A Novel Multicomponent PSO Algorithm Applied in FDE–AJTF Decomposition

Abstract

The echo of maneuvering targets can be expressed as a multicomponent polynomial phase signal (mc-PPS), which should be processed by time frequency analysis methods, while, as a modified maximum likelihood (ML) method, the frequency domain extraction-based adaptive joint time frequency (FDE–AJTF) decomposition method is an effective tool. However, the key procedure in the FDE–AJTF method is searching for the optimal parameters in the solution space, which is essentially a multidimensional optimization problem with different extremal solutions. To solve the problem, a novel multicomponent particle swarm optimization (mc-PSO) algorithm is presented and applied in the FDE–AJTF decomposition with the new characteristic that can extract several components simultaneously based on the feature of the standard PSO, in which the population is divided into three groups and the neighborhood of the best particle in the optimal group is set as the forbidden area for the suboptimal group, and then two different independent components can be obtained and extracted in one extraction. To analyze its performance, three simulation tests are carried out and compared with a standard PSO, genetic algorithm, and differential evolution algorithm. According to the tests, it is verified that the mc-PSO has the best performance in that the convergence, accuracy, and stability are improved, while its searching times and computation are reduced.

Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization

“…However, the algorithm and subsequent improvements are nonetheless prone to the problem of pseudo-minimums and cannot guarantee global convergence [23]. In theory, the components of the mc-PPS are uncorrelated with each other, which indicates that the extraction of the signal components will not affect other components [18]. Therefore, the improved co-evolutionary PSO algorithm is proposed, based on the division of multiple sub-groups in the original algorithm.…”

“…N pop is the total number of particles, which mainly depends on the vessel size and SAR image resolution, and typically has a value of 50~120. N p is the polynomial order of the input signal phase, and has a value of 3~4 [18]. (c) Divide P1 into three groups.…”

“…Searching for optimal parameter components with different extremal solutions in the solution space is a multidimensional optimization problem during AJTF processing. Therefore, optimization algorithms, such as genetic algorithms (GA) and particle swarm optimization (PSO) algorithms, are used to reduce the computing complexity of searching, and simulation results confirm the efficiency of these approaches during processing [18,19]. The frequency-domain extraction-based AJTF decomposition method has been proposed to deal with the high-resolution space-borne SAR.…”