In automatic target recognition applications, an important task is to obtain denoised signal signatures of the object. In this paper, the reconstruction of 1-D deterministic signals, for example, range profiles, corrupted by random signal shift and additive white Gaussian noise using 2-D bispectrum is considered. Combined bispectrum-filtering techniques based on smoothing the noisy bispectrum estimates by 2-D linear and nonlinear filters are proposed. It is shown that bispectrum estimates obtained by the conventional direct bispectrum estimator are corrupted by fluctuation errors and are biased. The performance of the proposed bispectrum-based signal reconstruction methods is analysed using two conventional criteria -the reconstructed signal fluctuation variance and bias. The numerical simulation results show that 2-D filtering of real and imaginary components of noisy bispectrum estimates is most efficient in the sense of minimum MS errors.
ABSTRACT:The problem of reconstruction an unknown signal waveform from bispectrum is considered. A new approach based on utilization of continuous sine and cosine functions instead of conventional discontinuous bispectrum phase function in signal reconstruction recursive algorithm is proposed. The performance of the developed signal waveform reconstruction algorithm is analyzed by numerical simulation for realvalued deterministic discrete-time signals containing discontinuities of the first kind in the cases of different sampling rates. Simulation studies demonstrate that significant reconstructed signal quality enhancement occurs with the proposed simple and computationally efficient algorithm.
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