Abstract
The spaceborne synthetic aperture radar (SAR) system working at P-band, is vulnerable to the ionospheric effect. The ionospheric scintillation will introduce random phase fluctuations into the SAR signal and deteriorate the imaging performance. In this paper, a minimum-entropy autofocusing method based on the intelligent optimization strategy is proposed to compensate for the scintillation phase error in spaceborne P-band SAR images. A refined particle swarm optimization (Re-PSO) is proposed to provide an intelligent strategy in SAR autofocusing. Compared with the traditional minimum-entropy autofocusing methods, the proposed Re-PSO algorithm is a heuristic method which has extremely strong exploring abilities to the global optimum. The genetic multi-crossover operator and the gradient accelerator are utilized to improve the convergence property of the basic PSO. Furthermore, since the isolate strong scatterers are not required in minimum-entropy SAR autofocusing, the proposed method has strong robustness. The simulations on point and area targets validate the effectiveness and better performance of the proposed method.
Highlights
The P-band synthetic aperture radar (SAR) maintains outstanding capabilities in penetration which can be widely applied in biomass measurement, agriculture observation and military surveillance [1]–[3]
The flowchart of the proposed refined particle swarm optimization (Re-particle swarm optimization (PSO)) algorithm for scintillation mitigation is shown in Figure 2 and the detailed process is described in pseudocode as follow: IV
The P-band SAR system will be seriously deteriorated by ionospheric scintillation when the radio waves penetrate the ionospheric irregularities
Summary
The P-band synthetic aperture radar (SAR) maintains outstanding capabilities in penetration which can be widely applied in biomass measurement, agriculture observation and military surveillance [1]–[3]. L. Yu et al.: Minimum-Entropy Autofocusing Based on Re-PSO for Ionospheric Scintillation Mitigation in P-Band SAR Imaging algorithm is not effective anymore when the signal-to-clutter ratio (SCR) is lower than 16 dB [18]. Wang’s work [25], an adaptive-order polynomial phase model is introduced to minimize the SAR image entropy through the iteration, the parameter optimization strategy is not efficient in [25]. The position of particles is defined as the parameters of the polynomial phase model and the fitness is defined as the corresponding image entropy. The aim of our work is to minimize the image entropy by searching and compensating the scintillation phase error. A linear decreasing inertia weight will lead to overall lowest errors [30]
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