Spectrogram-Based Methods for Human Identification in Single-Channel SAR Data

Abstract

Radar offers unique advantages over other sensors, such as visual or seismic sensors, for human target detection and identification. Radar can operate far away from potential targets, and functions during the daytime as well as nighttime in virtually all weather conditions. In this paper, we examine the problem of human target detection and identification using single-channel synthetic aperture radar (SAR) data. A 12-point human model, together with kinematic equations of motion for each body part, is used to calculate the expected target return and spectrogram. The unique characteristics of the human spectrogram are analysed and used to design a prototype for an automated gender discrimination scheme. Simulation results show a 83.97% detection rate for males and 91.11% detection rate for females. Inherent deficiencies of spectrogram-based methods are discussed. Future work will focus on the development of an alternative solution for overcoming these deficiencies.