An array antenna based localization using spatial smoothing processing (SSP) is proposed for wireless security and monitoring, referred to as array sensor. The proposed method is based on the array sensor that exploits an array antenna at the receiver to detect the propagation environment of interest. If an event occurs, e.g., human motion, the propagation environment is changed. Thus the eigenvector and eigenvalue spanning the signal subspace that is inherent to its environment changes as well. Using a machine learning technique based on the eigenvector and eigenvalue, we can detect the event accurately. The proposed method is improved from our previous work which uses only a limited number of signal subspace features. The basic idea of this work is the extension of the dimension of the signal subspace by using SSP without increasing the number of array element. In addition, this work investigates the impact of the array antenna placement on localization performance. The experimental results show that the proposed SSP based method achieves a 41.83% improvement in localization accuracy, and a 1.24 m improvement in root mean square error (RMSE) compared to the previous method.
ASJC Scopus subject areas
- Electrical and Electronic Engineering