The result of radar signal sorting directly affects the performance of electronic reconnaissance equipment. Sorting method based on intra-pulse features has become a research focus in recent years. However, as the number of extracted features increases, the dimension of the feature vector becomes higher and higher. And too many dimensional feature vectors would make the complexity of the sorting algorithm increase geometrically. In this way, feature selection becomes more and more necessary. Combining the latest research on fuzzy rough sets, this paper proposes two feature selection methods, namely two-steps attribute reduction based on fuzzy dependency (TARFD) algorithm and fuzzy rough artificial bee colony (FRABC) algorithm. The TARFD method uses the candidate attribute set as starting point, according to the definition of the redundant attribute set. Then the less important attributes are successively eliminated. The FRABC method starts from the dependence degree of fuzzy rough set, and constructs a fitness function that reflects the importance of the attribute subset and the reduction rate. Based on this function, the artificial bee colony algorithm is used to reduce the attributes of the dataset. Using the TARFD and FRABC algorithms, the extracted feature sets, including entropy feature set, Zernike moment feature set, pseudo Zernike feature set, gray level co-occurrence matrix (GLCM) feature set, and Hu-invariant moment feature set are processed, then an optimal feature subset was obtained and a sorting test was performed. The results show the effectiveness of the extracted intra-pulse features and the efficiency of the feature selection algorithm.
Abstract-Long data collecting time is one of the bottlenecks of the stepped-frequency continuous-wave ground penetrating radar (SFCW-GPR). We discuss the applicability of the Compressive Sensing (CS) method to three dimensional buried point-like targets imaging for SFCW-GPR. It is shown that the image of the sparse targets can be reconstructed by solving a constrained convex optimization problem based on l 1 -norm minimization with only a small number of data from randomly selected frequencies and antenna scan positions, which will reduce the data collecting time. Target localization ability, performance in noise, the effect of frequency bandwidth, and the effect of the wave travel velocity in the soil are demonstrated by simulated data. Numerical results show that the presented CS method can reconstruct the point-like targets in the right position even with 10% additive Gaussian white noise and some wave travel velocity estimation error.
Abstract-When particle swarm optimization (PSO) technique is used for the inverse scattering problems it will take unbearably long time for the final solution, especially when the PSO algorithm traps into the premature convergence. To overcome this problem a hybrid multi-phased particle swarm optimization algorithm (HMPPSO) is proposed. By adopting the small swarm size strategy and the idea of "sub swarms" working cooperatively and alternatively with "optimal swarm" into the MPPSO, the HMPPSO can converge quickly with much less fitness function evaluation times, thus will reduce the reconstruction time. After the HMPPSO is validated by the numerical simulations on benchmark functions, the wall parameters (permittivity, conductivity, and thickness) together with target shape parameters (approximated by the trigonometric serials) with 20 dB additive Gaussian white noise are successfully reconstructed by HMPPSO using multi-frequency, multiview/singleillumination scattering fields calculated by MOM.
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