboration. Carrier frequency and bandwidth can be estimated by spectrum sensing, whereas modulation classification requires further signal processing and classification operations. Processing speed and performance of the classification system can be controlled by appropriate selection of signal parameters, signal processing techniques and the classification algorithms. A wireless disaster area emergency network (W-DAEN) can be installed in the disaster area to detect and capture data (time samples) of the occupied frequencies. This study consists of some simulation results of a machine learning based cooperative automatic modulation classification technique by using six unique features. The classification performance and processing time of the proposed algorithm is quite satisfactory for real-time classification system.