River Surface Analysis and Characterization Using FMCW Radar

Abstract

Today's warning systems for floods or droughts require sensor applications that provide a vast array of information. Current systems provide insight into either the water level or the river velocity. In order to obtain additional parameters for the characterisation of the flow behaviour in a non-contact manner, a frequency modulated continuous wave (FMCW) Radar with chirp sequence is used. Since these sensors provide range and velocity information but also enable gathering of additional parameters. The presented measurements in this contribution were performed at four different rivers with a commercially available radar sensor. The results of the classical post-processed two-dimensional fast Fourier transform are used as basis for an image-wise processing approach to obtain additional features for classifying the behaviour of the river surface. For this purpose, the frame-wise enveloping velocities are depicted in a time sequence. Due to processing the detected reflection patterns in relation to time, a characteristic pattern for river flow profiles can be extracted. By reducing the information using time averaging, characteristic features for different flows can be extracted from the spatial envelope velocity distribution. In particular, the resulting insights lead to characteristic features for single flow distributions. That enables novel monitoring systems with new possibilities to classify rivers using radar sensors.

Reconstruction of the Frequency-Wavenumber Spectrum of Water Waves With an Airborne Acoustic Doppler Array for Noncontact River Monitoring

“…It could be rapidly deployed on unmanned aerial vehicles [46] to monitor floods and representative sections of river reaches. The approach could be extended to electromagnetic waves, complementing existing techniques for the reconstruction of dynamic rough surface profiles [47], [48] or for the monitoring of the ocean [49]- [54] and rivers [55]- [57]. Fabio Muraro received his M.Sc.…”

Machine Learning-Based Human Posture Identification from Point Cloud Data Acquisitioned by FMCW Millimetre-Wave Radar

“…Compared with the traditional calibration method, the calibration error is reduced significantly. Other applications include automotive radar [ 10 ], drone detection [ 11 ], snow research [ 12 ], contactless measurement [ 13 , 14 ], vital sign detection [ 15 ], remote sensing [ 16 ], gait recognition [ 17 ], etc. With the increasing demand for healthcare technology, surveillance, and human-machine interfaces, the use of FMCW mmWave radar to recognize human postures has become an important topic.…”

2D-Unitary ESPRIT Based Multi-Target Joint Range and Velocity Estimation Algorithm for FMCW Radar

“…The frequency shift keying (FSK) [5] or the phase shift keying (PSK) continuous wave radar achieves range-velocity estimation of multi-targets simultaneously, but the estimation is not accurate when the targets are relatively stationary. Frequency Modulated Continuous Wave (FMCW) radar [6,7], which utilizes frequency modulation such as triangle wave and sawtooth wave, can provide range and velocity estimates of multiple targets simultaneously.…”