Deep neural network for object classification and optimization algorithms for 3D positioning in Ultrasonic Sensor Array

Abstract: Ultrasonic sensors are commonly used in automobiles to assist driving maneuvers, e.g., parking, because of their cost-effectiveness and robustness. This thesis investigated the feasibility of using an Ultrasonic Sensor Array to locate the 3D position of an object and also using the measurements from the sensor array to train a Convolutional Neural Network (CNN) to classify the objects. A simulated Ultrasonic Sensor array was built in COMSOL Multiphysics. The simulation of ultrasound used Ray Tracing technology to track the path of ultrasound rays. The readouts from the sensor array are used to formulate an optimization problem to address the 3D positioning of the object. We investigated the performance of two optimization methods in terms of the accuracy of the prediction and the efficiency of solving the problem. The average mean absolute error (MAE) and average mean squared error (MSE) of the Nelder-Mead method (without constraints) are 2.66 mm and 12.79 mm2 respectively, the average running time to predict one 3D position is 97.62 ms. The average MAE and average MSE of Powell’s method (with constraints) are 2.84 mm and 23.66 mm2 respectively, average running time to predict one 3D position is 84.68 ms. The result of Powell’s method (without constraints) is much worse than the above two, its average MAE and MSE are 24.93 mm and 7559.46 mm2, average running time is 238.30 ms. The readouts from the sensor array are also used to build eight different datasets of which the data structures are different combinations of the information from the readouts. Each of these eight data sets is used to train a CNN, and the classification accuracy of each CNN indicates that how well the data structure represents the objects. The results showed that the CNN trained by stacked time array 5×5×3 had the best classification accuracy among eight datasets, the classification accuracy on the test set is 85.05%.