## 1 Introduction  
Accurate localization based on difference of arrival (TDOA) data collected from a single receiver and a set of transmitters is essential for many wireless sensor network (WSN) applications [1, 2]. The majority of localization technologies that have been developed and are widely in usage, such as the global positioning system (GPS) [3], ultrasonic-based systems [4], infrared-based systems [5], and RF-based systems [6], require the attachment of an additional device with its own power source to localize the target. Systems such as these are known as \"active localization systems,\" since the target directly obtains the range measurements, thus being directly involved in localization. Recent research focuses on device-free passive localization, in which the target to be located does not need to be equipped with additional devices or take any active part in the localization process and simply reflects the signals coming from the transmitters [7]. In practice, the GPS, as a prominent active system, shows significant drawbacks, such as excessive power consumption and poor localization performance in cases with weak satellite signals such us indoor areas [3]. Passive target localization has become one of the most popular techniques used to locate the passive targets in a variety of applications, including emergency rescue, intelligent transportation systems, radar, and mobile phone localization, due to its effectiveness as a replacement for GPS and other traditional positioning systems [8, 9]. The vast majority of TDOA-based localization techniques in the literature are classified as \"active systems\". In this paper, we outline the development of a device-free passive target localization system for real-world environments based on TDOA measurements, along with the appropriate design of a metaheuristic hybrid algorithm to obtain a solution to the considered problem. Therefore, this work considers the passive target localization model, where the positioning is performed based on TDOA measurements under the noisy conditions. The TDOA measurements are produced by subtracting the required time propagation of a signal emitted from the transmitter over the target reflected to a receiver from the direct signal propagation time captured at the receiver. Due to a nonlinear connection between the measured distances and the target's true location, estimating the target's unknown position is a difficult process. A reasonable assumption for the error distribution of TDOA measurements when modelling the considered passive target localization problem in a line of sight (LOS) environment is to model the measurement error as Gaussian-distributed random variables [2]. A number of estimation techniques, including nonlinear least squares (NLS) [10] and maximum likelihood (ML) [11] estimators, that do not have a closed form solution, are used in literature to tackle localization problems. With the linear least squares (LLS) and weighed linear least squares (WLS) methods, the NLS method's nonlinear objective function can be made linear, thus providing a closed-form solution [12]. The constrained weighted least squares (CWLS) approach is introduced to provide a solution, in closed form, that has higher quality [13]. In this regard, the relationship between the target location and an auxiliary variable is introduced into the CWLS optimization problem as the equality constraint that has the highest term of the second order. The ML estimator is another widely used estimation method that can improve estimation accuracy. When the statistical information regarding the distribution of the error in measurements is identified, the ML estimator method is straightforward to apply [11]. Due to the pronounced nonentities and complex landscape of the ML objective function, it is difficult to obtain a global optimum solution. In order to solve this type of optimization problem, various efficient optimization algorithms have been suggested in the literature.