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

作　　者：Junaid Khan Eunkyu Lee Kyungsup Kim 

机构地区：[1]Department of Environmental&IT Engineering,Chungnam National University,Daejeon,Republic of Korea [2]Research and Development Department,SafeTech Research,Inc,Daejeon,Republic of Korea [3]Department of Computer Engineering,Chungnam National University,Daejeon,Republic of Korea

出　　处：《CAAI Transactions on Intelligence Technology》2023年第4期1124-1139,共16页智能技术学报（英文）

基　　金：supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2020‐0‐01441,Artificial Intelligence Convergence Research Center(Chungnam National University));“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS‐004).

摘　　要：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.

关 键 词：alpha beta filter artificial neural network navigation prediction accuracy target tracking problems 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论] TP18[自动化与计算机技术—计算机科学与技术]