Adaptive combined bispectrum-filtering signal processing in radar systems with low SNR Lukin V., Totsky A., Fevralev D., Roenko A.
Astola J., Egiazarian K.
Department of Transmitters, Receivers, and Signal Processing, National Aerospace University, Chkalova Str. 17, 61070, Kharkov, Ukraine e-mail:
[email protected];
[email protected]
Institute of Signal Processing, Tampere University of Technology P. O. Box 553, FIN-33101, Tampere, Finland e-mail:
[email protected];
[email protected]
Abstract—The application of adaptive techniques for obtaining bispectrum estimates in additive Gaussian noise and random shifts of received signals is considered. An approach using joint adaptive robust forming of bispectrum estimates and processing of complex-valued signal Fourier spectrum estimates by discrete cosine transform-based filtering with local variance estimation within each block is proposed. The advantages of the proposed approach in comparison to the conventional signal waveform recovery from bispectrum are illustrated by computer simulations.
INTRODUCTION Bispectrum-based methods designed for signature reconstruction and target recognition in interference background are of large interest in radar applications nowadays [1–4]. The main motivation for employing bispectrum analysis to radar return signal processing in comparison to the conventional power spectral analysis is the possibility of preserving signal phase Fourier spectrum, good suppression of additive Gaussian noise and invariance property to temporal return signal shifts caused by changing a target coordinates and aspect angle. It should be especially stressed that in practice information about interference statistics in radar scene is not available or is available partially and with a limited accuracy. Under these conditions, the use of traditional matched filtering becomes ineffective. On the contrary, bispectrumbased signal processing techniques are able to operate sufficiently well in a priori unknown noise characteristics and for different kinds of return signal waveform (target range profile). These techniques allow accomplishing quasicoherent accumulation of realizations of received data with rather efficient suppression of additive Gaussian noise. However, in cases of low input SNRs, noise suppression and quality of radar signatures reconstructed by widely used recursive bispectrum-based algorithm [5] can be of level unsatisfactory for reliable target recognition. One possible approach for resolving this problem is the use of the so
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called combined bispectrum-filtering techniques earlier proposed in our papers [4, 6, 7, 8]. By generalizing the results obtained in these papers, one can conclude that the improvement of a signal waveform estimate recovered from bispectrum estimate (BE) is possible in, at least, two ways: 1) by improving the accuracy of BE; 2) by improving the accuracy of signal Fourier spectrum estimate recovered from BE. The first approach is based on the following peculiarities. The probability density function (PDF) of real (Re) and imaginary (Im) parts of BE are close to Gaussian [7] for all frequencies in bispectrum domain for large input SNR values. Since sample mean is the optimal (efficient) estimate for Gaussian PDF data, the optimal procedure for forming BE for a set of observed realizations is their averaging that is used in conventional approach [5]. However, in the case of input SNR
