MambaMTT: A deep learning method based on mamba structure for maneuvering target tracking

Abstract

The research area of maneuvering target-tracking in radar system has emerged as a critical and valuable research frontier. The diverse and unpredictable movements of maneuvering targets make it hard to estimate their state accurately. This challenge often makes previous methods unreliable in dealing with maneuvering targets, especially the highly maneuvering ones. Although Transformer-based models possess global modeling capabilities, they encounter computational challenges when applied to long trajectory sequences due to their inherent computational complexity. To address the problem of highly maneuvering targets tracking, this paper proposes a Mamba-based maneuvering target tracking algorithm, termed as MambaMTT. MambaMTT is specifically designed to model trajectory change patterns by focusing on local and global feature correlation information of the trajectory sequence, facilitating the effective processing of highly maneuvering targets. Meanwhile, we introduce a multi-scale feature fusion module to capture spatial and temporal correlations at different scales within the trajectory sequence. With this module, the MambaMTT network is able to capture local and global trajectory features more efficiently, improving the model’s adaptability and accuracy for complex maneuvering targets. Experimental results show that the proposed MambaMTT algorithm exhibits higher tracking efficiency and accuracy in various maneuvering target tracking, and it also has better generalization ability on data beyond the training range.