Hybrid radio tomographic imaging: potential localization for human detection in outdoor environment

Abstract

Radio Frequency Tomography (RTI) is a technique use to localize the human position. This approach offers great potential in monitoring activities such as perimeter surveillance monitoring, virtual fence for farm monitoring, and residential monitoring. Conventionally, RTI uses Linear Back Projection algorithm (LBP) to reconstruct the tomographic image. However, the ill-posed problem caused by back-projection and the smearing effect due to the overlapping image produce a low-quality tomographic image. To improve the quality tomographic image, several regularization approaches has been introduced by other researchers. These regularization techniques used in RTI to eliminate the smearing impact on the RTI image. However, because the target occupies only a small amount of space compared to the entire area monitored, the resulting image is blurred with noise. Thus, this paper proposed a Hybrid Radio Tomographic Imaging (HRTI) to overcome this problem. Our main focus is to improve the quality of RTI image by reducing the smeared area. In this approach, threshold sensor value has been introduced to reduce the impact of noise. By applying the HRTI, the average of reconstruction error can be reduced and accuracy localization of HRTI exceed 96%.