## 1 Introduction
The optimization approaches for geolocation and target tracking is: (i) A Least Square (LS) and Non-linear Least Square (NLS), (ii) Maximum Likelihood (ML), (iii) Genetic Algorithm (GA) approach, and (v) Constrained Weighted Least Square (CWLS) and Weighted Least Square (WLS) methods [13,14]. Mostly, there are two approaches for solving the non-linear equations: (a) The NLS, which is a direct approach for solving non-linear least squares, or CWLS, which is a constrained framework [15]. (b) An iterative scheme so-called the Taylor series estimation, otherwise known as Gauss-Newton interpolation. This utilizes the solution of a simultaneous set of non-linear algebraic geolocation equations, beginning with an initial guess and updating the guess at each step by finding the local linear least-sum-squared-error correction [16]. Various studies have focused on the ambiguity problem of geolocation methods. For example, the authors of [17] solved the problem of ambiguity for some neighbouring nodes and derived Tangent Linear Approximation (TLA) for the non-linear localization approach, which contributed to incorporating the effect of the neighbours' position ambiguity into Belief Propagation (BP) localization. Moreover, Kalman Filtering (KF) based on geolocation measurements can be employed to estimate emitter tracking. It is a useful tool for solving navigation and tracking problems. In order to estimate the emitter position, we first must have a reliable estimate of the emitter's present position. Kalman filtering provides a tool to obtain that reliable estimate. It is an algorithm, which uses a form of feedback control. The filter estimates the emitter location. At the same time, it obtains measurement feedback. The KF algorithm operates in two steps. The first step is a prediction: in this step, the filter produces estimates of the current state variables within their uncertainties. The second step is a correction: in this step, the filter will update the measurement using a weighted average, while giving more weight to estimates in higher certainty [18]. Since 1960, several versions of the KF are developed. This filter was improved to be compatible with non-linear systems rather than linear systems, such as the Extended Kalman Filter (EKF), Adaptive Extended Kalman Filter (AEKF), Unscented Kalman Filter (UKF), Cubature Kalman Filter (CKF), and Particle Filter (PF) [19,20]. From the methods and approaches mentioned, it is observed that the current interest in using geolocation and target tracking approaches for RF emitters is increased rapidly. In response to requests imposed by advanced applications that need to geolocate and track the RF emitter at high speeds of movement and rotation, Quadrature Information Kalman Filters (QIKFs) and Gauss-Hermite Quadrature Filter (GHQF), as well as \(H_{\infty}\), have been developed to solve the non-linearity and uncertainty problem [21, 22, 23, 24]. To process such complex algorithms, we have started by introducing a summary of the most popular methods for geolocation and target tracking [12; 13; 25]. In addition, we have conducted a focused exploration of optimizing-geolocation-measurements approaches, as well as optimal-state-estimation-utilizing-filtering approaches [14; 15; 16; 17; 18; 19; 20]. In contrast to the previous studies and surveys, the literature review of this survey focused on the state-of-the-art research articles for geolocation and target tracking approaches for wireless and satellite systems. Table 1 presents recent surveys tackling similar topics for our survey [5; 7; 26; 27; 28; 29]. Based on what was introduced, the main contribution of this survey is presenting the state-of-the-art of conventional methods and techniques, compared with more advanced and modern approaches in geolocation and tracking that were developed under uncertainty, biases and coloured noises [21; 22; 30].