A higher prediction accuracy-based alpha-beta filter algorithm using the feedforward artificial neural network

The alpha-beta filter algorithm has been widely researched for various applications, for example, navigation and target tracking systems. To improve the dynamic performance of the alpha-beta filter algorithm, a new prediction learning model is proposed in this study. The proposed model has two main components: (1) the alpha-beta filter algorithm is the main prediction module, and (2) the learning module is a feedforward artificial neural network (FF-ANN). Furthermore, the model uses two inputs, temperature sensor and humidity sensor data, and a prediction algorithm is used to predict actual sensor readings from noisy sensor readings. Using the novel proposed technique, prediction accuracy is significantly improved while adding the feed-forward backpropagation neural network, and also reduces the root mean square error (RMSE) and mean absolute error (MAE). We carried out different experiments with different experimental setups. The proposed model performance was evaluated with the traditional alpha-beta filter algorithm and other algorithms such as the Kalman filter. A higher prediction accuracy was achieved, and the MAE and RMSE were 35.1%-38.2% respectively. The final proposed model results show increased performance when compared to traditional methods.

Automated summary

In this study, a new prediction learning model combining the alpha-beta filter algorithm and a feed-forward backpropagation neural network (FF-ANN) is proposed. The model improves prediction accuracy by using temperature and humidity sensor data and reduces errors. Experiments show that the proposed model outperforms traditional methods.