Radar Micro-Doppler Signatures: Processing and Applications

Chen, Victor C. Chen; Tahmoush, David; Miceli, William J.

doi:10.1049/pbra034e

Cited by 141 publications

(3 citation statements)

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“…Figure 9 (top left) shows three successive frames divided by a vertical dashed line. The second figure from the top left in Figure 9 shows a micro-Doppler map obtained by processing three frames of radar signal (Chen et al, 2014) (computed as described in Supplementary Material). The micro-Doppler maps show the distribution of reflected energy over velocity, at a fixed distance, as a slow-time function.…”

Section: Radar-based Hand Gesture Classification In µBrainmentioning

confidence: 99%

μBrain: An Event-Driven and Fully Synthesizable Architecture for Spiking Neural Networks

et al. 2021

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The development of brain-inspired neuromorphic computing architectures as a paradigm for Artificial Intelligence (AI) at the edge is a candidate solution that can meet strict energy and cost reduction constraints in the Internet of Things (IoT) application areas. Toward this goal, we present μBrain: the first digital yet fully event-driven without clock architecture, with co-located memory and processing capability that exploits event-based processing to reduce an always-on system's overall energy consumption (μW dynamic operation). The chip area in a 40 nm Complementary Metal Oxide Semiconductor (CMOS) digital technology is 2.82 mm2 including pads (without pads 1.42 mm2). This small area footprint enables μBrain integration in re-trainable sensor ICs to perform various signal processing tasks, such as data preprocessing, dimensionality reduction, feature selection, and application-specific inference. We present an instantiation of the μBrain architecture in a 40 nm CMOS digital chip and demonstrate its efficiency in a radar-based gesture classification with a power consumption of 70 μW and energy consumption of 340 nJ per classification. As a digital architecture, μBrain is fully synthesizable and lends to a fast development-to-deployment cycle in Application-Specific Integrated Circuits (ASIC). To the best of our knowledge, μBrain is the first tiny-scale digital, spike-based, fully parallel, non-Von-Neumann architecture (without schedules, clocks, nor state machines). For these reasons, μBrain is ultra-low-power and offers software-to-hardware fidelity. μBrain enables always-on neuromorphic computing in IoT sensor nodes that require running on battery power for years.

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Section: Radar-based Hand Gesture Classification In µBrainmentioning

confidence: 99%

μBrain: An Event-Driven and Fully Synthesizable Architecture for Spiking Neural Networks

et al. 2021

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“…The circular and elliptical target models in our 2dimensional scenario correspond to circular and elliptical cylinders in an actual 3-dimensional scenario. Various human models are reviewed in [11] and [12]. The center of the torso (X a (t), Y a (t)), center of the left arm (X b (t), Y b (t)), and center of the right arm (X c (t), Y c (t)) move with time t according to…”

Section: System Modelmentioning

confidence: 99%

False Image Suppression in Two-Dimensional Shape Estimates of a Walking Human Using Multiple Ultra-Wideband Doppler Radar Interferometers

Yamazaki

Sakamoto

Taki

et al. 2016

IEICE Trans. Commun.

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Microwave systems have a number of promising applications in surveillance and monitoring systems. The main advantage of microwave systems is their ability to detect targets at distance under adverse conditions such as dim, smoky, and humid environments. Specifically, the wide bandwidth of ultra-wideband radar enables high range resolution. In a previous study, we proposed an accurate shape estimation algorithm for multiple targets using multiple ultra-wideband Doppler interferometers. However, this algorithm produces false image artifacts under conditions with severe interference. The present paper proposes a technique to suppress such false images by detecting inconsistent combinations of the radial velocity and time derivative of image positions. We study the performance of the proposed method through numerical simulations of a two-dimensional section of a moving human body, and demonstrate the remarkable performance of the proposed method in suppressing false image artifacts in many scenarios.

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“…Sensing-related problems include lighting conditions (poor illumination), thermal conditions (increased radiative heat), occlusion, and atmospheric phenomena (fog, mirages). Besides these, radar-based systems are independent of privacy-related conditions since the target information does not rely explicitly on target shapes but can be derived from microscale movements based on micro-Doppler signatures [33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Recent Progress of Machine Learning-Based Human Activity Recognition with Radar

Papadopoulos,

Jelali

2023

Applied Sciences

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The importance of radar-based human activity recognition has increased significantly over the last two decades in safety and smart surveillance applications due to its superiority in vision-based sensing in the presence of poor environmental conditions like low illumination, increased radiative heat, occlusion, and fog. Increased public sensitivity to privacy protection and the progress of cost-effective manufacturing have led to higher acceptance and distribution of this technology. Deep learning approaches have proven that manual feature extraction that relies heavily on process knowledge can be avoided due to its hierarchical, non-descriptive nature. On the other hand, ML techniques based on manual feature extraction provide a robust, yet empirical-based approach, where the computational effort is comparatively low. This review outlines the basics of classical ML- and DL-based human activity recognition and its advances, taking the recent progress in both categories into account. For every category, state-of-the-art methods are introduced, briefly explained, and their related works summarized. A comparative study is performed to evaluate the performance and computational effort based on a benchmarking dataset to provide a common basis for the assessment of the techniques’ degrees of suitability.

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Radar Micro-Doppler Signatures: Processing and Applications

Cited by 141 publications

References 0 publications

μBrain: An Event-Driven and Fully Synthesizable Architecture for Spiking Neural Networks

μBrain: An Event-Driven and Fully Synthesizable Architecture for Spiking Neural Networks

False Image Suppression in Two-Dimensional Shape Estimates of a Walking Human Using Multiple Ultra-Wideband Doppler Radar Interferometers

A Comparative Study on Recent Progress of Machine Learning-Based Human Activity Recognition with Radar

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