IEE Proceedings F (Radar and Signal Processing)

IEE Proceedings F (Radar and Signal Processing)

Online ISSN 2053-9045
Print ISSN 0956-375X

Published from 1989-1993, IEE Proceedings F contained significant and original contributions on radar and signal processing.

This journal was previously known as IEE Proceedings F (Communications, Radar and Signal Processing) 1980-1988. ISSN 0143-7070. more..

This journal was previously known as IEE Proceedings - Vision, Image and Signal Processing 1994-2006. ISSN 1350-245X. more..

This journal was previously known as IEE Proceedings - Radar, Sonar and Navigation 1994-2006. ISSN 1350-2395. more..

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Editorial. Applications of higher order statistics
- Author(s): Jerry M. Mendel and Asoke K. Nandi
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- p. 341 –342 (2)
Two-channel tests for common non-gaussian signal detection
- Author(s): J.K. Tugnait
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- p. 343 –349 (7)
  The problem of detecting an unknown, random, stationary, non-Gaussian signal that is common to two spatially separated sensors, is considered. The signal is assumed to have a nonvanishing bispectrum. The measurement noise sequences at the two sensors are either mutually independent with arbitrary cumulant spectra, or dependent with vanishing bispectra. Two statistical tests are presented for signal detection when the noise statistics are unknown. The performance of the tests is illustrated by computer simulation examples.
Single sensor detection and classification of multiple sources by higher-order spectra
- Author(s): M.C. Doǧan and J.M. Mendel
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- p. 350 –355 (6)
  The authors consider the detection and classification of multiple non-Gaussian linear sources by superposition of their waveforms available from a single sensor whose measurements are possibly corrupted by additive Gaussian noise. It is shown that by using multiple frequency lags of the trispectrum of single sensor measurements, it is possible to form a trispectral matrix C that possesses the same structure as the array covariance matrix of narrowband multisensor measurements. Consequently, techniques that are applicable to narrowband array processing can be adapted for the analysis of single sensor data; the rank of C reveals the number of sources, and a multiple signal characterisation (MUSIC)-like method can be used for source classification using a directory of candidate source spectra. Simulations are included to illustrate the proposed methods.
Blind deconvolution of coloured signals based on higher-order cepstra and data fusion
- Author(s): A.P. Petropulu and C.L. Nikias
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- p. 356 –361 (6)
  A new nonparametric blind deconvolution algorithm for coloured non-Gaussian signals is presented. The goal of blind deconvolution is the reconstruction of the input of an unknown linear time-invariant (LTI) mixed-phase system based only on the system output. The existing deconvolution schemes that require the least amount of knowledge about the input signal and the LTI system were developed for white input signals, or rely on parametric modelling of both the system and the input. To develop nonparametric algorithms for the deconvolution of coloured processes of unknown statistics, we are forced to consider a two-channel approach. The proposed algorithm utilises the data collected by two different receivers, each being the output of a different system due to the same input. The two systems are then reconstructed combining higherorder statistics of the measured signals and the theory of signal reconstruction from the higherorder spectral phase only.
Blind beamforming for non-gaussian signals
- Author(s): J.F. Cardoso and A. Souloumiac
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- p. 362 –370 (9)
  The paper considers an application of blind identification to beamforming. The key point is to use estimates of directional vectors rather than resort to their hypothesised value. By using estimates of the directional vectors obtained via blind identification, i.e. without knowing the array manifold, beamforming is made robust with respect to array deformations, distortion of the wave front, pointing errors etc., so that neither array calibration nor physical modelling is necessary. Rather suprisingly, ‘blind beamformers’ may outperform ‘informed beamformers’ in a plausible range of parameters, even when the array is perfectly known to the informed beamformer. The key assumption on which blind identification relies is the statistical independence of the sources, which is exploited using fourth-order cumulants. A computationally efficient technique is presented for the blind estimation of directional vectors, based on joint diagonalisation of fourth-order cumulant matrices; its implementation is described, and its performance is investigated by numerical experiments.

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IEE Proceedings F (Radar and Signal Processing)

Volumes & issues:

Volume 140 (1993)

Volume 139 (1992)

Volume 138 (1991)

Volume 137 (1990)

Volume 136 (1989)

Latest content

Editorial. Applications of higher order statistics

Two-channel tests for common non-gaussian signal detection

Single sensor detection and classification of multiple sources by higher-order spectra

Blind deconvolution of coloured signals based on higher-order cepstra and data fusion

Blind beamforming for non-gaussian signals

Most viewed content for this Journal

Most cited content for this Journal