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

Abstract

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