Research On Underwater Multi-clutter Data Association Based On Fuzzy Clustering

In this paper, a multi-target tracking association algorithm based on fuzzy clustering is proposed to cope with the multi-clutter phenomenon and large measurement error in underwater environment. The algorithm obtains the membership degree of each measurement to the clustering center point by using the Fuzzy Clustering (FCM) algorithm, and then weights the associated measurement values as a weight coefficient. Finally, the Kalman filter algorithm is used to obtain the estimated value of the state, so that multiple targets can be tracked. Simulation results show that the traditional algorithm is easy to generate false alarms or even filter divergence in the multi-clutter environment. However, the algorithm proposed in this paper takes all the measured values falling within the tracking gate into account, which greatly improves the tracking accuracy and stability. So that our method can be applied to actual engineering projects.