The Wigner-Ville Distribution (WVD) has been gaining importance in the recent years for the analysis of time varying signals (B. Boashash, in: Simon Haykin (Ed.), Advances in Spectrum Analysis and Array Processing, Prentice-Hall, Englewood Cliffs, NJ, 1991). In this paper, an adaptive moving target detector (AMTD) based on WVD as a Doppler filter bank has been introduced. The proposed AMTD processor has been simulated in the presence of Gaussian and non-Gaussian clutter environments. The preliminary investigations reveal that the proposed processor performs better in terms of probability of detection than the existing moving target detector (MTD) processors. © 2001 Published by Elsevier Science B.V.

K. M.Muraleedhara Prabhu

Probability

Radar clutter

Radar target recognition

Signal detection

ADAPTIVE MOVING TARGET DETECTOR

Radar signal processing

WIGNER-VILLE DISTRIBUTION

Signal filtering and prediction

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Detection performance of an adaptive MTD with WVD as a Doppler filter bank

Signal Processing

Of late, Wigner-Ville distribution (WVD)-based time-frequency representation has been shown to become a viable tool in the study of time-varying signals. In this paper, we discuss the implementation details of a WVD-based moving target detector (MTD) on a TMS320C30 floating-point digital signal processor. In this approach, WVD has been employed as a functional block instead of the conventional fast Fourier transform (FFT) processor [1]. A comparison of the implementation and the computational details of MTD based on FFT and WVD has been included. Finally the probability of detection (Pd) and the probability of false alarm (Pfa) curves, in the presence of log-normal clutter, have been presented to support the superiority of the proposed MTD-WVD over MTD-I. © 1998 Elsevier Science B.V.

Microprocessors and Microsystems

Implementation of MTD-WVD on a TMS320C30 DSP processor

The authors discuss the application of Wigner-Ville distribution (WVD) for moving-target detection (MTD) in a typical ground-based radar. The proposed technique enhances the target-detection capability using the fact that the sidelobe levels in the Wigner spectrum are very small in magnitude. This in turn facilitates the realisation of the Doppler-filter bank by WVD without resorting to additional weighting to reduce the sidelobes, unlike in the current MTD-I system (FFT-based). The MTD scheme proposed exhibits good performance capabilities, by providing higher detection probabilities, lower false-alarm rates and an additional 10 dB gain in improvement factor, for ground plus weather clutter, over MTD-I. This novel approach to MTD serves as a simple and superior alternative to the MTD-II and MTD-III schemes, which have similar performance capabilities over MTD-I. Computer-simulation results are presented to support the superiority of the proposed approach. © IEE, 1997.

IEE Proceedings: Radar, Sonar and Navigation

Simulation studies of moving-target detection: A new approach with Wigner-Ville distribution

Erratum to “Implementation of MTD–WVD on a TMS320C30 DSP processor”

Proceedings of the IEEE

Signal analysis by means of time-frequency (Wigner-type) distributions-applications to sonar and radar echoes

Advanced Signal Processing Algorithms, Architectures, and Implementations IV

<title>Recent advances in nonstationary signal analysis: time-varying higher-order spectra and multilinear time-frequency signal analysis</title>

Journal	Signal Processing
ISSN	01651684
Open Access	No