Radar Measurement of Human Polarimetric Micro-Doppler

Hindawi Publishing Corporation Journal of Electrical and Computer Engineering Volume 2013, Article ID 804954, 5 pages http://dx.doi.org/10.1155/2013/804954 Research Article Radar Measurement of Human Polarimetric Micro-Doppler David Tahmoush and Jerry Silvious U.S. Army Research Lab, Adelphi, MD 20783, USA Correspondence should be addressed to David Tahmoush; Received 24 May 2013; Accepted 4 September 2013 Academic Editor: Sandra Costanzo Copyright © 2013 D. Tahmoush and J. Silvious. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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, 𝐹𝑑 , of a simple point scatterer moving with speed V with respect to a stationary transmitter is 2V (1) cos 𝜃 cos 𝜙, 𝑐 where 𝐹𝑡 is the frequency of the transmitted signal, 𝜃 is the angle between the subject’s velocity and the beam of the radar in the ground plane, and 𝜙 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 𝐹𝑑 = 𝐹𝑡 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 the STFT is ∞ STFT (𝑥 (𝑡)) = 𝑋 (𝜏, 𝜔) = ∫ −∞ 𝑥 (𝑡) 𝑤 (𝑡 − 𝜏) 𝑒−𝑗𝑤𝑡 𝑑𝑡, (2) where 𝑤(𝑡) is the window function. Because human microDoppler varies slowly with time, we employ STFTs of the IQ radar data. The length of time used in the STFT is called coherent processing window, and this determines the resolution in Doppler frequency that can be measured. This can partially be overcome by superresolving methods. The spectrogram is the square modulus of the STFT and is then spectrogram (𝜏, 𝜔) = 10log10 |𝑋(𝜏, 𝜔)|2 (3) which is often used to display micro-Doppler data in decibels. Much of the analysis in this report makes use of spectrograms for the display of micro-Doppler phenomenology. The extraction of micro-Doppler features is typically performed in the joint time-frequency domain. Chirplet techniques [12] as well as linear FM basis decomposition [13] can be used to perform feature extraction. Independent component analysis (ICA) can be used to extract independent basis functions from the spectrogram to be used as features in a classifier [14]. Micro-Doppler signatures have been suggested as a biometric [15], and micro-Doppler features have been used in classification algorithms [15–18]. MicroDoppler signatures have been extracted through a brick wall [19]. Fully polarimetric human radar signatures at different approach angles with respect to the radar have been collected [20]. Automatic target classification has also been done on data including multiple humans, wheeled vehicles, tracked vehicles, clutter, and animal classes [21]. Micro-Doppler phenomena have been investigated in frequencies as low as UHF [22]. A 77 GHz radar was used to observe micro-Doppler signatures of human gait to recognize multiple persons and attempt to identify whether the person is swinging their arms [23]. An ultrawide band (UWB) impulse radar was used to provide both high resolution range profiles and high resolution Doppler spectrogram, which helps to extract detailed micro-Doppler signatures like swinging arms [24]. The detailed signatures are used to recognize human activities, such as marching, walking, one-arm swinging, or twoarm swinging. A combination of micro-Doppler signatures with microrange features was also proposed [25]. 2. Models To understand the micro-Doppler presented by moving humans, a model was built using the human motion as a collection of simple scatterers. Several other micro-Doppler models have been developed that characterise and attempt to predict the human micro-Doppler response [8–10]. We use research on human gait to model the expected Doppler shifts measured over time by a radar system. We started with the measurements made on twenty men and twenty women whose ages ranged from 20 to 38 years with an average age of 26 years and had their motions captured on video and extracted then their characteristics analyzed [26]. The resulting motion information was extracted, and then animated. We took the animated gait and extracted the micro-Doppler velocities that would be created by differentiating the motions using a point-scatterer model for each separate part. We neglected obscuration for these simulations because they were limited to frontal view, and we used a metallic skin approximation to simplify the calculations by neglecting the skin depth. The simulated micro-Doppler motions for different body parts are compared to measured data. These are calculated from the model and are calculated and measured at 17 GHz. The scaling for the images was set in order to simplify the comparison of images to demonstrate the variability of the human gait as viewed by the radar. The strid (...truncated)