A two-stage position estimation system is presented in : the first step is to ignore the additional path error and estimate an initial position the second step is to include the additional path error in the estimation problem to enhance the results using a variable projection method. The work presented in, uses evolutionary algorithms to optimize target localization in a wireless sensor network. The hybrid approach presented in, uses multipath information, machine learning, and propagation simulation tools to enhance the performance of outdoor TDOA systems. In, multipath information is used with the image theory to locate the emitter using only one sensor. However, it is still possible to use these signals for transmitter localization and tracking, if multipath exploitation is performed and information about the environment is available. The non-line-of-sight (NLOS) between the emitter and the sensors affects the position estimation in an outdoor scenario, because only reflections can be used to estimate the emitter localization. In a multipath scenario, the primary signal of an emitter and/or its reflections arrives at the sensors, resulting in a different number of possible targets. Node position estimation enables a large set of location-based services, boosts biological studies, and improves defense capabilities. The localization problem in communication systems has been on research spot for many years. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm. However, they achieved similar results in a mismatch experiment. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). We selected algorithms random forest and gradient boosting both considered efficient tools in the literature. We employ a machine learning (ML) algorithm to explore the multipath information. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. In outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate’s performance.
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