Three-dimensional Underwater Dynamic Target Tracking Based on Adaptive Interactive Multi-model Algorithm

Abstract

Dynamic target tracking is a crucial technique for autonomous underwater vehicles (AUV), enabling key operations such as target detection and reconnaissance. Standard approaches for tracking maneuvering targets often involve the interacting multiple model (IMM) algorithm which integrates the constant velocity (CV) model and the coordinated turn (CT) model. However, these models traditionally utilize fixed transition probabilities and turn rates based on prior information, potentially leading to imprecise state estimations. In response to this, the paper introduces an adaptive parallel IMM (APIMM) based on current adaptive IMM algorithms. This method adaptively adjusts transition probabilities and pairs with the unscented Kalman filter (UKF) algorithm for state prediction of maneuvering targets in a 3D underwater environment. The enhanced algorithm chooses from a model set that encompasses the CV model, the 3D fixed center constant speed and turning rate model (CSCTR) with an adaptive turn rate, and the current statistical (CS) model. Simulation outcomes have demonstrated that this algorithm utilizes posterior information more effectively, possesses an accelerated model switching speed, and improves the prediction accuracy of the underwater dynamic target's state in three-dimensional space by approximately 15%.

Keywords

• underwater target tracking
• unscented kalman filter(ukf)
• interacting multiple model(imm)
• state prediction
• transition probability matrix