Novel Global Network Signal Station Sorting Algorithm Based on Hop Describe Word (HDW) and Clustering-Assisted Temporal Sorting

To address the sorting challenge of multiple stations and multiple networking modes (networks with inconsistent features and networks with similar features but asynchrony) in complex electromagnetic environments, this paper proposes a full-network station sorting algorithm that integrates Hop Describe Word (HDW), hierarchical clustering, and temporal sorting. First, from the perspectives of hardware differences, channel interference, and networking strategies, it is demonstrated that “there theoretically exist no multiple frequency-hopping networks that are completely synchronized and have consistent features”, thereby defining the sorting boundary of the algorithm. Second, “preliminary clustering sorting” is used to separate networks with significant differences in HDW static features, and then the “temporal sorting algorithm” designed in this paper is applied to overcome the sorting bottleneck of networks with similar features but asynchrony. Finally, based on the feature rules of the sorted networks, ARIMA temporal prediction and K-nearest neighbor (KNN) feature completion are adopted to achieve accurate recovery of missing signals. Experimental results show that in scenarios with a signal-to-noise ratio (SNR) of −8 dB to 5 dB and a signal loss rate of 0% to 15%, the proposed algorithm achieves an average sorting accuracy of 96.7%, a sorting completeness of 94.3%, and a robustness fluctuation range of only ±4.2% for 10 mixed networks. It significantly outperforms the traditional K-means algorithm and the single HDW clustering algorithm, and can effectively meet the needs of military and civilian spectrum reconnaissance and station sorting.