A Gradient Descent Multi-Algorithm Grid Search Optimization of Deep Learning for Sensor Fusion

Sensor fusion approaches combine data from a suite of sensors into an integrated solution that represents the target environment more accurately than that produced by an individual sensor. Deep learning (DL) based approaches can address challenges with sensor fusion more accurately than classical approaches. However, the accuracy of the selected approach can change when sensors are modified, upgraded or swapped out within the system of sensors. Historically, this can require an expensive manual refactor of the sensor fusion solution.This paper develops 12 DL-based sensor fusion approaches and proposes a systematic and iterative methodology for selecting an optimal DL approach and hyperparameter settings simultaneously. The Gradient Descent Multi-Algorithm Grid Search (GD-MAGS) methodology is an iterative grid search technique enhanced by gradient descent predictions and expanded to exchange performance measure information across concurrently running DL-based approaches. Additionally, at each iteration, the worst two performing DL approaches are pruned to reduce the resource usage as computational expense increases from hyperparameter tuning. We evaluate this methodology using an open source, time-series aircraft data set trained on the aircraft’s altitude using multi-modal sensors that measure variables such as velocities, accelerations, pressures, temperatures, and aircraft orientation and position. We demonstrate the selection of an optimal DL model and an increase of 88% in model accuracy compared to the other 11 DL approaches analyzed. Verification of the model selected shows that it outperforms pruned models on data from other aircraft with the same system of sensors.