This paper considers interference cancellation in radar systems when the signal environment is non-stationary and focuses on the extended sample matrix inversion algorithm (ESMI) first proposed by Hay ward (1996). An explicit expression of the signal to noise plus interference ratio (SINR) obtained by this ESMI algorithm is given and analyzed. Compared to the SMI algorithm, it is shown that the performance improves for mainlobe jammers but degrades for sidelobe jammers. To overcome this drawback, an alternative constraint strategy is proposed which attains the good performances of the standard ESMI algorithm whatever the position of the jammers. Finally, the explicit expressions of these SINR are compared to Monte Carlo simulations w.r.t. implementation conditions

This paper considers spatio-temporal filtering in ground-based rotating radar systems. After the drawbacks of the standard spatio-temporal processing in this context are underlined, an hybrid spatio-temporal scheme is proposed to overcome them. Finally, this introduced processing is compared to standard ones through Monte Carlo simulations

An effective scheme for spatio-temporal processing with ground-based rotating radars consists in making adaptive spatial filtering, with frequent updates, followed by temporal processing. In presence of jamming and clutter, spatial processing first filters jammers, whereas temporal processing then filters clutter. However, starting by making a spatial adaptive processing requires to dispose of jamming + noise alone reference. But as clutter has not been filtered yet, it may be present in estimation data with stronger power than that of jammers. Therefore, the estimation of the jammer correlation matrix will be degraded resulting in a fall of performances of spatial processing. In this paper, it is first proposed to use a preprocessing before spatial filtering. The objective of this preprocessing is to reduce clutter power in the estimation samples used to compute adaptive spatial filters and thus improve the efficiency of jamming filtering. Then, a performance study of this preprocessing in terms of clutter power reduction is made, aiming at quantifying both the influence of antenna rotation and clutter decorrelation.

The purpose of this work is the estimation of Doppler echoes spectral moments. In case of strong overlapping, Fourier-like techniques provide poor results because of the lack of resolution. We propose the use of stochastic maximum-likelihood (SML) and subspace-based methods (WPSF algorithm) for a joint estimation of spectral moments. The statistical performances (theoretical and empirical by Monte Carlo simulations) of estimators are compared with the Cramer-Rao lower bound. The results of tests performed on very high frequency (VHF) times series obtained during Thunderstorm, Arecibo, PR during September and October 1998 validate the model and algorithms and confirm the interest of both approaches.

Abstract. A classical way to reduce a radar’s data is to compute the spectrum using FFT and then to identify the different peak contributions. But in case an overlapping between the different echoes (atmospheric echo, clutter, hydrometeor echo. . . ) exists, Fourier-like techniques provide poor frequency resolution and then sophisticated peak-identification may not be able to detect the different echoes. In order to improve the number of reduced data and their quality relative to Fourier spectrum analysis, three different methods are presented in this paper and applied to actual data. Their approach consists of predicting the main frequency-components, which avoids the development of very sophisticated peak-identification algorithms. The first method is based on cepstrum properties generally used to determine the shift between two close identical echoes. We will see in this paper that this method cannot provide a better estimate than Fourier-like techniques in an operational use. The second method consists of an autoregressive estimation of the spectrum. Since the tests were promising, this method was applied to reduce the radar data obtained during two thunder-storms. The autoregressive method, which is very simple to implement, improved the Doppler-frequency data reduction relative to the FFT spectrum analysis. The third method exploits a MUSIC algorithm, one of the numerous subspace-based methods, which is well adapted to estimate spectra composed of pure lines. A statistical study of performances of this method is presented, and points out the very good resolution of this estimator in comparison with Fourier-like techniques. Application to actual data confirms the good qualities of this estimator for reducing radar’s data. Key words. Meteorology and atmospheric dynamics (tropical meteorology)- Radio science (signal processing)- General (techniques applicable in three or more fields)

Blind source separation is now a well known problem. When a priori information about the propagation or the geometry of the array are not available, the model can be generalized to a blind source separation model. It supposes the statistical independence of the sources and their non-gaussianity. We focus on an algorithm called canonical correlation analysis, based on the use of second order statistics

Blind source separation is now a well known problem. When a priori information about the propagation or the geometry of the array is not available, the model can be generalized to a blind source separation model. It supposes the statistical independence of the sources and their non-gaussianity. In this paper, we focus on an algorithm, called canonical correlation analysis, based on the use of second order statistics

In paths localization, a resolution that goes beyond the classical Rayleigh beamwidth is of great interest. To improve the resolution, model based techniques have been introduced (high resolution methods), but they are very sensitive to noise correlation and they assume underlying data model. We develop a parameterized maximum likelihood (PML) technique, based on a knowledge of the transmitted signal. We develop the exact PML approach and present its implementation by a Gauss Newton procedure. Simulations on data sets are examined. The performances are compared to the Cramer Rao bound. Its superiority over the traditional matched filter (MF) and the conditional maximum likelihood (CML) is shown. The paper concludes with the improvements introduced by a knowledge of the transmitted signals

We present in this article a direction of arrival estimation algorithm for non circular sources. We show how to take into account non circularity of signals in array processing and develop extensions of classical algorithms. The main improvement linked to the non circularity concerns the resolution, the variance of estimation and the number of resolvable sources. These characteristics are illustrated by simulations and theoretical analysis.

Nous presentons dans cet article un algorithme de localisation angulaire de sources non circulaires. Nous montrons comment prendre en compte la nature non circulaire des signaux en traitement d'antenne et developpons une extension de l'algorithme MUSIC. Cette extension offre, pour une mise en oeuvre relativement simple, des performances sensiblement superieures a l'algorithme classique. Les principaux avantages lies a la non circularite concernent la resolution, la variance d'estimation et le nombre de sources localisables. Ces caracteristiques sont illustrees par des simulations.

Blind source separation is now a well known problem. Various methods have been proposed for instantaneous and convolutive mixtures of sources. Conventional antenna array processing techniques are based on the use of second order statistics but rest on restrictive assumptions. Thus, when a priori informations about the propagation or the geometry of the array are not available, the model can be generalized to a blind sources separation model. It supposes the statistical independence of the sources and their non-gaussianity. In this paper, we focus on the narrow band source separation problem embedded in wide band jammers. We show that the JADE algorithm made for instantaneous mixture is still valid in a wide band context where only the signals of interest are narrow-band. We also prove that a wide band signal tends to occupy all the degrees of freedom of the covariance matrix and modifies the signal subspace dimension.

In this paper, we address several methods which permit to suppress the interference signals received at the antenna level. The classical method to suppress interference uses second order statistics and consists in forming a spatial filter or in calculating the opposition coefficients to apply on different channels. An original solution consists in using higher order statistics for the blind source separation. These algorithms do not use any a priori knowledge on the array manifold. One of the benefits of such blind separation is that source separation is essentially unaffected by errors in the propagation model or in array calibration. Only the statistical independence and the non-Gaussian source signals are important. An other method: the canonical correlation analysis is also used and compared to the others