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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:2468-2322
2468-6557
2468-2322
DOI:10.1049/cit2.12148