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...

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Published in:	CAAI Transactions on Intelligence Technology Vol. 8; no. 4; pp. 1124 - 1139
Main Authors:	Khan, Junaid, Lee, Eunkyu, Kim, Kyungsup
Format:	Journal Article
Language:	English
Published:	Beijing John Wiley & Sons, Inc 01.12.2023 Wiley
Subjects:	Accuracy Algorithms alpha beta filter Artificial intelligence artificial neural network Artificial neural networks Back propagation Back propagation networks Classification Deep learning Kalman filters Machine learning Methods Modules navigation Navigation systems Neural networks Performance evaluation Performance prediction prediction accuracy Root-mean-square errors Sensors target tracking problems Temperature sensors Tracking systems
ISSN:	2468-2322, 2468-6557, 2468-2322
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Abstract	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.
AbstractList	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. Abstract 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.
Author	Lee, Eunkyu Kim, Kyungsup Khan, Junaid
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Snippet	The alpha–beta filter algorithm has been widely researched for various applications, for example, navigation and target tracking systems. To improve the... Abstract The alpha–beta filter algorithm has been widely researched for various applications, for example, navigation and target tracking systems. To improve...
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SubjectTerms	Accuracy Algorithms alpha beta filter Artificial intelligence artificial neural network Artificial neural networks Back propagation Back propagation networks Classification Deep learning Kalman filters Machine learning Methods Modules navigation Navigation systems Neural networks Performance evaluation Performance prediction prediction accuracy Root-mean-square errors Sensors target tracking problems Temperature sensors Tracking systems
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Title	A higher prediction accuracy–based alpha–beta filter algorithm using the feedforward artificial neural network
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