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Radar is a remote sensing system that uses techniques of statistical signal processing to obtain information from the received signal. The most common radars are active systems that radiate an electromagnetic wave into space and receive the echo signals reflected from a target and its environment. One of the difficulties to consider in radar systems is that the signal of interest is obscured by reflections from the environment, a phenomenon called clutter. In general, and depending on the application, clutter is considered an interference source whose effects should be eliminated or mitigated. Due to its random nature it is important to develop statistical signal processing methods for target detection and for estimation of its properties in intense and dynamic clutter situations. Realistic modeling of the received signals is essential to obtain efficient algorithms. These models should emphasize the differences between the target and the clutter. Thus, signal processing methods are employed to detect target echoes in presence of clutter. In this thesis we address the detection problem in the presence of dynamic clutter for radar applications. Specifically, we develop models which consider the environment changes and use the process history to improve their characterization at current time and for future predictions. First, we propose modeling the clutter as a time series, using Generalized Autoregressive Conditional Heteroscedastic (GARCH) processes. One of their main characteristic of these models is their heavy tailed probability density function, but they have the disadvantage that an explicit expression for their probability density function does not exist. For this reason, we analyze alternatives to estimate their parameters and to determine the estimates quality. Moreover, we adapt a conventional hypothesis test to derive a detection scheme based on the GARCH model. In addition, we extend the previous ideas by proposing a second clutter model that combines a GARCH-2D process with an AR process to use the information of multiple radar pulses at each decision instant. The GARCH-2D part of the model preserves the impulsivity property of the GARCH processes, i.e., it preserves the heavy tailed pdf, and the AR model in the innovations lets us model pulsewise correlation. In both cases, we carry out a theoretical analysis to determine its false alarm probability and we compute by means of numerical simulations the detection probability to evaluate the detectors performance. We also analyze how the error in the estimation of their parameters affect their performance. Although the resulting detectors are not of constant false alarm rate, we show that they are very robust with respect to the pro-

Procesamiento de señales de radar en presencia de clutter dinámico