Abstract
The detection performance of high-frequency surface-wave radar (HFSWR) is closely related to the suppression effect of sea clutter. To effectively suppress sea clutter, a sea clutter suppression method based on radial basis function neural network (RBFNN) optimized by improved gray wolf optimization (IGWO) algorithm is proposed. Firstly, according to shortcomings of the standard gray wolf optimization (GWO) algorithm, such as slow convergence speed and easily getting into local optimum, an adaptive division of labor search strategy is proposed, which makes the population have abilities of both large-scale search and local exploration in the entire optimization process. Then, the IGWO algorithm is used to optimize RBFNN, finally, establishing a sea clutter prediction model (IGWO-RBFNN) and realizing the prediction and suppression of sea clutter. Experiments show that the IGWO algorithm has significantly improved convergence speed and optimization accuracy. Compared with the particle swarm algorithm with linear decreasing weight strategy (LDWPSO) and the GWO algorithm, the RBFNN prediction model optimized by the IGWO algorithm has higher prediction accuracy and has a better suppression effect on sea clutter of HFSWR.
Highlights
High-frequency surface-wave radar (HFSWR) transmits highfrequency (3–30 MHz) electromagnetic waves with vertical polarization antenna, and short waves propagate along the surface of the conductive ocean without being affected by the curvature of the Earth
The suppression methods of sea clutter mainly include the cyclic iterative cancellation method [13,14,15], subspace estimation method [16,17,18], and neural network method [19,20,21]. e cyclic iterative cancellation method constructs sinusoidal signals by estimating parameters and subtracts the sinusoidal signals from the echo to realize the sea clutter suppression. e iterative steps of this method are set through experience, which is likely to cause some problems of incomplete suppression of sea clutter and false cancellation of target signals. e subspace estimation method realizes the suppression of sea clutter through its clustering characteristics in the subspace
According to the sea clutter suppression in target detection of HFSWR, this paper proposes a sea clutter suppression method based on the radial basis function neural network (RBFNN) optimized by improved gray wolf optimization (GWO) algorithm
Summary
High-frequency surface-wave radar (HFSWR) transmits highfrequency (3–30 MHz) electromagnetic waves with vertical polarization antenna, and short waves propagate along the surface of the conductive ocean without being affected by the curvature of the Earth It can achieve all-weather and overhorizon detection of maritime targets, such as vessels and lowflying aircraft [1,2,3]. Is paper presents a sea clutter suppression method based on an improved GWO (IGWO) algorithm optimizing RBFNN. Compared with the RBFNN prediction model optimized by the LDWPSO and GWO algorithms, the model proposed in this paper can save calculation time or energy, and it has a better suppression effect on sea clutter
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