A measure of some time-frequency distributions concentration
Signal Processing - Special section on digital signal processing for multimedia communications and services
Autofocusing of SAR images based on parameters estimated from the PHAF
Signal Processing
Genetic algorithms based robust frequency estimation of sinusoidal signals with stationary errors
Engineering Applications of Artificial Intelligence
Robust L-estimation based forms of signal transforms and time-frequency representations
IEEE Transactions on Signal Processing
Are genetic algorithms useful for the parameter estimation of FM signals?
Digital Signal Processing
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Recently, an L-statistics based method for the micro-Doppler effects removal has been proposed by the authors. Order statistics is performed on the spectrogram, while the rigid body signal synthesis is done by using the remaining STFT samples, after micro-Doppler removal. By the proposed method, the Fourier transform is recovered with a concentration close to the original one. However, during the procedure of the micro-Doppler removal, the STFT samples that correspond to the rigid body are retracted, as well. Consequently, in the reconstructed Fourier transform of the rigid body, we get one very highly concentrated pulse, as in the original Fourier transform, and a number of low-concentrated components, being spread around the peak. These low concentrated components are summed up by different random phases. In this paper, we propose a genetic algorithm for the estimation of the removed STFT samples corresponding to the rigid body. Each individual in the genetic algorithm contains possible estimation of the phases of the missing STFT samples, whereas fitness function forces individuals (combination of phases) for which a minimal energy of the side lobs is obtained. The individual with the highest fitness is considered as the final phases' estimation of the missing STFT values, and then used for the reconstruction of the original Fourier transform. The amplitude of a STFT sample is estimated as median of the amplitudes of the remaining samples at the same frequency. Performance of the proposed genetic algorithm is illustrated by examples.