A Fast Time-Adaptive Data Association Method for Multi-Target Tracking with Discontinuous Sparse LEO Satellite Observations

In low-Earth-orbit (LEO) satellite constellation remote sensing for surface maritime target detection, the inherent characteristics of discontinuous detection epochs, non-uniform temporal intervals, and clutter contamination invariably cause conventional data association algorithms to suffer from validation gate degradation, covariance divergence, and combinatorial explosion. To circumvent these limitations, this paper proposes a multi-target, time-adaptive fast association method tailored for discontinuous sparse observations. Within the joint probabilistic data association (JPDA) framework, the proposed method analyzes the mismatch between the Kalman filter prediction covariance and the actual error under discontinuous observations. A time-interval adaptive gating mechanism maintains the gate detection probability across arbitrary revisit intervals. Secondly, to resolve the massive connected cluster problem triggered by the densification of the validation matrix, a progressive clustering strategy inspired by simulated annealing is designed, which recursively decomposes the global, exponentially scaling association graph into independent subgraphs of manageable sizes. Building upon this, a depth-first search (DFS) heap pruning technique is integrated with the Hungarian hard assignment algorithm as a safety-degradation mechanism to safeguard numerical robustness in extreme scenarios. Comparative experiments demonstrate that the proposed method significantly enhances both tracking accuracy and track completeness across various constellation coverage characteristics and maritime clutter intensities. Furthermore, its execution efficiency satisfies real-time simulation requirements, effectively supporting engineering application for surface maritime target detection.