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In this paper, the chaotic characteristics of air traffic flow are studied, ADS-B data easily available to ground aviation users are selected as the basic data of traffic flow, and a high-dimensional prediction model of air traffic flow time series based on the non-iterative PSR-ELM algorithm is established. The prediction results of the proposed algorithm are then compared with those of the SVR algorithm, which requires iteration. Moreover, airspace operation data before and after the outbreak of the COVID-19 epidemic are selected as the experimental scene, and the prediction effects of time series with different degrees of chaos are comparatively analyzed. The experimental results reveal that the PSR-ELM algorithm achieves fast and accurate results, and, when the traffic flow state is sparse, the degree of chaos is reduced and the prediction effect is improved. The findings of this research provide a reference for air traffic flow theory.

Fractal theory is a new scientific method and theory that can describe the complexity and irregularity of nature. Aiming at the problem that the frequency modulation slope obtained by the traditional linear frequency modulation (LFM) signal parameter estimation algorithm has high complexity with poor real-time performance and small SNR adaptation range, the LFM signal frequency modulation slope estimation method based on the fractal-box dimension is proposed. The proposed method is utilized to evaluate the frequency modulation slope of the LFM signal, and the affect of signal amplitude and phase on the fractal-box dimension of the signal is taken into account. The estimation error at different SNRs is analyzed, and the relationship graph of the pulse width, FM bandwidth and fractal-box dimension is drawn. The simulation results demonstrate that the proposed method can accurately estimate the parameters of LFM signals under varying SNR environment. Compared with the traditional linear frequency modulation (LFM) signal parameter estimation algorithms, the anti-noise performance of the proposed algorithm is stronger, and the proposed algorithm is relatively simple and has good application value.