Abstract
In addition to target echoes, high frequency surface wave radar (HFSWR) receives sea and ionospheric clutter. Among these clutters, the ionospheric clutter is dominant and significantly affects the detection performance of HFSWR, particularly when the targets are located 100 kilometers away from the radar, rendering it an unsolved problem for HFSWR. Existing studies concerning HFSWR ionospheric clutter lack empirical research on the nonlinear dynamical characteristics of the ionosphere of HFSWR as existing ionospheric suppression methods are still insufficient to adapt the project application. Therefore, the present study utilized the threshold segmentation method to eliminate the sea clutter in HFSWR Range-Doppler spectrum and extracted ionospheric signals from this spectrum by edge feature extraction. Subsequently, the chaotic invariants, such as correlation dimension and the largest Lyapunov exponent of HFSWR ionospheric clutter, were calculated by phase space reconstruction, whilst the chaotic dynamical characteristics of HFSWR ionospheric clutter were determined by the 0–1 test for chaos and other algorithms. Furthermore, the present study demonstrated, for the first time, the chaotic dynamics of the ionospheric clutter of HFSWR with a low-dimensional attractor by processing and analyzing the experimental data from the Weihai High Frequency Radar Station. The conclusion redefines HFSWR ionospheric clutter based on chaotic dynamics rather than regarding it as a stochastic process, which is conducive to efforts to explore the formation mechanism of ionospheric clutter in essence, which can ultimately improve the detection capability of HFSWR, particularly for long-distance targets.
Highlights
INTRODUCTIONThe high frequency surface wave radar (HFSWR) has been widely used for the detection of targets above the horizon (including offshore vessels and low-flying aircrafts) and sea state remote sensing (ocean currents, wind direction and wave height) [1]
The high frequency surface wave radar (HFSWR) has been widely used for the detection of targets above the horizon and sea state remote sensing [1]
TO CHAOTIC IDENTIFICATION METHODS According to the phase-space reconstruction theorem, the phase diagram method, correlation dimension method and the largest Lyapunov exponent can be criteria for assessing the chaotic dynamics of ionospheric clutter in experimental time-series data
Summary
TO CHAOTIC IDENTIFICATION METHODS According to the phase-space reconstruction theorem, the phase diagram method, correlation dimension method and the largest Lyapunov exponent can be criteria for assessing the chaotic dynamics of ionospheric clutter in experimental time-series data. The saturation of the correlation dimension can be used as the criterion for judging the chaotic dynamical process of ionospheric clutter time-series data [23]. The power spectrum method can identify and distinguish the chaotic dynamical process, periodic motions and random noises from the unique characteristics of time series in the time-frequency domain. E. 0-1 TEST FOR CHAOS The 0-1 test for chaos for deterministic dynamical systems is designed to distinguish between regular, periodic or quasiperiodic, and chaotic dynamics It functions directly with the time series and does not require any phase-space reconstruction which needs to estimate the time delay and the embedding dimension. If Kc ≈ 1, the ionospheric clutter is a chaotic process
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