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Spotlight Synthetic Aperture Radar Signal Processing Algorithms

This paper reviews recent progress of portable short-range noncontact microwave radar systems for motion detection, positioning, and imaging applications. With the continuous advancements of modern semiconductor technologies and embedded computing, many functionalities that could only be achieved by bulky radar systems in the past are now integrated into portable devices with integrated circuit chips and printed circuits boards. These portable solutions are able to provide high motion detection sensitivity, excellent signal-to-noise ratio, and satisfactory range detection capability. Assisted by on-board signal processing algorithms, they can play important roles in various areas, such as health and elderly care, veterinary monitoring, human-computer interaction, structural monitoring, indoor tracking, and wind engineering. This paper reviews some system architectures and practical implementations for typical wireless sensing applications. It also discusses potential future developments for the next-generation portable smart radar systems.

A Review on Recent Progress of Portable Short-Range Noncontact Microwave Radar Systems

Hyperspectral Imaging: Techniques for Spectral Detection and Classification is an outgrowth of the research conducted over the years in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for subpixel detection and mixed pixel classification. This text is the first of its kind on the topic and can be considered a recipe book offering various techniques for hyperspectral data exploitation. In particular, some known techniques, such as OSP (Orthogonal Subspace Projection) and CEM (Constrained Energy Minimization) that were previously developed in the RSSIPL, are discussed in great detail. This book is self-contained and can serve as a valuable and useful reference for researchers in academia and practitioners in government and industry.

Hyperspectral Imaging Techniques For Spectral Detection And Classification

Radar, as one of the sensors for human activity recognition (HAR), has unique characteristics such as privacy protection and contactless sensing. Radar-based HAR has been applied in many fields such as human–computer interaction, smart surveillance and health assessment. Conventional machine learning approaches rely on heuristic hand-crafted feature extraction, and their generalization capability is limited. Additionally, extracting features manually is time–consuming and inefficient. Deep learning acts as a hierarchical approach to learn high-level features automatically and has achieved superior performance for HAR. This paper surveys deep learning based HAR in radar from three aspects: deep learning techniques, radar systems, and deep learning for radar-based HAR. Especially, we elaborate deep learning approaches designed for activity recognition in radar according to the dimension of radar returns (i.e., 1D, 2D and 3D echoes). Due to the difference of echo forms, corresponding deep learning approaches are different to fully exploit motion information. Experimental results have demonstrated the feasibility of applying deep learning for radar-based HAR in 1D, 2D and 3D echoes. Finally, we address some current research considerations and future opportunities.

https://www.mdpi.com/2072-4292/11/9/1068/pdf

A Survey of Deep Learning-Based Human Activity Recognition in Radar

Various medical systems exist for monitoring people in daily life, but they typically require the patient to wear a device, which can create discomfort and can limit long-term use. Contactless vital-sign monitoring would be preferable, but such technology is challenging to develop as it involves weak signals that need to be accurately detected within a practical distance, while being reliably distinguished from unwanted disturbance. Here, we show that a radar-based sensor can be used to monitor the individual vital signs (heartbeat and respiration) of multiple people in a real-world setting. The contactless approach, which does not require any body parts to be worn, uses two antennas (one transmitter and one receiver) and algorithms for target tracking and rejection of random body movements. As a result, it is robust against moderate random body movements (limb movements and desk work) and can keep track of individual people during vigorous movement (such as walking and standing up). A radar-based sensor can monitor the individual vital signs—heartbeat and respiration—of multiple people in a real-world setting, keeping track of individual people during vigorous movement.

Vital-sign monitoring and spatial tracking of multiple people using a contactless radar-based sensor

This paper presents a portable radar system for short-range localization, inverse synthetic aperture radar imaging, and vital sign tracking. The proposed sensor incorporates frequency-modulated continuous-wave (FMCW) and interferometry (Doppler) modes, which enable this radar system to obtain both absolute range information and tiny vital signs (i.e., respiration and heartbeat) of human targets. These two different operation modes can be switched through an on-board microcontroller. To simplify the system, the proposed radar utilizes the audio card of a laptop to sample the baseband signal. The FMCW mode of the radar uses operational-amplifier-based circuits to generate an analog sawtooth signal and a reference pulse sequence (RPS). The RPS is locked to the sawtooth signal to obtain coherence for the radar system. For the interferometry mode, a low-intermediate-frequency modulation method is implemented to avoid the slow vital signs from being distorted by the high-pass filter of the audio card. Several experiments were carried out to reveal the capability and distinct operational features of the proposed portable hybrid radar. The experiments also showed that the system can easily detect glass, which is usually difficult to identify for optical-based sensors. In addition, 2-D scanning in a complex environment revealed that the proposed radar was able to differentiate human targets from other objects. Moreover, ISAR images were used to isolate moving human targets from surrounding clutter. Finally, the proposed radar also demonstrated its ability to accurately measure vital signs when a human subject sits still.

A Portable FMCW Interferometry Radar With Programmable Low-IF Architecture for Localization, ISAR Imaging, and Vital Sign Tracking

This paper presents a noncontact human–machine interface which is capable of sensing and indicating the position of a human hand that is approaching a probing coil. High sensitivity is achieved by utilizing a structure including two oscillators and a mixer to compare and extract the electromagnetic shift between two probing coils. The probing coils chosen in this system have a planar structure and small size, which can be easily built on a printed circuit board or integrated into a portable device for hand detection. The hand–coil interface is analyzed quantitatively for both the inductive and the capacitive links. Based on the analysis, a circuit model for the hand–coil interaction is presented. The proposed sensing circuit was built on an evaluation board with two commercial planar coils. A small phantom was used to imitate the hand electrical characteristic for a reliable and repeatable experimental setup. The measured results showed a hand-detection sensitivity of 10 kHz/mm at the output of the mixer stage and 30 mV/mm at the final output of the proposed interface.

Noncontact Human–Machine Interface With Planar Probing Coils in a Differential Sensing Architecture

The interaction between human and smart phones is mainly based on press buttons and touch screens. As wireless charging based on wireless power transfer (WPT) coils is standardized and becoming a competitive feature for smart phones in recent years, it is also possible to control smart phones without contact by interacting with their wireless charging coils. In this paper, a non-contact method to interact with smart phones based on their wireless charging coils is proposed and investigated. A Colpitts oscillator is built and tested using a standard WPT coil as part of the resonant tank elements. Experiment results show that the resonant frequency of the coil is heavily influenced by hand movement, which could be further interpreted as the interaction between human and smart phones. By analyzing the resonant frequency changes of multiple coils embedded into a phone, it is possible to determine which hand movement is performed by a user.

Non-contact hand interaction with smart phones using the wireless power transfer features

This paper presents a non-contact human machine interface to sense the position or bio-activity of human body. Based on the electrical response of power transferring coil to the physical shapes, impedance, and dielectric constant of human body, the proposed architecture can be easily integrated and allows the smart devices to read information including physical position or cardiorespiratory activity of human body. A commercial WPT/NFC coils incorporated in an LC resonate tank senses the human activities and changes the oscillating frequency accordingly. High sensitivity is achieved by utilizing a mixer that down converts the resonant frequency to baseband from a carrier frequency of several MHz. Experiment was carried out by correlating the frequency shifts to the hand-coil distance. As an example, the result shows good indication of hand position when the distance is below 10mm. By proper arrangement of multiple coils, the information of hand position can be easily extracted through further processing by smart devices.

Non-contact human machine interface based on bio-interaction with wireless power transfer features

Disclosed is a system and method for controlling handheld devices without contact by interacting with their wireless charging coils or other inductive coil antennae. The present disclosure utilizes the interaction between human body and the coil wherein the coil is used to send alternating magnetic field to interact with a control signal, such as a hand, instead of simply using the coil in the smart phone as a power receiver. The hand movement in front of the wireless charging coil changes the coil's conductivity distribution, which creates effective coil impedance also known as reflected impedance.

Chenhui Liu

Papers

A Portable FMCW Interferometry Radar With Programmable Low-IF Architecture for Localization, ISAR Imaging, and Vital Sign Tracking

Noncontact Human–Machine Interface With Planar Probing Coils in a Differential Sensing Architecture

Non-contact hand interaction with smart phones using the wireless power transfer features

Non-contact human machine interface based on bio-interaction with wireless power transfer features

System and method for non-contact interaction with mobile devices