Automatic target recognition (ATR) using micro-Doppler analysis is a technique that has been a topic of great research over the past decade, with key applications to border control and security, perimeter defense, and force protection. Patterns in the movements of animals, humans, and drones can all be accomplished through classification of the target’s micro-Doppler signature. Typically, classification is based on a set of fixed, pre-defined features extracted from the signature; however, such features can perform poorly under low signal-to-noise ratio (SNR), or when the number and similarity of classes increases. This paper proposes a novel set of data-driven frequency-warped cepstral coefficients (FWCC) for classification of micro-Doppler signatures, and compares performance with that attained from the data-driven features learned in deep neural networks (DNNs). FWCC features are computed by first filtering the discrete Fourier Transform (DFT) of the input signal using a frequency-warped filter bank, and then computing the discrete cosine transform (DCT) of the logarithm. The filter bank is optimized for radar using genetic algorithms (GA) to adjust the spacing, weight, and width of individual filters. For a 11-class case of human activity recognition, it is shown that the proposed data-driven FWCC features yield similar classification accuracy to that of DNNs, and thus provides interesting insights on the benefits of learned features.
Cognitive radar is a novel concept for next-generation radar systems, which as part of the perception-action cycle to improve the measurement process based on dynamic changes in the environment. Although most work in this area to-date have focused on adaptation on the transmitted waveform, in this paper, we propose adaptive control of novel multifunctional reconfigurable antennas (MRAs) as a mechanism for action within the cognitive radar framework. Reconfigurable parasitic layer based MRAs have the capability of dynamically and simultaneously changing its electromagnetic characteristics (mode of operation), e.g. antenna beam pattern, polarization, center frequency, or a combination of thereof. Different modes of an MRA are controlled via RF switches interconnecting the pixels of the reconfigurable parasitic layer. This enhanced capability can be controlled using adaptive mode selection schemes. In particular, an array of MRAs provides more degrees of freedom, where each element of an array can be controlled to generate one of many modes depending on the environmental measured variables as a feedback mechanism. In this work, a designed and fabricated reconfigurable parasitic layer based MRA operating over 4.94-4.99 GHz band with 25 different radiation patterns, i.e., modes of operation, is utilized for cognitive direction-of-arrival (DoA) estimation and target tracking. A novel computationally efficient iterative mode selection (IMS) technique for MRA arrays is developed, where the modes are cognitively selected to minimize the DoA estimation error in target track. It is demonstrated that the proposed cognitive mode selection for MRA arrays achieves remarkably lower estimation errors compared to uniform pattern arrays without adaptive capability.
The human micro-Doppler signature is a unique signature caused by the time-varying motion of each point on the human
body, which can be used to discriminate humans from other targets exhibiting micro-Doppler, such as vehicles, tanks,
helicopters, and even other animals. Classification of targets based on micro-Doppler generally involves joint timefrequency
analysis of the radar return coupled with extraction of features that may be used to identify the target.
Although many techniques have been investigated, including artificial neural networks and support vector machines,
almost all suffer a drastic drop in classification performance as the aspect angle of human motion relative to the radar
increases. This paper focuses on the use of radar networks to obtain multi-aspect angle data and thereby ameliorate the
dependence of classification performance on aspect angle. Knowledge of human walking kinematics is exploited to
generate a fuse spectrogram that incorporates estimates of model parameters obtained from each radar in the network. It
is shown that the fused spectrogram better approximates the truly underlying motion of the target observed as compared
with spectrograms generated from individual nodes.
Radar offers unique advantages over other sensors, such as visual or seismic sensors, for human target detection.
Many situations, especially military applications, prevent the placement of video cameras or implantment seismic
sensors in the area being observed, because of security or other threats. However, radar can operate far away
from potential targets, and functions during 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, airborne,
synthetic aperture radar (SAR). Human targets are differentiated from other detected slow-moving targets by
analyzing the spectrogram of each potential target. Human spectrograms are unique, and can be used not
just to identify targets as human, but also to determine features about the human target being observed, such
as size, gender, action, and speed. 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. A MATLAB simulation
environment is developed including ground clutter, human and non-human targets for the testing of spectrogram-based
detection and identification algorithms. Simulations show that spectrograms have some ability to detect
and identify human targets in low noise. An example gender discrimination system correctly detected 83.97%
of males and 91.11% of females. The problems and limitations of spectrogram-based methods in high clutter
environments are discussed. The SNR loss inherent to spectrogram-based methods is quantified. An alternate
detection and identification method that will be used as a basis for future work is proposed.