Multi-target tracking is considered to be a significant issue in various areas of monitoring, supervision, and updated communication services. It is a logical, generalized, single-target tracking problem. Therefore, it is of paramount importance to apply filters to meas
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Multi-target tracking is considered to be a significant issue in various areas of monitoring, supervision, and updated communication services. It is a logical, generalized, single-target tracking problem. Therefore, it is of paramount importance to apply filters to measure the direction or relative distance of the target from the viewer. Not showing the position of the sensor is the functional advantage of such sensors. One of the main issues in tracking is the dependence of estimation accuracy on the moving path of the viewer when the sensor only measures the direction of the target. With this background in mind, it is essential to estimate the position of the target. The present study aimed to determine the optimal path of the viewer in the tracking of two moving targets in order to improve the tracking performance. Target tracking was performed by a viewer only by measuring the direction of the target toward the viewer. Initially, the viewer path was introduced as a mathematical profile, and its coefficients were determined using an optimization algorithm, which demonstrated the lowest error rate in target tracking using the Kalman filter as an optimal estimator. Afterwards, another path was introduced, which was developed based on the estimates obtained by two Kalman filters, followed by the unscented Kalman filter. At the final stage, the most efficient method to continue the desired viewer path was proposed based on the comparison of the two methods, and the results of the optimization path were obtained using a multi-objective genetic algorithm.
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