Abstract:

In response to the challenges of dependency on prior knowledge and difficulty in parameter adaptation and tuning in the radar signal sorting process, a parameter self-adaptive signal sorting method based on self-constrained search density clustering is proposed. This method leverages the reachable distance sequence produced by the ordering points to identify the clustering structure (OPTICS) algorithm and introduces a heuristic self-constraining search mechanism. This mechanism is capable of autonomously analyzing the intrinsic structure of a dataset and adaptively partitioning clusters based on their data characteristics. With the capability to automatically adjust hyperparameters, the algorithm efficiently processes pulse description word (PDW) data with diverse parameter distributions. Simulation experiments demonstrate that, without the dependency on prior knowledge, the proposed algorithm outperforms traditional methods in terms of accuracy and anti-interference capability in radar signal sorting, achieving an accuracy rate of over 98% in complex electromagnetic environments with interference pulse ratios not exceeding 60%.

Key words: signal sorting, density clustering, self-constrained search, parameter-adaptive