Radar has emerged as a leading technology supporting large sectors of commerce, defense and security. Enabled by the advent of small, low-cost solid-state and software-defined radar technologies, new radar applications involving cognitive radar, medical and biometric radar, passive radar, and automotive radar have been made possible. In this paper, we examine redundancy in human motion signatures along the data and short-time Fourier transform (STFT) parameters. With an "eye" on a final product, we evaluate the effect of reduced sampling along slow-time on classification performance. The goal is to determine the degree of data down-sampling that can be tolerated without compromising feature extraction or significantly impeding motion classifications. We search for the optimum STFT parameters that provide the best classification performance for the given radar measurements and gain an understanding of their respective nominal range values.
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.
Falls are a major cause of accidents in elderly people. Even simple falls can lead to severe injuries, and sometimes result in death. Doppler fall detection has drawn much attention in recent years. Micro-Doppler signatures play an important role for the Doppler-based radar systems. Numerous studies have demonstrated the offerings of micro-Doppler characteristics for fall detection. In this respect, a plethora of micro-Doppler signature features have been proposed, including those stemming from speech recognition and wavelet decomposition. In this work, we consider four different sets of features for fall detection. These can be categorized as spectrogram based features, wavelet based features, mel-frequency cepstrum coefficients, and power burst curve features. Support vector machine is employed as the classifier. Performance of the respective fall detectors is investigated using real data obtained with the same radar operating resources and under identical sensing conditions. For the considered data, the spectrogram based feature set is shown to provide superior fall detection performance.