GP-Based Track Segment Association in Limited Field of View

In practice, target tracking often suffers from track breakage due to sensor limitation and the target’s agile movements. To enhance track continuity, track segment association technology is commonly employed. However, existing approaches heavily rely on dynamic model, which require significant prior knowledge that can be difficult to obtain in practical scenarios. In contrast, this study proposes a novel track segment association method based on Gaussian Process (GP). As a data-driven approach, GP can learn from the data, reducing the dependence on prior knowledge. Simulation results demonstrate that employing GP for track segment association leads to improved accuracy and correctness compared with model-based methods.