Pulsar identification method based on adaptive grey wolf optimization algorithm in X-ray pulsar-based navigations

Fast and accurate identification of pulsar signals is important for X-ray pulsar-based navigation (XNAV). Traditional signal detection technology based on FFT search epoch folding (EF) requires a very long time to obtain an appropriate signal-to-noise ratio (SNR) gain, especially weak with low photon fluxes. This paper proposes adaptive stochastic resonance (SR) method the grey wolf optimizer (GWO) algorithm fast identification. The GWO can optimize SR parameters optimal output that matches input signal. Meanwhile, initial population generated by Chebyshev chaotic maps, which guarantees diversity enhances global capability GWO. To further improve performance SR, we add extra denoising process, including high-pass filter singular value decomposition (SVD) determines effective components through dispersion entropy (DE), suppress noise great extent. We use simulation data Rossi Timing Explorer (RXTE) observation verify proposed method. SNR greatly improved, identify pulsars in short time. results indicated has practical engineering. Further experiments reveal importance SVD, DE map algorithm.