## 1 Introduction  
Tracking filters are vital in target tracking problems. By using these filters, we can tackle real estimation problems, which can also help reduce tracking errors. The \(\alpha\)-\(\beta\) family of filters was developed and created during the Cold War [1]. Usually, this family of filters is utilized to track stable velocity and constant acceleration by using the Kalman filter as a continual state filter. However, the ability of these filters to accurately track high-momentum maneuvering targets is minimal, characterized by jerky motions. The alpha-beta (\(\alpha\)-\(\beta\)) filter algorithm [2] is a suitable method for the observer; it is mainly used for data smoothing, control, and estimation. This algorithm is very lightweight and has the same functionality as the Kalman filter. If we compare the \(\alpha\)-\(\beta\) filter's performance with the Kalman filter [3] and the linear filter, we can see that the \(\alpha\)-\(\beta\) filter performs better than these filters because it does not require a complex system model. Another exciting feature of this filter is that \(\alpha\)-\(\beta\) requires less memory and computation time. State-of-the-art progress in technology, including the deep belief network (DBN) [4], the deep extreme learning machine (ELM) [5], classification and regression trees (CARTs) [6], the support vector machine (SVM) algorithm [7], and other machine-learning advancements have improved our living standards and assist us in different ways. These methods are mainly based on our current knowledge and the data extracted from existing and past data, which enable us to make progressive decisions for the future to minimize losses and achieve the maximum benefits [2]. To enable us to benefit from these algorithms to progress as much as possible, first, we train them by using historical data; the more data an algorithm has access to, the more accurate the results are. When the training stage of such an algorithm is finished, the algorithm is ready for use in the designed application. However, at this stage, one issue that arises is that these algorithms are designed for a particular task and setting; therefore, the performance of such algorithms degrades over time as their operational environments change. Numerous well-known algorithms are used to overcome this limitation [1], such as DELM, DBN, and SVM algorithms, and the stacked generalization [8] technique has been proposed to improve the accuracy in prediction and classification. When this machine-learning technique [9] is attached to an algorithm, the performance may improve in different ways. We applied these three algorithms as a learning unit to the \(\alpha\)-\(\beta\) filter to boost the performance of the filters. The alpha-beta filter algorithm is the most basic linear observer method, and it is utilized to evaluate problem and application control. We obtained this method from the Kalman filter. The Kalman filter [10] is the most complex method; in comparison, it is easy to use the alpha-beta filter in different applications. It also displays a better performance than other filters. In this paper, new ML-based algorithms were implemented, and three different combinations of these algorithms were used to enhance the accuracy of the \(\alpha\)-\(\beta\) filter algorithm and to tune the parameters of \(\alpha\) and \(\beta\) in dynamic conditions. The proposed system uses the deep belief network, support vector machine, and deep extreme learning machine as three different learning algorithms. Then these learned parameters are trained by the machine-learning algorithms, tuned to the \(\alpha\)-\(\beta\) filter algorithm as a prediction module, and provide the final predicted results. The MAE and RMSE were used to calculate the performance of the proposed \(\alpha\)-\(\beta\) filter with different learning algorithms. As evaluated by comparing the proposed algorithm with the typical alpha-beta filter algorithm, it was shown that the proposed system provides results with better accuracy.