Radar Measurement of Human Polarimetric Micro-Doppler

We use polarimetric micro-Doppler for the detection of arm motion, especially for the classification of whether someone has their arms swinging and is thus unloaded. The arm is often bent at the elbow, providing a surface somewhat similar to a dihedral. This is distinct from the more planar surfaces of the body which allows us to isolate the signals of the arm (and knee). The dihedral produces a double bounce that can be seen in polarimetric radar data by measuring the phase difference between HH and VV. This measurement can then be used to determine whether the subject is unloaded. 1. Introduction Detailed radar processing can reveal many characteristics of human motions and of the human body, including gait characteristics. Micro-Doppler signals refer to Doppler scattering returns produced by the motions of the target other than gross translation. Parts of the human body do not move with constant radial velocity; some of the small micro-Doppler signatures are periodic, and therefore analysis techniques can be used to obtain more characteristics [1, 2]. Micro-Doppler gives rise to many detailed radar image features in addition to those associated with the bulk target motions. Modulations of the radar return from arms, legs, and even body sway are being investigated by researchers [3–5]. There are also some tutorials on micro-Doppler phenomena [2, 6, 7]. The Doppler information measured by a radar arises from target motions. The equation for computing the nonrelativistic Doppler frequency shift, , of a simple point scatterer moving with speed with respect to a stationary transmitter is where is the frequency of the transmitted signal, is the angle between the subject’s velocity and the beam of the radar in the ground plane, and is the elevation angle between the subject’s velocity and the radar beam. This assumes that the radar itself is stationary. Targets can be considered as collections of simple scatterers, though this is a rough approximation. The micromotion of the scatterers around the center frequency creates a micro-Doppler model that varies with time. Several micro-Doppler models have been developed which characterize and attempt to predict the human micro-Doppler response [8–10] using animated collections of simple scatterers as the foundation. A short-time FT (STFT) is one way to explore the slow time-dependent behaviour of the Doppler spectrum by doing a Fourier transform over a small window in time, then sliding the window [11]. This avoids the loss of time information that occurs when applying a Fourier transform. The continuous form of