An ultrasonic ranging interference error prediction method based on an uncertainty perception double-flow model

Abstract

The application provides an ultrasonic ranging interference error prediction method based on an uncertainty perception double-flow model. The spatial position of an ultrasonic emission device and an ultrasonic ranging sequence are extracted as space-time features to construct a double-flow space-time network. The model simultaneously outputs predicted results and two kinds of uncertainties by combining a Bayesian neural network, thereby achieving high robustness and ensuring that the method is more reliable when migrated to different external ultrasonic interference environments. The double-flow neural network simultaneously captures space-time features and fuses predictions by using a long short-term memory network and a double-separated attention graph neural network, thereby comprehensively learning the influence of space-time attributes of the physical world on the prediction accuracy of ultrasonic ranging. The Bayesian network variational inference part introduces a mixed prior, thereby accelerating the convergence speed of the variational distribution and the real distribution in the model training process and realizing faster and more efficient training.

Images