Open Access

A New Method for Estimating the Number of Harmonic Components in Noise with Application in High Resolution Radar

EURASIP Journal on Advances in Signal Processing20042004:615890

Received: 18 February 2003

Published: 8 July 2004


In order to operate properly, the superresolution methods based on orthogonal subspace decomposition, such as multiple signal classification (MUSIC) or estimation of signal parameters by rotational invariance techniques (ESPRIT), need accurate estimation of the signal subspace dimension, that is, of the number of harmonic components that are superimposed and corrupted by noise. This estimation is particularly difficult when the S/N ratio is low and the statistical properties of the noise are unknown. Moreover, in some applications such as radar imagery, it is very important to avoid underestimation of the number of harmonic components which are associated to the target scattering centers. In this paper, we propose an effective method for the estimation of the signal subspace dimension which is able to operate against colored noise with performances superior to those exhibited by the classical information theoretic criteria of Akaike and Rissanen. The capabilities of the new method are demonstrated through computer simulations and it is proved that compared to three other methods it carries out the best trade-off from four points of view, S/N ratio in white noise, frequency band of colored noise, dynamic range of the harmonic component amplitudes, and computing time.

Keywords and phrases

superresolution methodssubspace projectiondiscriminant functionhigh-resolution radar

Authors’ Affiliations

Laboratoire E3I2, Ecole Nationale Supérieure des Ingénieurs des Etudes et Techniques d'Armement (ENSIETA)


© Radoi and Quinquis 2004