基于单声矢量传声器虚拟扩展的多机动声目标跟踪算法

To solve the problem of poor performance and fewer trackable targets in the multi-targets tracking of acoustic vector sensor (AVS) in multi-target tracking, a multi-maneuvering acoustic targets tracking algorithm based on virtual extension of single AVS is proposed. First, the higher-order cumulants processing method is introduced to establish a higher-order likelihood function, which can not only improve the estimation accuracy by suppressing the Gaussian noise, but also increase the number of estimable targets by virtually extending the AVS. Then, under the marginalized δ -generalized label multi-bernoulli (M δ -GLMB) framework, a cumulants-based augmented motion model state M δ -GLMB (Cum-AMMS-GLMB) algorithm is proposed. The algorithm introduces multiple models, and uses the model index labels that distinguish different motion models as an augmented parameter for the target state, and obtains a better tracking performance than a single motion model through weighted mixing of the updated states of each model. In addition, in the sequential Monte Carlo (SMC) implementation of the algorithm, the detection probability function is fitted based on the normalized spatial spectrum obtained by higher-order cumulants preprocessing can suppress the diffusion of clutter to the available particles, and further enhance the particles in the high-likelihood region. Finally, the posterior cram é r-rao lower bound (PCRLB) for targets tracking of single AVS is derived, and the performance of measurement noise suppression and acoustic targets tracking is verified by simulation experiments.