Abstract
To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the characteristics of the high-order multi-component polynomial phase signal, this algorithm provides parallel processing and co-evolution methods by setting the different permissions of the sub-population and sharing its search information. As a result, the multiple components can be extracted simultaneously. Experiments were conducted using the simulation data and Gaofen-3 (GF-3) SAR data. Results showed the processing speed increased by more than 40% and the global convergence was significantly improved. The imaging results verify the efficiency and robustness of this co-evolutionary PSO.
Highlights
Synthetic aperture radar (SAR) has the distinct ability to be able to observe vessels at all times, and is an important method in the detection and monitoring of marine moving targets [1,2,3]
Marine application research of SAR has been carried out around the world, and ship detection systems based on space-borne SAR have been developed and used in practical applications, e.g., the ocean monitoring workstation (OMW) system of Canada, the automated maritime surveillance tool (MaST) system of England, the Kongsberg satellite services (KSAT) system of Norway, the collect localization satellite (CLS) system of France, and the Ship Surveillance system of China
According to the relevant statistics, the number of defocused vessel images affected by complex motion accounts for 15–20% of GF-3 high-resolution ocean data
Summary
Synthetic aperture radar (SAR) has the distinct ability to be able to observe vessels at all times, and is an important method in the detection and monitoring of marine moving targets [1,2,3]. Martorella [12] applied ISAR processing to the Cosmo-SkyMed SAR system and refocused moving targets These methods are suitable for vessels with relatively stable motion or medium-resolution SAR. For high-resolution SAR, the signal of a moving vessel can be represented by a high-order multicomponent polynomial phase signal (mc-PPS), which includes complex envelope migration and Doppler wrapping In these circumstances, the traditional time-frequency analysis methods, such as short-time Fourier transform, Wigner Ville distribution, and polynomial phase transformation [15,16], are seriously affected by cross-terms and cannot adapt to the practical applications of the mc-PPS. For an effective extraction of signal components, the adaptive joint time-frequency (AJTF) method, as an improved maximum likelihood method, is proposed to represent the mc-PPS in ISAR imaging, and offers better results without being affected by cross-terms. These results verify the robustness and efficiency of the presented algorithm, under high sea conditions
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