## 1 Introduction  
Automation of the process of determining Surface Vehicle (SV) position coordinates in the coastal zone using radar, Light Detection And Ranging (LiDAR) or Charge-Coupled Device (CCD), and Complementary Metal-Oxide-Semiconductor (CMOS) cameras is currently the subject of much research [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. Much of it is directed towards developing algorithms for coordinate position estimation using the Extended Kalman Filter (EKF) in its classical form or its modified versions: Unscented Kalman Filter (UKF), Particle Filter (PF), Central Difference Kalman Filter (CDKF) [11; 12; 13; 14; 15; 16; 17; 18; 19], adaptive fault-tolerant extended Kalman filter (AFTKEY) [20], and resilient extended Kalman filter [21]. The last two of the mentioned algorithms work well in the case of partial loss of data from sensors--\"input\". The idea of the AFTEKF algorithm is to choose a suitable so-called fading factor based on the orthogonal theory and embed it into the prior state covariance matrix. The resilient extended Kalman filter is proposed for discrete-time nonlinear stochastic systems with sensor failures and random observer gain perturbations. Resilient extended Kalman filter is intended for discrete-time nonlinear stochastic systems with sensor failures and random observer gain perturbations. During his work, he assumes that the failure mechanisms of multiple sensors are independent of each other, with different failure rates. The locally unbiased robust minimum mean square filter is designed for state estimation under these conditions. In the case of SV positioning, the EKF input provides both parameters describing the SV's movement vector (e.g., course and speed)--determined, for example, by Doppler Velocity Log (DVL) and Fibre-Optic Gyroscope (FOG)--as well as the results from measurements (distances and bearings) taken relative to beacons with known positions (obtained, for example, from an electronic navigation chart)--determined, for instance, by radar or LiDAR, or by the camera. The EKF works perfectly when there is a misidentification of beacons, leading to the assignment of converted measurement results [3]. The algorithm treats such measurement results as having a gross error and, as a result, suppresses them. In particular, this problem may arise when the beacons are located very close to each other or when the movement of the SV from which the measurements are taken is disturbed (i.e., the SV tilts, causing uncontrolled changes in course and speed). However, several position coordinate estimation methods have also been developed in the field of surveying that can detect, suppress, and eliminate gross error measurements [22; 23; 24; 25; 26]. The best known and considered among the best are methods based on the so-called equivalent weight matrix. Of these, [PERSON]'s Geodetic Robust Adjustment (GRA) and Danish methods are the most widely used [25; 26]. However, there is an essential difference between EKF and GRA, regarding how gross error measurements are suppressed. EKF uses a gain matrix for this purpose, taking into account the historical covariance matrix. GRA, on the other hand, uses a covariance matrix built continuously, and considers the equivalent weighting matrix. For these reasons, the accuracy of position coordinate estimation by these methods can be expected to depend to varying degrees on the number and type of measurements, the way the beacons are positioned relative to each other, and the position relative to the SV beacons. A study is therefore warranted to allow a comparative assessment of the two methods. Since EKF is used for positioning in dynamics and GRA for positioning in statics, this type of research is not likely to be found in the literature. For these reasons, it is expected that their results may be unique, but they can indeed find their practical application in specific measurement conditions. This article presents research into the potential for improving surface vehicle positioning accuracy through the interchangeable use of GRA and EKF methods.