Underwater Target Tracking In Sonar Images Using GPF

Target tracking technology is widely used in surveillance, navigation, obstacle avoidance and so on, which needs to make sure the targets' number, locations, speeds and identities. Especially the underwater target tracking technology is not as mature as air target tracking technology, research of which has very important significance.This paper is concerned with the study on underwater target tracking in sonar images using Gaussian particle filter (GPF), and it is divided into two parts: the one is single target tracking, and the other is multi-target tracking. And the single target tracking is the basis of multi-target tracking. The most important step of single target tracking is to select an appropriate target dynamics model, a proper measurement model and a good filter. As the forward-looking sonar images are with less detail information of the targets like contour and color compared with the optical image, the first-order autoregressive process equation is selected as state transition model and the weight of a particle is evaluated according to matching its two characteristics of moment invariant and area with the corresponding characteristics of the target. simulation results of one-dimensional and two-dimensional non-linear non-Gaussian tracking model show that the Gaussian particle filter can not only solve the linear Gaussian problem, but also can be applied to non-linear non-Gaussian problems , comparing with the well-known Kalman filter who is only suitable for the linear cases; and as a improvement of particle filter it eliminates particle impoverishment without resampling, therefore it is easier to practice and more suitable for solving the practical engineering problems. Tank experiments are carried out .Results demonstrate the method's advantages which is showed in the simulation. Based on the single-target tracking method, firstly GPF is joined with data association of nearest neighbor data association(NNDA), which is the easiest multi-target tracking method named GPF-NNDA, but it can't fulfill the task of tracking multiple closed targets. Then a new multi-target tracking method is proposed in this paper which is based on the Gaussian particle filter and joint probabilistic data association named GPF-JPDA. Simulation and tank experiments of multi-target tracking are performed using GPF-JPDA and some other data association to test the performance of the presented method in this paper which implies that GPF-JPDA has the advantages of good robustness, high accuracy and real-time characteristic, and it is efficient in underwater multi-target tracking based on sonar images.