Human Target Detection, Tracking, and Classification Using 24-GHz FMCW Radar

Will, Christoph; Vaishnav, Prachi; Chakraborty, Abhiram; Santra, Avik

doi:10.1109/jsen.2019.2914365

Cited by 114 publications

(36 citation statements)

References 47 publications

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“…En este punto sería importante aclarar al estudiante que si bien este mapa permite una identificación rápida de forma visual no es muy práctico desde el punto de vista computacional puesto que un microcontrolador por sí solo no sería capaz de identificar los cuerpos sin un algoritmo adicional que se encargue de ello (en [31] se detalla con profundidad uno de estos algoritmos). Es en este punto donde la aplicación del algoritmo CFAR facilita obtener un límite de detección de forma dinámica de forma tal que se tome en cuenta la potencia con respecto al ruido (SNR).…”

Section: Figura 11 Mapa Range-dopplerunclassified

Evaluación de un radar fmcw como herramienta didáctica en las carreras de Ingeniería Automotriz y Telecomunicaciones

Ferrer¹

2020

Ingenius

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En las últimas décadas los sistemas avanzados de asistencia al conductor (ADAS) han evolucionado hasta estar disponibles en gran parte de los vehículos fabricados hoy en día; mantener actualizada la enseñanza en esta área es de vital importancia. Este artículo presenta un radar de onda continua modulado en frecuencia que trabaja en la banda de 24 GHz ISM. El propósito es evaluar su desempeño e idoneidad para usarse como herramienta didáctica en la enseñanza en las carreras de Ingeniería Automotriz y de Telecomunicaciones con énfasis en las asignaturas de Matemáticas y Telecomunicaciones con escenarios factibles de encontrar en los laboratorios universitarios. Para ello se describe el escenario de medición, así como el hardware, el firmware y un algoritmo genérico implementado en MATLAB basado en transformadas rápidas de Fourier para obtener mapas Range-Doppler que permiten junto con el algoritmo CFAR mejorar la detección de objetos al comparar con la detección a partir de un nivel fijo. Se presentan resultados que demuestran que la exactitud y precisión del radar se encuentran dentro de los parámetros para un radar de corto alcance para vehículos, encontrándose, además, una herramienta con gran potencial didáctico, con la cual los estudiantes pueden comprender las aplicaciones que hoy tienen las matemáticas en el ámbito de las telecomunicaciones, especialmente en radares que sirven a sistemas ADAS.

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Section: Figura 11 Mapa Range-dopplerunclassified

Evaluación de un radar fmcw como herramienta didáctica en las carreras de Ingeniería Automotriz y Telecomunicaciones

Ferrer¹

2020

Ingenius

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“…Therefore, it is necessary to fuse different sensors to solve a specific task, especially target detection and classification. In the past years, many sensor fusion methods have been proposed for autonomous driving applications [13][14][15][16]. In addition, [17][18][19][20][21] examine the existing problems of the current sensor fusion algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research of Target Detection and Classification Techniques Using Millimeter-Wave Radar and Vision Sensors

et al. 2021

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The development of autonomous vehicles and unmanned aerial vehicles has led to a current research focus on improving the environmental perception of automation equipment. The unmanned platform detects its surroundings and then makes a decision based on environmental information. The major challenge of environmental perception is to detect and classify objects precisely; thus, it is necessary to perform fusion of different heterogeneous data to achieve complementary advantages. In this paper, a robust object detection and classification algorithm based on millimeter-wave (MMW) radar and camera fusion is proposed. The corresponding regions of interest (ROIs) are accurately calculated from the approximate position of the target detected by radar and cameras. A joint classification network is used to extract micro-Doppler features from the time-frequency spectrum and texture features from images in the ROIs. A fusion dataset between radar and camera is established using a fusion data acquisition platform and includes intersections, highways, roads, and playgrounds in schools during the day and at night. The traditional radar signal algorithm, the Faster R-CNN model and our proposed fusion network model, called RCF-Faster R-CNN, are evaluated in this dataset. The experimental results indicate that the mAP(mean Average Precision) of our network is up to 89.42% more accurate than the traditional radar signal algorithm and up to 32.76% higher than Faster R-CNN, especially in the environment of low light and strong electromagnetic clutter.

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“…An additional reference antenna was used to remove multi-path clutter by observing the frequency domain correlation between the echo signal and the reference signal captured by the reference antenna [33]. The exponential moving average (EMA) algorithm extracted from the moving target indicator (MTI) was used to remove stationary targets while measuring vital signs and tracking multiple human subjects [34]- [36]. In [37], an adaptive EMA algorithm was proposed for UWB radars to suppress stationary clutter and maintain the signals reflected from a human subject.…”

Section: Introductionmentioning

confidence: 99%

Human Presence Sensing and Gesture Recognition for Smart Home Applications With Moving and Stationary Clutter Suppression Using a 60-GHz Digital Beamforming FMCW Radar

et al. 2021

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With the advancement of radio frequency (RF) assisted smart home technology, it is critical for the RF sensors deployed indoors to isolate the target of interest from unwanted clutter sources. This paper presents a novel method for suppressing both moving and stationary clutters in an indoor environment to localize stationary human subjects with a millimeter-wave frequency-modulated continuous-wave (FMCW) radar. The method derives its roots from the intrinsic high-pass filter (HPF) characteristic of the exponential moving average (EMA) algorithm, a preferred approach for background stationary clutter suppression. In this work, emphasis was laid on expanding the capability to detect and suppress unwanted moving clutter sources in the indoor environment along with stationary clutters, which has not been widely explored before. The proposed method removes motion artifacts so that the characteristic respiratory signal can be identified for human-aware localization. The paper provides experimental validation of the proposed method, wherein a 60-GHz FMCW radar with digital beamforming (DBF) capability was used to identify the 2-D location of a sitting human subject, with a moving window curtain in the background acting as a strong moving clutter source along with other stationary clutters. In addition, a lateral hand gesture recognition technique is presented, wherein the EMA algorithm was used to enhance the signature of the hand motion. The instantaneous position of the hand at the beginning and end of the gesture was determined to classify the gesture as a left-to-right or right-to-left hand swipe.INDEX TERMS Exponential moving average (EMA) algorithm, frequency-modulated continuous-wave (FMCW) radar, gesture recognition, human localization, moving clutter suppression, smart homes.

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Human Target Detection, Tracking, and Classification Using 24-GHz FMCW Radar

Cited by 114 publications

References 47 publications

Evaluación de un radar fmcw como herramienta didáctica en las carreras de Ingeniería Automotriz y Telecomunicaciones

Evaluación de un radar fmcw como herramienta didáctica en las carreras de Ingeniería Automotriz y Telecomunicaciones

Research of Target Detection and Classification Techniques Using Millimeter-Wave Radar and Vision Sensors

Human Presence Sensing and Gesture Recognition for Smart Home Applications With Moving and Stationary Clutter Suppression Using a 60-GHz Digital Beamforming FMCW Radar

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