Abstract：Target state estimation is a key problem of collaborative detection. Traditional model-based state estimation algorithms perform poorly under partially known state-space models. In contrast, existing neural network-based state estimation algorithms have low interpretability, making it difficult for them to effectively apply in real-world scenarios (e.g., collaborative detection). This paper proposed a highly interpretable neural network-based state estimation and fusion framework to address the above problems. First, a Kalman filter neural network model was employed to obtain high interpretability by approximating the model-based state estimation algorithm. Second, a learnable weighted robust fusion framework was introduced to improve the fusion accuracy under partially known state space models. Experimental results show that the proposed method performs high target state estimation accuracy and robustness in simulation environments and real datasets, significantly outperforming traditional methods.
周添龙, 姚方競, 饶卫雄. 部分已知状态空间模型下的目标状态估计算法[J]. 空天防御, 2025, 8(3): 111-122.
ZHOU Tianlong, YAO Fangjing, RAO Weixiong. Target State Estimation Algorithms Under Partially Known State Space Models. Air & Space Defense, 2025, 8(3): 111-122.
