Multi-Sensor Control Algorithm For Multi-Target Search And Tracking With Limited Sensor Field-of-View

In recent years,multi-sensor control technology based on random finite set has attracted more and more attention in the field of multi-target tracking.Sensor control refers to assigning the optimal state to sensors through decision optimization to obtain better quality measurements,so as to improve the accuracy of multi-target state estimation,which is of great significance to solve the multi-target tracking problem in complex environment.Most of the existing researches on sensor control assume that the sensor can observe all targets,but the Field-of-View(Fo V)of the sensor is limited in practical applications.While maintaining tracking of discovered targets in the Fo V,the search for undiscovered targets outside the Fo V should also be taken into account.Therefore,this thesis studies the multi-sensor control algorithm with limited sensor Fo V for the multitarget search and tracking problem based on the random finite set theory.The main research contents are as follows:1.Aiming at the problem of multi-target tracking with limited sensor Fo V,a multisensor control algorithm based on Probability Hypothesis Density(PHD)filtering is proposed,which can realize accurate tracking of the discovered targets in the Fo V when targets will leave the sensor Fo V at any time.Through simulation experiments,the algorithm has been verified to be effective and has better tracking accuracy than the conventional multi-sensor control algorithm without considering the influence of sensor Fo V.2.Aiming at the problem of multi-target search and tracking with limited sensor Fo V,a multi-sensor control algorithm based on Poisson Multi-Bernoulli Mixture(PMBM)filtering is proposed.A joint objective function of the search task and the tracking task is constructed for control,which can not only ensure the tracking of the detected targets within the sensor Fo V,but also actively search for unknown targets outside the Fo V and track them.Through simulation experiments,the algorithm has been verified to be effective,and its performance is superior to the multi-sensor control algorithm for tracking task only and the random control strategy.3.Aiming at the problem of multi-target search and tracking with limited sensor Fo V,in order to further improve the target tracking accuracy and computational efficiency of multi-sensor control,a two-stage multi-sensor control algorithm is proposed based on the previous work,which firstly assigns the search or tracking task to each sensor dynamically,and then performs decision optimization on the sensors of the corresponding task.Through simulation experiments,the algorithm has been verified to be effective and has better tracking accuracy and lower computational complexity than the multi-sensor control algorithm based on the search and tracking joint objective function.