Classifying Surface Roughness With CTFM Ultrasonic Sensing

Abstract

Roughness is a characteristic of a surface that is a function of its geometry. Ultrasonic sensing in air provides range, area, and angle information because the surface geometry determines the characteristics of the echo. The authors introduce the "spatial-angle-filter model" to explain the impact of surface roughness on the echo. On the basis of this model, they design a set of features for use in classifying surfaces. The quality of the features and the classification is measured with the Mahalanobis distance. The resultant system is able to achieve 99.73% classification of a set of 12 surfaces using five features. Abstract-Roughness is a characteristic of a surface that is a function of its geometry. Ultrasonic sensing in air provides range, area, and angle information because the surface geometry determines the characteristics of the echo. The authors introduce the "spatial-angle-filter model" to explain the impact of surface roughness on the echo. On the basis of this model, they design a set of features for use in classifying surfaces. The quality of the features and the classification is measured with the Mahalanobis distance. The resultant system is able to achieve 99.73% classification of a set of 12 surfaces using five features. Disciplines Physical Sciences and MathematicsIndex Terms-Continuous transmission frequency modulated (CTFM) ultrasonic sensor, Mahalanobis classifier, spatial-angle filter, surface roughness, ultrasonic sensing, wheeled mobile robot.

Estimating bearing angles by encoding ultrasonic transmissions at different frequencies

“…As has been mentioned, the maximum correlation values decrease at 1.5m. Similar conclusions had been already achieved in previous works, based on FFT computing or on filter banks for the reception stage [7] [8]. The main advantage of this proposal is that the ultrasonic signal encoding allows lower signal-to-noise ratios (SNR).…”

“…In these proposals, the ultrasonic emission is often carried out at different frequencies, using a bandwidth from 20kHz up to 100kHz. The behaviour presented by the environment and the transducers (specially their emission/reception pattern) depends on the frequency, so it can be considered in order to obtain as much information as possible [7] [8]. Most developments are based on the emission of sinusoidal tones with a sweeping frequency.…”

Automotive System for Remote Surface Classification

“…The objectives of the papers [9,10,11,12,13,14,15,16] were to classify surfaces using ultrasonic signals, primarily for use in robotics. In the case of robotic applications the task of classification is simplified comparing with automotive, as robots speed is usually low and the distance is limited to a few meters.…”

Continuous Transmission Frequency Modulation Detection under Variable Sonar-Target Speed Conditions

“…In a more recent work, a CTFM sonar was mounted on a mobile agent to classify different rough surfaces [16]. The spectrum of the demodulation output signal was averaged to reduce the influence of Doppler shift to the range profile.…”