Space-based multi-target passive tracking is critical for space situational awareness, but faces severe challenges due to the limited field-of-view (FoV) and directional ambiguity of onboard sensors. These constraints often lead to target loss, poor observability, and decreased estimation accuracy. To address these issues, different fusion architectures have been explored. While centralized measurement-level fusion offers superior accuracy for estimating target states, distributed estimation-level fusion provides greater reliability for estimating the number of targets. To adaptively leverage these two complementary strengths, a dynamic hierarchical fusion method through real-time optimization of the fusion topology is proposed. Specifically, at each decision epoch, sensor nodes are dynamically partitioned into local fusion nodes (LFNs) and detection-only nodes (DONs). Each LFN receives measurements from selected DONs and executes an iterated-correction Gaussian-mixture probability hypothesis density filter. Subsequently, LFNs share and fuse their estimates using the intensity-dependent arithmetic average fusion. This dynamic process is achieved by applying a sensor management scheme based on partially observable Markov decision process (POMDP). To ensure accurate cardinality estimation, the reward function in POMDP utilizes the posterior expected number of targets. The resultant optimization is efficiently solved using a binary particle swarm optimization algorithm. Numerical and hardware-in-the-loop simulations demonstrate the effectiveness of the proposed method in balancing the accuracy of target number and state estimation.
Wang et al. (Tue,) studied this question.