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.
Highlights
Synthetic aperture radar (SAR) and inverse SAR (ISAR), which have all-time and all-weather active imaging abilities, play important roles in the civil and military fields, and their echo signals’processing has always been a research focus and hotspot
Similar to the other maximum likelihood (ML) methods, the key procedure in the FDE–adaptive joint time frequency (AJTF) method is searching for the optimal parameters in the solution space, which is essentially a multidimensional optimization problem with different extremal solutions [17]
particle swarm optimization (PSO) is proposed 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 two different independent components can be obtained and extracted in one extraction
Summary
Synthetic aperture radar (SAR) and inverse SAR (ISAR), which have all-time and all-weather active imaging abilities, play important roles in the civil and military fields, and their echo signals’. The important feature of PSO is that every particle in the swarm has an overall moving tendency toward its local historical best position and the global historical best position The feature makes it efficient and fast; when applied in the mc-PPS TF decomposition, it brings two influences: on one hand, the algorithm falls into the local optimal solution and enters the premature stagnation state, which reduces its global convergence ability; on the other hand, the different extremal solutions may be true components, and it makes the simultaneous extraction of several components possible, which can increase the decomposing efficiency. According to the test results, the mc-PSO has the best performance among the four optimal algorithms
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