Object Classification Technique for mmWave FMCW Radars using Range-FFT Features

Bhatia, Jyoti; Dayal, Aveen; Jha, Ajit; Vishvakarma, Santosh Kumar; Soumya, J; Srinivas, M.B.; Yalavarthy, Phaneendra K.; Kumar, Abhinav; Lalitha, V.; Koorapati, Sagar; Cenkeramaddi, Linga Reddy

doi:10.1109/comsnets51098.2021.9352894

Cited by 15 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Related literature Indoors [182], [183], [185]- [194], [197]- [204], [206], [208]- [211], [241], [243]- [245], [247], [249], [250] Outdoors [185], [205], [207], [228], [242], [244]- [246]…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications

Shastri

Valecha

Bashirov

et al. 2022

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

The commercial availability of low-cost millimeterwave (mmWave) communication and radar devices is starting to improve the adoption of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifthgeneration (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices.This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We overview key concepts about mmWave signal propagation and system design, detailing approaches, algorithms and applications for mmWave localization and sensing. Several dimensions are considered, including the main objectives, techniques, and performance of each work, whether they reached an implementation stage, and which hardware platforms or software tools were used.We analyze theoretical (including signal processing and machine learning), technological, and implementation (hardware and prototyping) aspects, exposing under-performing or missing techniques and items towards enabling a highly effective sensing of human parameters, such as position, movement, activity and vital signs. Among many interesting findings, we observe that device-based localization systems would greatly benefit from commercial-grade hardware that exposes channel state information, as well as from a better integration between standardcompliant mmWave initial access and localization algorithms, especially with multiple access points (APs). Moreover, more advanced algorithms requiring zero-initial knowledge of the environment would greatly help improve the adoption of mmWave simultaneous localization and mapping (SLAM). Machine learning (ML)-based algorithms are gaining momentum, but still require the collection of extensive training datasets, and do not yet generalize to any indoor environment, limiting their applicability. Manuscript received xxxx xx, xxxx . . .

show abstract

“…Related literature Indoors [182], [183], [185]- [194], [197]- [204], [206], [208]- [211], [241], [243]- [245], [247], [249], [250] Outdoors [185], [205], [207], [228], [242], [244]- [246]…”

Section: Discussionmentioning

confidence: 99%

“…The authors of [246] perform object classification considering three classes: humans, drones and cars. The feature set used in their algorithm consists of radial range, area under the peak, width of the peak, height and standard deviation of the peak in the range-FFT domain.…”

Section: Object Detectionmentioning

confidence: 99%

A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications

Shastri

Valecha

Bashirov

et al. 2022

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…The system uses signal-to-noise ratio (SNR) and channel state information (CSI) measurements, with a classification method for human pose recognition and seat occupancy sensing. Besides vitalsign monitoring, novel mmWave radar applications for object and material classification have recently been proposed [38]- [40].…”

Section: Related Workmentioning

confidence: 99%

Seat-Occupancy Detection System and Breathing Rate Monitoring Based on a Low-Cost mm-Wave Radar at 60 GHz

et al. 2021

View full text Add to dashboard Cite

This work presents a seat-occupancy detection system based on a PCR (Pulsed Coherent Radar) at the unlicensed 60 GHz ISM band. The radar can measure distances with sub-millimeter resolution. Therefore, the system can detect the presence of people occupying the seats by measuring small movements of the body, such as those produced by breathing. In consequence, the system not only measures seatoccupancy but also breathing rate, which is estimated from the amplitude peaks after filtering the amplitude measured by the radar to remove the noise. The effect of the car vibrations and random body movement is experimentally studied. Measurements performed with adults, as well as with a baby emulator sitting in a child seat, are presented. Comparison of the system with another that measures the breath based on airflow shows good agreement and permits its validation.

show abstract

“…The additional features of an intermediate frequency (IF) signal's range FFT profile have not been thoroughly investigated. It has been demonstrated in [38] that by utilizing the features from the range FFT profile, additional information about the objects can be extracted.…”

Section: Introductionmentioning

confidence: 99%

Classification of Targets Using Statistical Features from Range FFT of mmWave FMCW Radars

et al. 2021

Self Cite

View full text Add to dashboard Cite

Radars with mmWave frequency modulated continuous wave (FMCW) technology accurately estimate the range and velocity of targets in their field of view (FoV). The targeted angle of arrival (AoA) estimation can be improved by increasing receiving antennas or by using multiple-input multiple-output (MIMO). However, obtaining target features such as target type remains challenging. In this paper, we present a novel target classification method based on machine learning and features extracted from a range fast Fourier transform (FFT) profile by using mmWave FMCW Radars operating in the frequency range of 77–81 GHz. The measurements are carried out in a variety of realistic situations, including pedestrian, automotive, and unmanned aerial vehicle (UAV) (also known as drone). Peak, width, area, variance, and range are collected from range FFT profile peaks and fed into a machine learning model. In order to evaluate the performance, various light weight classification machine learning models such as logistic regression, Naive Bayes, support vector machine (SVM), and lightweight gradient boosting machine (GBM) are used. We demonstrate our findings by using outdoor measurements and achieve a classification accuracy of 95.6% by using LightGBM. The proposed method will be extremely useful in a wide range of applications, including cost-effective and dependable ground station traffic management and control systems for autonomous operations, and advanced driver-assistance systems (ADAS). The presented classification technique extends the potential of mmWave FMCW Radar beyond the detection of range, velocity, and AoA to classification. mmWave FMCW Radars will be more robust in computer vision, visual perception, and fully autonomous ground control and traffic management cyber-physical systems as a result of the added new feature.

show abstract

Object Classification Technique for mmWave FMCW Radars using Range-FFT Features

Cited by 15 publications

References 15 publications

A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications

A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications

Seat-Occupancy Detection System and Breathing Rate Monitoring Based on a Low-Cost mm-Wave Radar at 60 GHz

Classification of Targets Using Statistical Features from Range FFT of mmWave FMCW Radars

Contact Info

Product

Resources

About