Artificial neural network classification of surface reflectors and volume scatterers using sequential echoes acquired with a biomimetic audible sonar.

Abstract

This paper investigates classifying two target groups, surface reflectors (SR) and volume scatterers (VS), using echo envelope features. SR targets have convex surface patches that exhibit echo persistence over aspect angle, while VS targets are composed of random range-distributed and oriented reflectors producing echoes that become uncorrelated with small changes in aspect angle. The SR target group contains single-post (P1) and multiple-post (PM) types and the VS group contains Ficus benjamina (F) and Schefflera arboricola (S) foliage types with leaf areas that differ by a factor of 4. A biomimetic sonar emitting audible clicks acquired sequences of up to three binaural echoes from target views separated by 18°. Two artificial neural networks performing linear and nonlinear classification first differentiated SR/VS target groups and then P1/PM and F/S types. Classification performance improved with echo number, from a single monaural echo to three pairs of binaural echoes, demonstrating the benefit of sequential echoes. Linear and nonlinear classification of SR/VS targets achieved a minimum generalization error probability PEG = 0.003. Nonlinear P1/PM classification achieved PEG = 0.009 that was four times smaller than linear classification. Nonlinear F/S classification achieved PEG = 0.220, indicating that envelope features by themselves are inadequate to accurately differentiate foliage targets.