Classification of Human Activities with Distributed Radar Systems

Abstract

This thesis introduces the relevance of radar systems in the realm of human activity recognition (HAR) in Chapter 1. The study touches upon the complex understanding of continuous human activities and the existing challenges and gaps in current methodologies, hinting at the innovative technical approaches that are to be detailed in the following chapters.
The technical foundation of the research is given in Chapter 2 by introducing distributed ultrawideband (UWB) radar systems. These systems, especially when spatially distributed, bring a depth of information by integrating data from multiple radar nodes and spatial perspectives. There is a significant emphasis on how different fusion techniques, both late and early, play a crucial role in harnessing data effectively, particularly in the context of HAR.
A critical contribution in the study is the potential to deviate from conventional radar data domains, such as microDoppler spectrograms for activity recognition. The research in Chapter 3 highlights an alternative approach, rooted in the radar phase information from a highresolution rangetime map, which bypasses the limitations of common FFTbased radar data domains. This methodology, paired with the histogram of oriented gradients (HOG) algorithm, showcases promising results that can be particularly interesting for realtime applications with computational constraints.
The research in Chapter 4 underlines the efficacy of employing a network of spatially distributed UWB radars for continuous HAR. These networks address the downsides of using a single sensor, like unfavorable aspectangle observations. The study delves into fusion methodologies and their implementation in classifying activities, particularly using recurrent neural networks. To assess these continuous recognition systems, novel evaluation metrics are proposed, offering a deeper insight into the practicality and effectiveness of such systems with temporal classification capabilities.
Indoor radar networks often face multipath challenges. The study in Chapter 5 not only identifies this challenge, but also uses the multipath components by leveraging these typically unwanted phenomena to enhance classification capabilities. Through a pipeline that isolates, determines, and analyzes different propagation pathways, there is an evident boost in the network’s perception. This novel approach showcases a significant performance upward trend, especially when employing convolutional neural networks.
Chapter 6 of the research focuses on the complexities of HAR in crowded environments. The study introduces the challenges of differentiating the activities of walking versus standing idle for multiple individuals simultaneously. The investigation shows initial promising results by using synthetic data generated from experimental recordings, by employing a regressionbased approach and leveraging diverse techniques such as LSTM, CNN, SVM, and linear regression.
In conclusion, the research offers a reflective glance at the breakthroughs achieved in the domain of radarbased HAR in Chapter 7. The significant contributions and advancements of the study are highlighted. Looking ahead, the chapter identifies research areas for exploration and further improvement.