Mourad Barkat

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In this paper, we present two novel algorithms for automatic censoring of radar interfering targets in log-normal clutter. The proposed algorithms consist of two steps: removing the corrupted reference cells (censoring) and the actual detection. Both steps are performed dynamically by using a suitable set of ranked cells to estimate the unknown background(More)
In this paper, we investigate the use of two iterative algorithms for the suppression of interferences and thus, the detection of slow targets in monostatic airborne radar. The conventional space-time adaptive processing (STAP) such as the sample matrix inversion (SMI) or the Principal Components (PC) methods are computationally costly and require the(More)
The effect of the PRF on the performances of the reduced rank STAP is discussed. The numerical evaluation is based on three different models of changing the PRF, namely quadratic, pseudorandom and chaotic with two methods of reduction of the rank: Principle Components (PC) and Signal to Interference Noise Ratio (SINR metric). Rank reduction reduces the(More)
In this paper, we first present the principles of STAP and discuss the properties of optimum detector, as well as problems associated with estimating the adaptive weights such as ambiguities and the high computational cost. The performances are evaluated highlighting the influence of radar parameters on the detection of slow targets. To resolve problem of(More)
In this paper, we address the problem of automatic target detection in Weibull clutter and multiple target situations, without any prior knowledge of neither the non stationary clutter statistics in which the radar operates nor the number of outliers that may be present in the reference window. In doing this, we develop the Forward and Backward Order(More)