## 1 Introduction
The authors of [27] presented a position-estimation method based on a Deep-Learning (DL) algorithm that works directly on TDOA-based locating system channel impulse responses. The authors explained the signal and data possessing in depth and verified the method's effectiveness in various real-world scenarios. Although their method corresponds to traditional signal-processing methods under line-of-sight conditions, it outperforms earlier methods under significant multi-path propagation. To estimate the location and tracking of the RF emitter, Kalman filter (KF) and its expansion based on conventional geolocation measurements has been proposed in numerous studies [28; 29; 30]. In [31], the Extended Kalman filter (EKF) based on TDOA/FDOA was proposed to estimate target position and tracking. The main disadvantage of the EKF is that it relies on pre-estimation to estimate the variance of process and measurement noises, which may be difficult to accurately track the RFI position when predicting with a significant deviation from the actual values (measurement) [32]. In [33], the authors proposed two algorithms to solve the simultaneous mapping and localization (SLAM) problem. The first algorithm is an amortized constant-time coordinate descent algorithm for recovering these state estimates from the information form, and the second is an efficient algorithm for data association in Sparse Extended Information Filters (SEIFs) that takes logarithmic time, assuming that an efficient search tree searches for nearby features. The method was put into practice and compared to the EKF solution. In [34], the authors used EKF to create a unified Factor Graph (FG) architecture in which a practical AOA-based position detector accomplishes the observation process. Furthermore, the variance of observation errors, which EKF requires, was determined in real time by using both the AOA measurements and the expected target state. Compared to the traditional method, such a dynamic estimating strategy has higher performance robustness, especially when the sensing environment is unstable. Simulations show that the suggested system achieves lower Root Mean Square Error (RMSE) in many assessment settings and has a rapid convergence tendency. The Adaptive Extended Kalman Filter (AEKF) was proposed to reduce error estimation for similar problems [35]. Still, for high non-linearity, the error may increase because of the rotational speed of the emitter. Multiple Quadrature information Kalman filters were proposed in [36]; these can work in high non-linearity with a high-speed mobile sensor. In digital signal-processing, estimation has been considered an important challenge. For that, numerous studies have been deployed for estimations, and KF deemed as a highly powerful tool used for a linear dynamical system to estimate state and deems as a least-squares linear filter that provides an optimal recursive deployment. In [37], a hybrid localization approach based on the Particle Filter (PF) and particle swarm optimization algorithm are presented, focusing on the localization tasks when an a priori environment map is available. In addition, the approach was advocated for Distributed Bayesian filtering (DBF) in [38], where each node operates a distinct particle filter, and the collected consensus is sought on the sensor data alone or in conjunction with intermediate local filter estimates. However, it is quite challenging to design an optimal filter that functions in a nonlinear system. In addition, a study showed that the cubature Kalman filter (CKF) estimations are better when compared with the estimations of the unscented Kalman filter (UKF) [39]; however, its performances are worse compared with the Gauss-Hermite quadrature filter (GHQF) [40]. Therefore, for better performance, CKFs with random degrees of accuracy were proposed, and therefore it is stated that the high-degree CKFs could estimate better accuracy and stability performance such as GHQF but with less computational cost.